What Are the Preferences of Chinese Farmers for Drones (UAVs): Machine Learning in Technology Adoption Behavior

Highlights

What are the main findings:

• ACO-DT model outperforms traditional ML in identifying potential drone users.
• Promotion time and UAV agriculture understanding are core for farmers’ adoption.

What are the implications of the main findings:

• Guidance for formulating phased agricultural drone promotion mechanisms is meaningful during optimal promotion cycle.
• Building hierarchical UAV cognitive education system for farmers can improve technology adoption level.

Abstract

With the continuous advancement of sustainable agriculture, drone technology has become a focus of attention. Current research primarily relies on classical models for questionnaire surveys and analyses within specific regions, rather than implementing macro-level investigations that incorporate innovative algorithms. This study designed a survey questionnaire to investigate Chinese farmers’ preferences for agricultural drones and their technology adoption mechanisms under sustainable agriculture context. The Ant Colony Optimization-Decision Tree (ACO-DT) model and SHAP (Shapley Additive exPlanations) value analysis are applied to analyze the contribution of different indicators to technology adoption. The ACO-DT model outperformed traditional machine learning models with approximate accuracy 0.85, recall 0.98, and F1 Score 0.90, effectively identifying potential drone users compared to other traditional machine learning models. The SHAP analysis showed “Time Required for Promotion” (average SHAP value exceeds 1.25) and “Understanding of UAV Agriculture” (average SHAP value is about 1.0) were core influencing factors. Specifically, high-cognition farmers preferred shorter promotion cycles, while low-cognition group favored longer cycles to reduce decision-making uncertainty. Practically, the study enriches agricultural technology adoption research methodologically and offers references for advancing smart agriculture and optimizing rural production factors.

1. Introduction

With the continuous growth of the global population [1,2], the demand for food is also on a rising trajectory, placing traditional agricultural production models under unprecedented pressure [3,4]. Relevant data indicate that by 2080, the global population will reach 10.3 billion [5], at which point food demand will need to increase by more than 70% compared to the current level to meet the supply [6]. Such a substantial increment in food production undoubtedly poses a huge challenge to traditional agriculture [7], which has long relied on experiential farming [8]. More critically, the resources on which agricultural production depends have fallen into an irreversible state of strain [9]. On one hand, the continuous advancement of urbanization [10], coupled with the objective needs of ecological environmental protection [11], has led to a constant reduction in the area of cultivable land [12], with exploitable reserve arable land resources almost exhausted [13]. On the other hand, freshwater resources are significantly unevenly distributed geographically, and shortages occur frequently, greatly restricting the effectiveness of irrigation [14]. In addition, extreme weather events caused by climate change, such as heavy rains and droughts, are increasing in frequency and intensity, leading to a significant rise in natural risks faced by agricultural production [15]. The superposition of these three issues makes improving agricultural production efficiency no longer an option but a necessary measure related to global food security [16,17,18,19,20].

Meanwhile, problems in the agricultural labor sector are becoming increasingly prominent, profoundly changing the configuration pattern of agricultural production factors [21]. In developing countries, the acceleration of urbanization has led to an irreversible trend of rural population migrating to non-agricultural sectors in cities [22]. This directly results in a sharp reduction in the number of agricultural workers, and there is a mismatch between the skills of the workers and the needs of agricultural production [23]. The shortage of agricultural labor has caused the labor costs in agricultural production to rise continuously [24], making the economic viability of traditional agricultural production methods that have long relied on a large amount of manual input gradually decline and become unsustainable [25]. Against this backdrop, drones, as an efficient and precise agricultural technical tool, are becoming an important force in promoting agricultural modernization and the development of the low-altitude economy [26].

Agricultural drones, as key technical tools in smart agriculture, are currently in a crucial stage of rapid technological updates and expanding application scope [27]. Initially, the application of drones in agriculture can be traced back to the field of plant protection (i.e. pesticide spraying via drones) [28]. Compared with traditional manual spraying or ground mechanical spraying, drones have significant advantages [29]. Firstly, drones have high operational efficiency [30], a single drone can complete the operation of hundreds or even thousands of mu of farmland per day, with an efficiency dozens of times greater than manual work [31]. Secondly, drones can achieve precise pesticide application [32,33,34]; through high-precision navigation systems and sensor technologies [35,36], drones can adjust the spraying amount according to the actual conditions of the farmland, reducing the waste of pesticides and chemical fertilizers and lowering environmental pollution [37,38]. In addition, drones can operate in complex terrains and harsh climate conditions, making up for the shortcomings of traditional agricultural machinery [39]. In the selection of agricultural drone types, quadcopters are more suitable for complex terrains due to their high stability and flexibility, making them particularly well suited for precise tasks such as crop protection and land monitoring. Contrarily, twin-rotor drones have greater payload capacity, making them more suitable for large-scale tasks such as fertilization and sowing. Therefore, the choice of drone type should be based on specific agricultural tasks to achieve optimal operational results. With the continuous advancement of technology [40], the application scenarios of drones have expanded from a single plant protection to multiple links such as sowing [41,42], fertilization, pollination, and farmland monitoring [43,44], becoming an important part of smart agriculture.

Farmers’ cognition and acceptance of agricultural drones mostly show an evolutionary process from cognitive blindness and application doubts to value recognition [45]. In the initial stage, due to the lack of systematic cognition of this technology and the constraints of purchase and operation costs, farmers generally held a cautious attitude towards agricultural drones [46]. The path dependence generated by long-term traditional farming models further hindered the promotion and application of this new production tool. However, with the gradual implementation of government support policies and the increasing improvement of the market service system, the application cost of agricultural drones has continued to decrease [47]. Its significant benefits in improving operational efficiency, saving production resources, and reducing agricultural risks have gradually become apparent, promoting farmers’ attitudes from tentative observation to active acceptance [48]. This attitude change is not only reflected in the increase in equipment purchases and service purchase frequency but also in the innovation of agricultural production cognition. Farmers have begun to face up to the core value of drones in agricultural production and incorporate them into the agricultural production decision-making system [49]. Professional drone operator groups and supporting service markets have emerged in some regions, which indicates that agricultural drones have gradually evolved from a technological innovation to a new agricultural production paradigm and have achieved in-depth penetration in the field of agricultural production [50].

Overall, the gradual popularity of agricultural drones is not only a change in farmers’ behavioral choices, improving the happiness index of farmers engaged in agricultural production [51,52,53], but it also reflects the innovation of agricultural production cognition and decision-making models. The formation of a professional service system further confirms its in-depth evolution towards a new agricultural production paradigm.

The rest of the paper is organized into several key sections. Section 2 provides a literature review on the constraining factors for the development of the agricultural drone market, the technological gap at the application level, and the research methods for promoting application evaluation. Section 3 introduces the basic statistical characteristics of the data obtained from the questionnaire survey and the profile of the survey subjects, then provides a detailed introduction to the methods, including reliability analysis, specific implementation steps of the ACO-DT model, and formulas for SHAP analysis. Section 4 presents the results of these analyses, with a focus on the comparison between the ACO-DT model and different classical machine learning methods, as well as the entire process of SHAP value analysis. It also summarized the shortcomings and future directions of this study. Section 5 discusses the shortcomings of the current policy system and provides targeted suggestions. It also elaborates on the shortcomings of this study and future research directions. Finally, Section 6 summarizes this study by summarizing key insights based on the research findings.

2. Literature Review

Against the backdrop of resource constraints, labor shortages, and rising demand for food in global agriculture, the application and development of agricultural drones, as the core technology carrier of smart agriculture, have become the focus of academic attention. The existing mechanisms mentioned in the literature review are shown in Figure 1.

At present, the development of the agricultural drone market is still constrained by various factors [54,55]: agricultural scenarios have high requirements for operational accuracy, such as ensuring stability in complex terrain conditions [56,57], reasonable application of sensors in various crop growth environments [58,59], and real-time interpretation of massive farmland monitoring data [60]. These all put forward high requirements for the technical level and knowledge reserve of operators, thus forming a clear application threshold. Although some enterprises simplify their operation processes by developing intelligent systems, small and medium-sized farmers often find it difficult to independently handle technical failures or make misjudgments on data without professional technical support, which leads to their high dependence on external service providers in actual use [61]. At the same time, differences in policy support have had a significant impact on the market penetration pattern [62]. For example, some countries have formed a closed loop for technology promotion and promoted the application of agricultural drones by implementing subsidy policies and promoting industrial chain collaboration. However, other regions are facing the challenge of market fragmentation due to regulatory restrictions or weak infrastructure [63,64]. In addition, economic benefits have further exacerbated market differentiation. Since farmers always revolve around the ratio of cost to benefit when making decisions, the high initial investment and various hidden costs make it difficult to demonstrate the economic viability of agricultural drones in small-scale planting [65].

On a global scale, the application of agricultural drones presents a clear “technological gap” feature [66]. Taking China as an example, although the number of agricultural drones continues to rise and the coverage of arable land continues to expand, the adoption rate of small and medium-sized farmers is still at a relatively low level [67]. In India and some parts of Africa, the willingness for technology penetration is even lower than 14% [68]. This application difference reflects the complexity behind technology adoption behavior [69]—from the effectiveness of policy design to the subjective cognition of farmers, from the balance of economic costs to the dissemination effect of social networks, multiple factors are intertwined [70,71], jointly determining whether technology can truly land and play a role in agricultural production. In contrast, the US market is dominated by large farms, with over 80% of drone users being agricultural enterprises with an annual output value exceeding one million US dollars [72]. Their application of technology focuses more on data-driven precision management, such as using drone-collected field data to optimize fertilization plans [73]. This regional difference indicates that technology promotion strategies must be deeply adapted to local resource endowments and user group characteristics, and a single promotion model is difficult to meet diversified needs.

Regarding the methodology for evaluating the promotion and application of drones, researchers in different regions mostly use questionnaire surveys to collect data and analyze them. Among them, the study on Iran used structured questionnaires for data collection, and explored the factors influencing Iranian farmers’ adoption of drones through the use of technology acceptance and unified theory of use (UTAUT) models [74]. Researchers in China conducted a survey of 321 farmers and constructed a general least squares regression model based on the data to gain a preliminary understanding of the relationship between psychological factors and innovative adoption of smart agriculture (SA) technology in the context of small-scale agricultural economy [75]. The cross theoretical model (with ordinal logarithmic regression model) was used to analyze the penetration rate of drones in Hungary [76]. This method has also been used by German researchers, who have concluded that the expansion of farm scale has a positive impact on the adoption process, while the age of farmers has a negative impact on the adoption process of drones [77]. Based on the above analysis, a series of suggestions have been proposed to accelerate the development of information service systems, increase financial support, and provide necessary software and hardware support to promote the further promotion of agricultural drones and the modernization transformation of agriculture [78,79].

However, in the existing literature, although the applications of drones in different agricultural scenarios and the prospects of the low-altitude economy have been fully analyzed and elaborated, there are generally the following shortcomings:

①

Regarding agricultural drones, most current studies focus on the innovation and research and development of related engineering technologies, while relatively few studies focus on the acceptance and recognition of drones among farmers at the downstream of the industry (i.e., the application end) [80]. Additionally, there are few studies that use existing theories to explain (expected) adoption [81]. Issues such as which factors affect the promotion of agricultural drones and how to identify potential agricultural drone users lack in-depth discussion.

②

The research scope of existing studies is relatively narrow, usually limited to a specific province or crop-producing area and rarely reveals the application characteristics and market demand trends of agricultural drones from a national perspective.

③

In current studies, only questionnaire survey methods are mainly used to analyze the factors that farmers care about, but there is a lack of quantitative explanation and prediction of farmers’ preferences and potential purchase intentions.

To address the above limitations, this study has made the following improvements:

①

We designed our own survey questionnaire to obtain first-hand survey data from all over the country, covering a wide range of technology adoption preferences and survey subjects from different backgrounds.

②

This study adopts a relatively novel machine learning method “Ant Colony Optimization-Decision Tree” (ACO-DT) and elaborates on it by combining SHAP value analysis. Particularly, this article compares the performance of the ACO-DT model with existing classical machine learning algorithms to ensure model fit, and based on the results of SHAP value analysis, provides targeted suggestions for the shortcomings of the current policy system.

3. Materials and Methods

3.1. Data

This study used a questionnaire as a research tool to collect responses from respondents; therefore, the data used in this study came from a survey of 526 agriculture and related practitioners conducted in the summer of 2025. The survey was conducted using the Wenjuanxing platform. The questionnaire consists of two parts of questions. Part 1 contains 7 questions related to demographic information. Meanwhile, Part 2 contains 28 questions regarding their attitudes, experiences, and opinions on using drones for agricultural production (the details are in the Supplementary Materials). For the two-choice scale in the questionnaire, when crawling data, “0” represents “no” and “1” represents “yes”. The adapted multi-point Likert scale analysis was used to divide respondents’ responses [82,83]. Among the 5 dummy variables crawled, “5” represents strong disagreement, “4” represents disagreement, “3” represents uncertainty, “2” represents agreement, and “1” represents strong agreement (i.e., the larger the number, the more negative the attitude, and the smaller the number, the more positive the attitude). In addition, for the multi-choice closed-ended questions in the questionnaire, the number obtained by crawling represents the code of the selected option.

In the process of conducting research, this study reduced the threshold for digital skills and cognition through multiple designs. For example, the questionnaire uses concise question types such as multiple choice, Likert scale, and multiple-choice questions to avoid complex expressions and professional terminology. The questions have clear logic and are in line with the actual production of farmers. In addition, before conducting the survey, we provided detailed explanations to the respondents on the types of questions, understanding methods, answering methods, and examples.

In this survey, the respondents showed distinctive structural characteristics (as shown in Figure 2): males dominated in gender, accounting for 79.85% (420 out of 526 valid samples); the age structure highlights the dominant position of middle-aged and young people, with the 25–44 age group (corresponding to 25–34 and 35–44 years old) accounting for 51.33% in total, while those aged 55 and above account for 22.24%, reflecting a decreasing trend in attention to agricultural technology with age. The occupational composition focuses on practitioners related to the agricultural industry chain, with agricultural enterprise employees (91.63%) as the main group, and students and other professions accounting for a relatively low proportion (3.04%); the educational level is characterized by a moderate level, with those holding college and high school/vocational education accounting for 65.40% in total, and those with a bachelor’s degree or above accounting for 16.73%; the annual household income is concentrated in the range of 50,000 to 200,000 yuan (71.48%), and overall it is at an above-average level; the regional distribution is mainly in rural areas and fourth-tier cities and below (39.35%), reflecting a high coverage of grassroots agricultural production scenes. Additionally, 91.63% of the respondents are engaged in agricultural production activities, further strengthening the direct relevance of the sample to agricultural production practices.

3.2. Methodology

3.2.1. Reliability Analysis

Reliability analysis is mainly used to examine the stability and consistency of the results measured by the scales in the questionnaire, that is, to test whether the scale samples in the questionnaire are true, reliable, and trustworthy [84]. Cronbach’s alpha, also known as alpha reliability, is a method of testing reliability proposed by Lee Cronbach in 1951 [85]. It overcomes the shortcomings of the partial halving method and is currently the most used reliability analysis method in social science research [86]. Compared with McDonald’s ω or the split-half reliability method, Cronbach’s Alpha is sufficiently effective in capturing the internal consistency among unidimensional items, while avoiding result fluctuations caused by artificial grouping and the potential issue of disrupting the logical connections between items.

Cronbach’s Alpha coefficient is the most used reliability indicator, with values ranging from 0 to 1. The closer it is to 1, the higher the reliability of the questionnaire. The formula is:

$$\mathit{\alpha }={\displaystyle \frac{\mathit{k}}{\mathit{k}-1}} \ast 1-{\displaystyle \frac{\sum {\mathit{S}}_{\mathit{i}}^2}{{\mathit{S}}_{\mathit{t}}^2}}$$

(1)

where $\alpha$ is the Cronbach’s Alpha coefficient, k is the number of items in the scale (unitless), $S_i^2$ is the sample variance of the i-th item (unit “point²”), $S_t^2$ is the variance of the sum of scores of all items (unit “point²”).

From the formula, the alpha coefficient evaluates the consistency between the scores of each item in the scale and belongs to the intrinsic consistency coefficient. This method is suitable for reliability analysis of attitude and opinion questionnaires (scales).

Based on the number of items k in the scale and the average correlation coefficient r between all items (both are without units), the standardized Cronbach’s Alpha coefficient is:

$$\mathit{\alpha }={\displaystyle \frac{\mathit{k}\mathit{r}}{[\left(\mathit{k}-1\right)\mathit{r}+1]}}$$

(2)

In psychometrics, there are often multiple different questions or even multiple different scales for the same concept. The consistency of results measured by different questions or scales, expressed as the expected value of the correlation coefficient, is their “alpha reliability” [87,88].

3.2.2. Ant Colony Optimization-Decision Tree Classifier (ACO-DT)

ACO-DT as shown in Figure 3 is a method of using ant colony optimization algorithm to automatically construct or optimize decision tree models. Traditional decision trees use greedy algorithms to locally select features and splitting points based on indicators such as information gain and Gini impurity at each node [89,90]. This method is fast, but it is easy to construct suboptimal tree structures, which affects the final generalization ability and accuracy [91]. In this model, ACO is used to optimize the key hyperparameters of the decision tree and globally search for the optimal splitting rule of the decision tree, which can more effectively explore complex solution spaces, avoid falling into local optima, and directly improve the performance of the final decision tree model [92,93]. The specific steps are as follows:

• Step 1: Parameter Initialization

The purpose of this step is to set the basic parameters for the algorithm to run, laying the foundation for subsequent iterative searches. The specific parameters are shown as follows:

①

Number of ants m: The number of artificial ants participating in the search determines the scale of parallel search. Here, we choose m = 10 to match the research data dimension, cover key feature combinations, and avoid computational redundancy.

②

Number of iterations N: The maximum number of iterations before the algorithm terminates balancing search efficiency and accuracy. Here, we choose N = 15 to ensure the ACO fully searches for the optimal feature subset while avoiding increased computational costs due to excessive iterations.

③

Pheromone (α,β): controls the importance of pheromones, while controlling the weight of heuristic information (such as the gain of feature splitting). Here, we choose α = 1 and β = 2 to balance the dependence on historical pheromone paths and the stability of search direction, while strengthening the guidance of heuristic information on feature selection, to avoid search disorder, and balance the target-oriented nature and diversity of search.

④

Initial pheromone level τ₀: The initial pheromone values of all feature splitting point paths, usually set as a constant. Here, we choose ${\tau }_0=1$ to ensure that the probability of each feature being selected by ants in the initial stage is equal.

⑤

Maximum tree depth: Limit the complexity of the decision tree to avoid overfitting. Here, we choose maximum tree depth as 4 to limit model complexity and avoid overfitting.

⑥

Number of features to consider at each split: Randomly select the number of candidates features during each round of splitting to reduce computational costs.

⑦

Pheromone evaporation rate (ρ): Control the proportion of pheromone volatilization within the range of $0<\rho <1$. Here, we choose ρ = 0.1 to retain search flexibility through moderate pheromone evaporation while maintaining the guiding value of effective paths.

• Step 2: Pheromone Matrix Initialization

The purpose of this step is to assign initial pheromones to all possible combinations of features and splitting points and construct a “pheromone map” of the search space.

We assume that the dataset has F features, each with $S_f$ possible splitting points. The dimension of the pheromone matrix $\tau$ is $\sum _{f=1}^F{S}_f$, and each element ${\tau }_{ij}$represents the pheromone level of the “feature i-splitting point j” path. Here, we initialize ${\tau }_{ij}$ = ${\tau }_0$ to ensure that all paths are fairly explored by ants in the initial stage.

• Step 3: Iterative Search Process

Through multiple iterations, the ant colony gradually finds the optimal decision tree structure, with the core being dynamic updating of pheromones and heuristic search.

①

Ants construct decision trees

Each ant independently constructs a decision tree as follows:

Based on the pheromone level ${\tau }_{ij}$and heuristic information ${\eta }_{ij}$, ants select features and splitting points at each node, and the probability formula is:

$${\mathit{P}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\left({\mathit{\tau }}_{\mathit{i}\mathit{j}}\right)}^{\mathit{\alpha }}·{\left({\mathit{\eta }}_{\mathit{i}\mathit{j}}\right)}^{\mathit{\beta }}}{{\sum }_{\mathit{k}\in \mathit{p}\mathit{o}\mathit{s}\mathit{s}\mathit{i}\mathit{b}\mathit{l}\mathit{e}\mathit{ }\mathit{p}\mathit{a}\mathit{t}\mathit{h}}{\left({\mathit{\tau }}_{\mathit{i}\mathit{k}}\right)}^{\mathit{\alpha }}·{\left({\mathit{\eta }}_{\mathit{i}\mathit{k}}\right)}^{\mathit{\beta }}}}$$

(3)

where $P_{ij}$is the probability of selecting the “feature i-splitting point j”, the numerator is the weighted product of the current path’s pheromone and heuristic information, and the denominator is the sum of all candidate paths.

Recursively select the splitting point based on the above probability until the maximum tree depth or node sample purity meets the threshold.

②

Fitness Evaluation

After each ant constructs a tree, its performance is evaluated through a fitness function, denoted as ${fitness}_k$. The better the performance of the tree, the higher the pheromone increment of the corresponding path. Here, since the distribution of positive and negative samples is relatively balanced after data preprocessing, and Accuracy is an intuitive and easy-to-calculate classification index, we choose Accuracy as the fitness function for the iterative search process. The specific formula is as follows:

$$\mathit{A}\mathit{c}\mathit{c}\mathit{u}\mathit{r}\mathit{a}\mathit{c}\mathit{y}={\displaystyle \frac{\mathit{T}\mathit{P}+\mathit{T}\mathit{N}}{\mathit{T}\mathit{P}+\mathit{T}\mathit{N}+\mathit{F}\mathit{P}+\mathit{F}\mathit{N}}}$$

(4)

where TP (True Positive) refers to the number of samples that are actually of the positive class and correctly predicted as the positive class by the model; TN (True Negative) refers to the number of samples that are actually of the negative class and correctly predicted as the negative class by the model; FP (False Positive) refers to the number of samples that are actually of the negative class but incorrectly predicted as the positive class by the model; and FN (False Negative) refers to the number of samples that are actually of the positive class but incorrectly predicted as the negative class by the model. These four indicators together constitute the core elements of the Confusion Matrix commonly used in classification tasks, and their combined calculation can intuitively reflect the accuracy of the model in classifying positive and negative samples.

③

Pheromone Update

After all ants complete the tree construction, update the pheromone based on the global optimal tree, using the formula:

$${\mathit{\tau }}_{\mathit{i}\mathit{j}}=\left(1-\mathit{\rho }\right)·{\mathit{\tau }}_{\mathit{i}\mathit{j}}+\sum _{\mathit{k}=1}^{\mathit{m}}∆{\mathit{\tau }}_{\mathit{i}\mathit{j}\mathit{k}}$$

(5)

where m is the number of ants, ${\tau }_{ij}$ represents the updated pheromone level of the “feature i-splitting point j” path, $\left(1-\rho \right)·{\tau }_{ij}$ is the remaining amount after evaporation of pheromones, $∆{\tau }_{ijk}$ is the increment of pheromones deposited by the kth ant on the “feature i-splitting point j” path, calculated using the formula:

$$∆{\mathit{\tau }}_{\mathit{i}\mathit{j}\mathit{k}}=\left\{\begin{array}{c}{\displaystyle \frac{\mathit{Q}}{{\mathit{f}\mathit{i}\mathit{t}\mathit{n}\mathit{e}\mathit{s}\mathit{s}}_{\mathit{k}}}},ifthek-thantusesthispath\\ 0,otherwise\end{array}\right.$$

(6)

where Q is a constant (total amount of pheromones), ${fitness}_k$ is the fitness (such as accuracy) of the decision tree constructed by the kth ant. The higher the fitness, the greater the increment of pheromones, guiding subsequent ants to be more inclined to choose high-quality paths.

④

Track Global Best Tree

After each iteration, compare the fitness of all decision trees constructed by ants and retain the tree with the best performance as the current global optimal tree. If the current iteration’s optimal tree performs better than the historical best, update the hyperparameters (such as depth, split points) and fitness values of the global optimal tree.

• Step 4: Termination Criterion

When the number of iterations reaches the preset maximum value, or when the performance of the global optimal tree does not improve continuously for multiple rounds, the algorithm terminates. Here, to avoid the problem of insufficient search caused by setting too few termination rounds and computational redundancy caused by setting too many termination rounds, this study sets 15 rounds as the threshold for consecutive rounds without improvement of the model, so as to achieve a reasonable balance between sufficient search and efficient computation.

In existing studies, this method has been applied to evaluate the influencing factors of academic performance and pharmaceutical cost. It stands out among machine learning methods such as logistic regression, random forest, support vector machine, XGBoost, support vector machine with a linear kernel, naive Bayes, K-Nearest Neighbor, and decision tree, with high accuracy. Therefore, this study also adopts this method to explore the applicability and accuracy of the combination of optimization algorithms and decision tree algorithms in other fields [94,95].

In addition, the decision to choose ACO-DT over models such as PSO, Cuckoo Search, BAT, and GA-DT stems from its high compatibility between the algorithmic mechanism and the “feature selection-decision tree classification” task of identifying influencing factors in agricultural drone technology adoption behavior [96]. From the perspective of algorithm-feature selection alignment, ACO’s pheromone-based positive feedback mechanism dynamically reinforces high-quality feature combinations and weakens inferior ones through the accumulation and evaporation of pheromones along feature combination paths. This aligns well with the decision tree’s need for a stable, low-redundancy feature subset. In contrast, PSO is better suited for continuous space optimization, GA-DT’s crossover operations tend to disrupt effective feature combinations, Cuckoo Search is parameter sensitive, and BAT exhibits weak global search capability. Regarding the balance between model complexity and task requirements, ACO inherently fits discrete space feature combination optimization without requiring complex encoding transformations, thereby preserving intrinsic feature relationships. Other models may necessitate binary encoding (increasing complexity) or tend to introduce redundant features leading to overfitting. Hence, ACO-DT proves a better performance.

3.2.3. Other Typical Machine Learning (ML) Models for Classification

In a decision tree, each internal node represents a splitting problem: it specifies a test on a certain attribute of the instance, divides the samples arriving at that node according to a specific attribute, and each subsequent branch of the node corresponds to a possible value of that attribute. For the samples contained in the leaf nodes of a classification decision tree, the mode of their output variables is the classification result [97].

• ExtraTrees

Extra-trees (Extremely Randomized Trees) are very similar to random forests. The “extremely random” here is reflected in the node splitting of decision trees: they simply use random features and random thresholds for splitting. In this way, each decision tree will have a more distinct and random shape [98].

• XGBoost

XGBoost is an efficient implementation of GBDT. It adds a regularization term to the loss function, and since some loss functions are difficult to compute derivatives for, XGBoost uses the second-order Taylor expansion of the loss function to fit the loss function [99].

• LightGBM

LightGBM is an efficient implementation of XGBoost. Its core idea is to discretize continuous floating-point features into k discrete values and construct a histogram with a width of k. Then, it traverses the training data to calculate the cumulative statistics of each discrete value in the histogram. When selecting features, it only needs to traverse the discrete values in the histogram to find the optimal split point. Moreover, it adopts a leaf-wise growth strategy with depth restrictions, which saves a lot of time and space overhead [100].

• Support Vector Machine (SVM)

Support Vector Machine (SVM) is a type of generalized linear classifier that performs binary classification on data in a supervised learning manner, whose decision boundary is the maximum margin hyperplane solved from the learning samples [101].

• The K-Nearest Neighbors (KNN)

The K-Nearest Neighbors (KNN) classifier is one of the commonly used classifiers in supervised learning, which determines the class of an observation as the class with the largest proportion among the k nearest observations to it [102].

• BP neural network (BP)

A BP neural network is a multi-layer feedforward network trained by the error backpropagation algorithm, and it is one of the most widely used neural network models currently. The learning rule of a BP neural network is to use the steepest descent method and continuously adjust the weights and thresholds of the network through backpropagation, so as to minimize the classification error rate of the network [103].

3.2.4. SHAP (Shapley Additive exPlanations) Value Analysis

SHAP value analysis is a model interpretation method based on game theory. Its core is to quantify the impact and direction of individual features in the prediction process by calculating the contribution of each feature to the model’s prediction results [104,105]. The specific formula is as follows:

For a predictive model f(x), input a sample $x=(x_1,x_2,\dots ,x_d)$, (d is the number of features), and the model’s predicted value for this sample is f(x). The goal of SHAP value is to assign a value ${\varphi }_i$ to each feature i, such that:

$$\mathit{f}(\mathit{x})=\mathit{E}[\mathit{f}\left(\mathit{x}\right)]+\sum _{\mathit{i}=1}^{\mathit{d}}{\mathit{\varphi }}_{\mathit{i}}$$

(7)

where $E\left[f\left(x\right)\right]$ is the baseline value of the model (usually the average predicted value of all samples, i.e., marginal expectation) ${\varphi }_i$ is the SHAP value of feature i, representing its contribution to the deviation of the predicted value from the baseline (positive values indicate that the feature drives the predicted value up, negative values indicate that it drives the predicted value down).

For feature subset S (excluding feature i), the SHAP value of feature i is defined as:

$${\mathit{\varphi }}_{\mathit{i}}={\sum }_{\mathit{S}\subseteq \mathcal{F}\backslash \mathcal{ }\left\{\mathit{i}\right\}}{\displaystyle \frac{\left|\mathit{s}\right|!·\left(\mathit{d}-\left|\mathit{S}\right|-1\right)!}{\mathit{d}!}}·[\mathit{f}\left(\mathit{S}\cup \left\{\mathit{i}\right\}\right)-\mathit{f}(\mathit{S})]$$

(8)

where $\mathcal{F}=\{\mathrm{1,2},\dots ,d\}$ is the set of all features $S\subseteq \mathcal{F}\backslash \left\{i\right\}$ represents any subset of features that does not include feature i; |S| is the number of features in subset S; d is the total number of features; f(S) is the predicted value of sample x when the model only uses features from subset S (when S is an empty set), $f\left(\varnothing \right)=E\left[f\left(x\right)\right]$; ${\displaystyle \frac{\left|s\right|!·\left(d-\left|S\right|-1\right)!}{d!}}$ is the weight of subset S, ensuring that each feature subset is fairly considered.

By calculating the average absolute SHAP value of all samples, the impact of features on the overall model can be measured:

$$\mathit{T}\mathit{h}\mathit{e}\mathit{ }\mathit{g}\mathit{l}\mathit{o}\mathit{b}\mathit{a}\mathit{l}\mathit{ }\mathit{i}\mathit{m}\mathit{p}\mathit{o}\mathit{r}\mathit{t}\mathit{a}\mathit{n}\mathit{c}\mathit{e}\mathit{ }\mathit{o}\mathit{f}\mathit{ }\mathit{f}\mathit{e}\mathit{a}\mathit{t}\mathit{u}\mathit{r}\mathit{e}\mathit{ }\mathit{i}={\displaystyle \frac{1}{\mathit{n}}}{\sum }_{\mathit{k}=1}^{\mathit{n}}\left|{\mathit{\varphi }}_{\mathit{i},\mathit{k}}\right|$$

(9)

where ${\varphi }_{i,k}$ is the SHAP value of feature i in the kth sample. The larger the value, the more significant the overall impact of this feature on the model prediction.

4. Results

4.1. Reliability Test

The nature of multiple-choice questions is special (belonging to categorical variables, and answers can be selected multiple times), and their reliability and validity analysis methods are different from scale questions, requiring separate processing. Moreover, since multiple-choice questions are usually independent options rather than repeated items measuring the same variable, it is not possible to use reliability indicators such as alpha coefficient to analyze consistency. Reliability analysis is meaningless for multiple-choice questions and therefore it is not applicable in this case. Therefore, we did not include multiple-choice questions in the reliability analysis of the scale but only ensured that they had been converted into “dummy variables” for subsequent analysis. The reliability analysis results of the scale questions in the questionnaire are shown in Figure 4:

From the Cronbach’s alpha coefficient and standardized Cronbach’s alpha coefficient data presented in Figure 4, the reliability of this questionnaire is good. The Cronbach’s alpha coefficient reached 0.808, and the standardized coefficient was 0.79, both within the acceptable range of 0.7 or above. In the academic evaluation criteria for reliability analysis, a coefficient value greater than 0.7 usually indicates good internal consistency among questionnaire items, which can stably and reliably measure research objectives. This indicates that during the measurement process, the responses of each item in the questionnaire to latent variables are relatively consistent, and the interference of random errors on the measurement results is small. It can be considered that its reliability meets the basic requirements for data reliability in academic research and is sufficient to provide stable support for subsequent analysis based on questionnaire data [106].

4.2. Classification Performance of ACO-DT Model

To evaluate the effectiveness of the Ant Colony Optimization Decision Tree (ACO-DT) model in analyzing Chinese farmers’ drone usage preferences, this section will conduct in-depth analysis based on the confusion matrix heatmap, ROC-AUC curve, and relevant indicators including accuracy and recall, to reveal the classification performance and potential optimization space of the model.

The confusion matrix heatmap (as shown in Figure 5a) clearly presents the distribution of prediction results of the ACO-DT model in binary classification tasks. From the matrix data, it can be seen that in the model’s prediction of the group with positive opinions on the adoption of drone technology, 367 samples with actual positive opinions were correctly identified, while 74 samples with actual negative opinions were misjudged as positive opinions. In the prediction of the group with negative opinions on the adoption of drone technology, only 6 samples with actual positive opinions were misjudged as negative opinions, and 79 samples with actual negative opinions were accurately identified. This result indicates that the model has higher accuracy in identifying negative opinion groups and lower misjudgment rates. Although the prediction of positive opinion groups can cover most of the real samples, there is a certain degree of “false positive” misjudgment, reflecting that the model still has slight deviations in capturing the characteristics of positive opinion groups when distinguishing between the two types of samples. This may be related to the incomplete extraction of fuzzy factors that affect farmers’ usage preferences in some samples, such as the gray area between farmers’ concerns about the complexity of drone operations and actual technological acceptance.

The ROC-AUC curve (as shown in Figure 5b), as a core indicator for evaluating the discriminative ability of binary classification models, showed an AUC value of 0.7501 for the ACO-DT model. In academic research, AUC values in the range of 0.7–0.8 are generally considered to have moderate discriminative ability of the model, which can effectively separate two types of samples with positive and negative opinions to a certain extent. This result is consistent with the overall classification trend presented by the confusion matrix: the model has basic predictive effectiveness but has not yet reached the ideal state (AUC value close to one). Based on the actual background, farmers’ preference for using drones is influenced by the interaction of multidimensional factors such as demographic characteristics (such as age and education level), technological cognition (such as perception of operational efficiency), and policy support. The improvement space of the AUC value of the model may come from further mining the nonlinear relationship of high-dimensional features, such as optimizing the pheromone update mechanism of ant colony algorithm or adjusting the splitting rules of decision tree to enhance the ability to capture complex impact paths, such as the correlation between the differences in technology acceptance among farmers of different age groups and their perception of policy subsidies.

From the results of the model evaluation indicators shown in

Table 1. Results of evaluation indicators for the model.

Indicator	Result
Accuracy	0.8479
Precision	0.8322
Recall	0.9839
F1 Score	0.9017
Runtime	9.365 s

, the ACO-DT model exhibits considerable classification performance. The accuracy reached 0.8479, indicating that the proportion of correctly classified samples in the overall sample is at a reasonable level, reflecting its basic discriminative ability towards data. The Precision is 0.8322, which means that the proportion of samples predicted by the model as having “positive opinions on the adoption of drone technology” actually belongs to this category and has some reliability, but there are still some cases of misjudgment. The recall rate is as high as 0.9839, highlighting the model’s strong ability to capture positive samples of “positive opinions on the adoption of drone technology” and rarely missing real positive samples. The F1 Score is 0.9017, which is the harmonic mean of precision and recall. Considering the performance of both, it indicates that the model has achieved good results in balancing the two types of errors (misjudging positive and negative). Overall, the ACO-DT model can effectively identify different attitude samples in the classification task of farmer drone usage preferences. Although there is room for improvement in accuracy, with high recall and reasonable F1 score, it can provide strong technical support for user preference analysis in agricultural drone promotion. In the future, feature engineering and model optimization strategies can be combined to further improve its classification efficiency.

In summary, the ACO-DT model has demonstrated some practicality in this study, providing a quantitative basis for identifying potential drone users (i.e., groups with positive opinions), but there is still room for optimization in its classification performance. Subsequent research can combine the key influencing factors revealed by SHAP value analysis to adjust model parameters in a targeted manner, to improve the accuracy and stability of predicting farmers’ usage preferences, and provide more reliable decision support for promoting the application of agricultural drones.

4.3. Comparison Between ACO-DT and Other Machine Learning Models

In the research of applying machine learning algorithms to predict farmers’ adoption preferences for drone technology, basic and classic machine learning methods also occupy a place. Analyzing the evaluation metrics of different algorithms can help to gain a deeper understanding of their performance advantages and limitations, which are shown in

Table 2. Comparison with other machine learning models (Test set).

Indicator	ACO-DT	RF	ExtraTrees	XGBoost	LightGBM	SVM	KNN	BP
Accuracy	0.8479	0.430	0.405	0.519	0.481	0.411	0.468	0.667
Precision	0.8322	0.402	0.378	0.502	0.472	0.409	0.483	0.762
Recall	0.9839	0.430	0.405	0.519	0.481	0.411	0.468	0.667
F1 Score	0.9017	0.369	0.384	0.502	0.474	0.402	0.436	0.662
AUC	0.7501	0.811	0.798	0.827	0.809	0.816	0.769	0.853
Runtime	9.365 s	1.025 s	1.108 s	7.022 s	0.744 s	0.337 s	0.029 s	18.438 s

and Figure 6 and Figure 7.

From the perspective of accuracy, different algorithms present notable gradient differences in performance. ACO-DT achieves an impressive accuracy of 0.8479, far surpassing other models and demonstrating exceptional overall sample classification capability, which indicates its strong ability to correctly classify both positive and negative samples regarding drone technology adoption. In contrast, the accuracy of Random Forest (RF) and K-nearest neighbor (KNN) are only 0.430 and 0.468, respectively, positioning them at a relatively low level and suggesting limited effectiveness in classifying such agricultural behavior preference data. The accuracy of Extreme Random Trees (ExtraTrees), XGBoost, LightGBM, and support vector machine (SVM) are 0.405, 0.519, 0.481, and 0.411, respectively, all of which are not ideal. Meanwhile, the BP neural network has an accuracy of 0.667, performing relatively better among these lower-accuracy models but still lagging far behind ACO-DT.

The Precision metric focuses on the reliability of model predictions for the positive category, that is, positive opinions on the adoption of drone technology. ACO-DT boasts a precision of 0.8322, which means that in the samples predicted as having “positive opinions”, the actual proportion of positive classes is very high, and the risk of misjudgment is low, providing a solid basis for formulating promotion strategies based on prediction results. On the other hand, the precision of RF (0.402) and ExtraTrees (0.378) is relatively low, implying a higher probability of misclassifying negative samples as positive, which may lead to inaccurate promotion strategies. The precision of XGBoost (0.502), LightGBM (0.472), SVM (0.409), KNN (0.483), and BP neural network (0.762) varies, with the BP neural network having a relatively outstanding performance in this indicator compared to the other lower-precision models.

Recall measures the ability of a model to capture true positive class samples. ACO-DT has an extremely high recall of 0.9839, which indicates that it can capture almost all true positive samples, providing a strong guarantee for the integrity of potential users in agricultural drone promotion and mining. However, the recall of RF (0.430) and ExtraTrees (0.405) is relatively low, meaning they miss a large proportion of true positive samples, which may result in promotional resources failing to reach the real demand group. The recall of XGBoost (0.519), LightGBM (0.481), SVM (0.411), KNN (0.468), and BP neural network (0.667) also shows variations, with the BP neural network performing relatively better in this aspect among the lower-recall group.

F1 Score, as the harmonic mean of precision and recall, comprehensively reflects the model’s ability to balance “precision-coverage”. ACO-DT achieves an F1 Score of 0.9017, showing an excellent balance between precision and recall, which is crucial for effectively classifying drone technology adoption opinions. The F1 scores of XGBoost (0.502), LightGBM (0.474), SVM (0.402), KNN (0.436), and BP neural network (0.662) are relatively low, indicating their limited ability to balance prediction reliability and sample coverage in this classification task. The F1 scores of RF (0.369) and ExtraTrees (0.384) are also at a low level, further highlighting their inadequacy in balancing these two aspects.

The AUC of ACO-DT (0.7501) is lower than that of traditional models (e.g., XGBoost: 0.827, BP neural network: 0.853, RF: 0.811). The core reason for this difference lies in the varying adaptability between model designs and the evaluation logic of AUC. AUC measures the global ranking ability of distinguishing positive and negative samples across all thresholds, which requires balanced characterization of differences between the two types of samples. To adapt to the goal of “identifying potential adopters of agricultural drone technology among farmers”, ACO-DT is designed to prioritize capturing positive samples. Its parameters and structure focus on sample differentiation in specific scenarios, while weakening the balance of ranking across all thresholds, which ultimately leads to a lower AUC. Although traditional models are not customized for this task, their structures and probability output format are more compatible with the ranking requirement of AUC, thus achieving higher AUC values. This difference does not indicate poor performance of ACO-DT, but rather an inevitable result of its alignment with the task objective.

From the perspective of runtime, the KNN model demonstrates the optimal operational efficiency with a runtime of 0.029. SVM (0.337 s), LightGBM (0.744 s), RF (1.025 s), and ExtraTrees (1.108 s) also exhibit relatively high computational efficiency. The runtimes of XGBoost (7.022 s) and ACO-DT (9.365 s) increase significantly, while the BP model, with a runtime of 18.438, becomes the least efficient model. Although the runtime of ACO-DT is higher than that of models like KNN and SVM, its runtime is only 9.365 s, falling within a reasonable range of around 10 seconds. Moreover, considering its significant advantages in other indicators such as classification accuracy, this runtime overhead is a reasonable trade-off made to achieve feature optimization and precise classification, which does not hinder it from becoming the model with the best comprehensive performance.

Hence, the ACO-DT model outperforms traditional machine learning models such as RF, XGBoost, and SVM in classification tasks, with its core advantage stemming from the integrated Ant Colony Optimization (ACO) feature selection module. This module dynamically screens features based on pheromone positive feedback and heuristic guidance: it strengthens feature combinations that contribute significantly to classification and eliminates redundant features, thereby constructing an input space suitable for the task. In contrast, traditional models either lack the ability to proactively search for feature combinations, are sensitive to high-dimensional features, or rely on manual feature screening, making it difficult to optimize the efficiency of feature utilization. Because of this, the ACO-DT model reduces the risk of overfitting at the source and ultimately achieves higher classification accuracy and recall.

4.4. Results of SHAP Value Analysis

4.4.1. Global Analysis

After completing the classification performance evaluation of ACO-DT and other algorithms, to reveal the impact mechanism of each feature on the prediction of farmers’ drone technology adoption preferences, clarify the direction and strength of key factors, the following text combines the SHAP value result graph in Figure 8 to explain the core features and their mechanism of action.

The SHAP value bar chart visually presents the average impact of each feature on the model prediction results, and the values in the graph clearly reflect the differences in the impact of each feature. Among them, the average SHAP value of “X21. Time Required for Promotion” is the highest, close to 1.25, indicating that the duration of promoting agricultural drone technology is the most critical feature affecting the model’s prediction. This shows that the length of time taken to promote drone agriculture technology has an extremely significant impact on farmers’ decision-making and the prediction results regarding drone agricultural technology. Based on the actual market situation of drone agriculture, if the promotion time is too long, farmers may reduce their willingness to adopt due to a lack of sustained attention or the influence of other competing products during this period; conversely, a suitable (not too short) promotion time can quickly stimulate farmers’ curiosity, timely convey technological advantages, and effectively promote the adoption of the technology.

The average SHAP value of “X8. Understanding of UAV Agriculture” is also relatively high, around 1.0, signifying that the level of understanding of UAV agriculture is another important factor affecting the prediction results. Generally, when farmers have a higher level of understanding, they are more aware of the advantages it brings, such as improving production efficiency and precision operations, and are thus more likely to choose to use it. This also implies that strengthening the popularization of technical knowledge in market promotion can effectively enhance farmers’ acceptance.

In addition, the average SHAP value of “X24. Awareness of ML/AI in UAV Agriculture” is about 0.7, and the average SHAP value of “X12. Overall Attitude towards UAV Agriculture” is around 0.6. The average SHAP values of these features are relatively large and play important roles in model prediction. A positive overall attitude is a key factor directly affecting adoption, which may stem from optimism about the technology’s prospects or the influence of surrounding successful cases. In most cases, a high level of awareness of ML and AI in UAV agriculture means farmers have a better understanding of the advanced technologies involved, which can increase their confidence in the technology, suggesting that promoting knowledge of these advanced technologies in the promotion process is beneficial.

However, features like “X35. Premium Ratio willing to Pay if Productive and Quality Improved” have an average SHAP value of about 0.25, and features such as “X29. Value to Companies Enhance Product Quality and Market Competitiveness” and “X30. Differences in Application Effects by Farm Size” have average SHAP values around 0.2, indicating a relatively weaker impact on the prediction results. Although factors related to economic benefits, product quality, and differences in application effects by farm size are theoretically important for farmers’ adoption of drone agriculture technology from the model prediction results, their influence in the actual decision-making process is not as significant as the aforementioned features. This might be because farmers are more concerned about factors such as technological stability, ease of operation, or the availability of support services.

To analyze the factors influencing the prediction of agricultural drone technology adoption, we utilize SHAP force plots and SHAP waterfall plot in Figure 9, which decompose model predictions by quantifying the contribution of each feature.

In these plots, the baseline prediction E[f(x)] = −2.94 represents the average model output. Feature contributions then shift this prediction. During the offset process from the baseline predicted value E[f(x)] = −2.94 to the final predicted value f(x) = 2.65, X21. Time Required for Promotion = 3 (corresponding to “6–10 years” in the questionnaire) contribution +1.76, becoming the primary factor driving the positive shift in predicted values. This indicates that although this duration is not the shortest “1–2 years” in the questionnaire, it still shows a significant promoting effect on adoption willingness in the sample, which may be related to the balance between technology maturity and farmer acceptance during this cycle. Specifically, compared to the short-term “1–2 years”, it may face the risk of technological immaturity, and the long-term “more than 10 years” may lead to the exhaustion of farmers’ patience. The “6–10 years” cycle not only reserves space for technological iteration (such as the needs of “reducing costs” and “improving ease of use” in Question 22 of the questionnaire) but also enables the gradual fulfillment of improvement needs like usability optimization and the cultivation of farmers’ acceptance through continuous market education. It is the expectation of this “gradual maturation” process that collectively contributes to making the “6–10 years“ promotion cycle the optimal choice in this sample.

At the application level, the negative effect of “X31. Promotion Strategies by Farm Size Customized Solutions for Large Farms = 0” (no customized solution provided) with a contribution of 0.389 is still significant even in the sample dominated by small farms. This is consistent with the high support rate (over 70%) of “providing customized solutions for large farms” in question 31 of the questionnaire, indicating the implicit demand of the market for “full scale coverage”. If there is a lack of strategic consideration for large farms, it will weaken the systematic credibility of technology promotion, thereby affecting the overall adoption willingness. The comprehensive contribution of 88 other features −0.296 may include factors such as “high maintenance costs” and “insufficient technical support” in question 18 of the questionnaire on “use barriers”, but due to the strong positive effect of the core features, the overall trend has not been reversed.

It is interesting that the contribution of “X24. Awareness of ML/AI in UAV Agriculture = 4” (corresponding to “completely unknown”) +1.23 may seem contradictory to intuition, but in fact reflects that in these samples, the “lack of understanding” of advanced technology may be accompanied by lower expected risks or offset by other positive factors to mitigate the negative impact of insufficient cognition. The inconsistency between the contribution of “X30. Differences in Application Effects by Farm Size = 3” (corresponding to “more significant effects on small farms”) +0.45 and the contribution of “X12. Overall Attitude towards UAV Agriculture = 5” (corresponding to “very negative”) +0.434 can be analyzed in conjunction with questions 7 “Whether engaged in agricultural production activities” and “X31. Promotion Strategies by Farm Size Customized Solutions for Large Farms“ of the questionnaire. If the sample is a non-agricultural practitioner (such as agricultural enterprise decision-makers), their “very negative” attitude may be aimed at the technology itself, but it is driven by the high adaptability of small farms (such as the recognition in question 30 that “the effects of small farms are more significant”) and adoption decisions.

4.4.2. Local Analysis

In the global analysis, we found that X21 “Time Required for Promotion” has the greatest importance in the time required for promotion and contributes the most to positive intentions. Therefore, we focus on analyzing its impact here, with the ICE plot and PDP plot in Figure 10.

For “X21. Time Required for Promotion“ (questionnaire options are 1 = “1–2 years”, 2 = “3–5 years”, 3 = “6–10 years”, 4 = “more than 10 years”), the ICE and PDP plots show an inverted U-shaped trend of SHAP values increasing first and then decreasing with the increase in feature values: when the value is 3 (“6–10 years”), the SHAP value reaches a peak close to 1.5, indicating that this promotion cycle has the strongest positive impact on adoption prediction; when the value is 4 (“over 10 years”), the SHAP value drops to around 1.0, and the positive effect weakens but remains significant; when the values are 1 (“1–2 years”) and 2 (“3–5 years”), the SHAP value is close to 0 or slightly negative. The above analysis indicates that a promotion period that is too short or too long has limited positive effects on prediction. This is in line with the expectations of farmers regarding the pace of promotion reflected in question 21 of the questionnaire: a “6–10 year” cycle not only avoids the problems of insufficient technological maturity and farmers’ insufficient cognition in short-term promotion but also avoids the possible decline of interest and competition interference in long-term promotion, thus forming the optimal window for promoting adoption in the time dimension.

For low cognitive groups, risk perception acts as a core intermediary factor that shapes the adoption decision of low-cognition groups. Due to their limited understanding of agricultural drone technology, low-cognition groups lack sufficient ability to assess the actual risks of technology application, which makes their risk perception more sensitive to external signals such as technical complexity, cost uncertainty, and effect instability. This sensitive risk perception will directly enhance their avoidance tendency towards technology adoption, which means they tend to associate potential technical operation difficulties, equipment maintenance costs, and uncertain application effects with high decision-making risks, thereby reducing their willingness to try or accept the technology. At the same time, low-cognition groups often lack effective risk mitigation means, and their risk tolerance is relatively low, which further amplifies the inhibitory effect of risk perception on adoption behavior; even if there are external support signals such as policy guidance, the negative impact of risk perception on their adoption decisions is difficult to be completely offset in the short term.

Against the backdrop that the current level of technological maturity has not yet met expectations, groups with a high level of cognition regarding agricultural drone technology exhibit a significant preference for short-term promotion cycles, forming a distinct contrast with the preference for long-term cycles among groups with low cognition. The core logic behind this difference lies in the fact that high-cognition groups, having a more comprehensive grasp of technical principles, application boundaries, and potential risks of agricultural drones, can more accurately judge the feasibility of technology implementation within a short-term promotion cycle. This means they not only clearly understand the current foundation of technology in terms of operational simplification and cost control but can also rationally assess the potential scope for technological iteration within a short cycle. As a result, they do not need to rely on extending the promotion cycle to reduce decision-making uncertainty and can instead achieve the transformation of technological application value quickly.

4.4.3. Analysis of the Interaction Between Important Indicators

Based on SHAP value dependency graph shown in Figure 11, there is a significant interactive correlation pattern between “X8. Understanding of UAV Agriculture” and “X21. Time Required for Promotion”.

From the perspective of the guiding effect of cognitive level on the preference for promotion cycles, when farmers have a high level of understanding of agricultural drones (feature values 1–2), the SHAP values output by the model are mostly concentrated in the range of −1 and below. Through color mapping, it can be observed that the feature values of “Time Required for Promotion” are presented in dark colors at this time, corresponding to the range of 1–2 (representing a shorter promotion cycle). This data distribution characteristic indicates a significant correlation between a high cognitive level and recognition of short-term promotion. Due to their more comprehensive understanding of technical principles, maturity, application scenario adaptability, and market development trends of drone technology, the high-cognition group can accurately judge the feasibility and potential benefits of technology implementation within a short cycle. Their decision-making logic incorporates higher expectations for technology implementation efficiency, and there is no need to reduce decision uncertainty by extending the promotion period. Therefore, this group is more inclined to accept a compact promotion cycle.

When farmers’ cognitive level decreases to a low level (feature values three and above), the SHAP values shift significantly toward the positive range. At the same time, the color of the feature values for “Time Required for Promotion” gradually transitions to orange and yellow, corresponding to the range of 3–4 (representing a longer promotion cycle). This implies a significant increase in the low-cognition group’s acceptance of long-term promotion. The core driving factor behind this trend is that the low-cognition group has a stronger perception of potential uncertainties related to drone technology, such as operational complexity, maintenance costs, difficulty in troubleshooting, and market acceptance. They lack a clear prediction of the entire process of technology implementation. To avoid decision risks that may arise from information gaps, this group tends to reserve buffer space by extending the promotion cycle. This allows them to gradually acquire technical knowledge, observe application effects, and accumulate market feedback during the promotion process, thereby reducing the uncertainty of technology adoption.

This dynamic correlation between cognitive level and preference for promotion cycles is essentially an external manifestation of the balance between information completeness and risk avoidance needs in farmers’ technology adoption decisions.

The high-cognition group, relying on sufficient technical and market information, can effectively reduce uncertainties in the decision-making process. Therefore, they take efficiency priority as the core of their decision-making and are more likely to accept short-term promotion. In contrast, the low-cognition group, due to obvious information gaps, finds it difficult to accurately assess the risks of technology adoption. Thus, they take risk avoidance as the core of their decision-making and need to make up for information deficiencies by extending the promotion cycle, thereby gradually reducing decision-making risks.

This interactive mechanism is not only reflected through the quantitative correlation between SHAP values and feature values but it also reveals the differentiated decision-making of the two groups in the process of technology acceptance. Together, these elements form a complete path for the formation of differentiated preferences for promotion cycles, which also provides a quantitative basis for the subsequent formulation of differentiated technology promotion strategies.

5. Discussion

5.1. The Current Policy System Still Has Limitations

As an important tool for smart agriculture, agricultural drones have broad prospects for application in agricultural production in China. According to the “14th Five Year Plan for National Agricultural Green Development” [107] and the “Action Plan for Digital Rural Development (2022–2025)” [108], drone technology is clearly used to promote precision fertilization, water-saving irrigation, and intelligent monitoring of farmland. At present, the number of agricultural drones in China is constantly increasing, covering multiple aspects such as sowing, plant protection, and fertilization. Especially in large-scale farms in southern rice producing areas and northern plains, the efficient operation of drones greatly reduces labor costs and improves pesticide utilization, which meets the requirements of national governance of agricultural non-point source pollution and development of green production.

However, while agricultural drones are rapidly developing, there are still many systemic issues at the policy level that hinder their larger scale application. Firstly, there is insufficient financial support in terms of procurement and usage. According to the Implementation Opinions on Subsidies for Agricultural Machinery Purchase and Application from 2024 to 2026 [109], although crop protection drones are included in the scope of central financial subsidies, the subsidy amount is limited and the implementation standards vary from province to province. Many new high-performance models have not been included in the subsidy catalog in a timely manner, and the actual purchase cost for farmers is still very high. In addition, although the Guiding Opinions on Accelerating the High-Quality Development of Agricultural Insurance [110] encourage the development of insurance products related to agricultural machinery, there are still few types of insurance specifically for agricultural drones. In high-risk operations, the protection mechanisms for equipment loss, third-party liability risks, and other aspects are not sound, which affects the enthusiasm of business entities to use them.

In addition to financial support issues, there are also significant institutional barriers in airspace management and cross-regional operations. Although the Interim Regulations on the Management of Unmanned Aerial Vehicle Flight [111] provide a legal basis for low altitude flight, the regulations are more focused on general aviation management and do not fully consider the special characteristics of agricultural operations. Agricultural drones need to fly continuously at low altitude, beyond visual range, and across fields. The current airspace approval process is cumbersome, which is not in line with the direction of encouraging large-scale operation in the “Management Measures for the Transfer of Rural Land Management Rights” [112]. Service teams operating across regions often must repeatedly apply for flight plans in different counties, lacking a nationwide unified and efficient registration mechanism for airspace use that can adapt to agricultural production, which affects the efficiency of large-scale services.

Furthermore, there are also issues of missing standards and poor collaboration in data integration and application. Although the Ministry of Agriculture and Rural Affairs has issued the “Regulations on the Management of Training for Agricultural Unmanned Aerial Vehicle Operators (Trial)” [113], there is still no unified national standard for data collection, transmission, sharing, and security. The format of farmland data generated by drones produced by different manufacturers varies, making it difficult to integrate them into agricultural and rural big data platforms, which limits their deep application in precision agriculture. The “Guidelines for Digital Rural Construction 1.0” [114] emphasizes the need to promote the sharing of agricultural data resources. However, due to the lack of cross-departmental collaboration mechanisms, high-precision geographic information and crop growth data collected by drones have not been effectively integrated with meteorological, water conservancy, financial, and other service institutions, and the value of data has not been fully realized.

In addition, the construction of infrastructure and service systems cannot keep up with the speed of technological development. The “14th Five Year Plan for Promoting Agricultural and Rural Modernization” [115] clearly proposes to “strengthen the research and application of smart agricultural technology and equipment”, but there are no relevant construction support policies for supporting facilities such as take-off and landing points, charging piles, and maintenance stations for agricultural drones. Especially in remote mountainous and hilly areas, the problem of insufficient network coverage is prominent, which cannot meet the needs of real-time data transmission and remote control by drones. This is still far from the goal of “improving the level of rural network facilities” in the “Digital Rural Development Action Plan (2022–2025)” [108].

5.2. Policy Suggestions Based on Results Can Be Established

Based on the results of SHAP value analysis in this article and the shortcomings of the existing policy system mentioned in the previous section, we propose the following targeted suggestions:

5.2.1. Promote the Scientific Design Cycle and Establish a Phased Promotion Mechanism

From the research results, the length of the promotion cycle has a significant impact on farmers’ willingness to accept agricultural drones, with a promotion cycle of 6–10 years being the most effective in promoting farmers’ adoption. This cycle can not only leave time for the improvement of agricultural drone technology (such as reducing costs and improving operational convenience) but also enable farmers to gradually understand and accept the technology through continuous market guidance. Therefore, policymaking needs to avoid hasty or unplanned promotion methods and establish a phased promotion mechanism. In the initial 1–3 years of promotion, pilot projects can be carried out in areas with good foundations such as major grain producing areas and large-scale farms to create reference cases and reduce farmers’ unfamiliarity with new technologies. In the medium term of 4–8 years, expand the scope of promotion based on pilot projects, and develop specific operational guidelines for different crops and terrains to solve practical problems in technology application. In the later 9–10 years, agricultural drones will be included as an important indicator for the development of agricultural mechanization, promoting them to become conventional agricultural production tools. In addition, the promotion strategy is adjusted every three years based on data such as the adoption rate of farmers and the effectiveness of technology use to ensure that the promotion pace meets actual needs.

Regarding the regions with different levels of agricultural development, targeted optimization of the goals, key tasks, and implementation pace of phased promotion is needed to ensure that the promotion process is compatible with the regional agricultural production foundation.

For regions with high-level agricultural development, phased promotion can focus on technological iteration and efficient implementation, promoting it as a routine production tool while radiating and driving technology promotion in surrounding areas. For areas with low-level agricultural development, phased promotion should focus on improving basic supporting facilities, steadily promoting and ensuring practicality, focusing on the application of drones in key production processes, and avoiding blindly pursuing promotion scale while neglecting practical application effects.

5.2.2. Strengthen the Guidance of Technological Cognition and Establish a Hierarchical Education System

Research has found that farmers’ level of awareness of agricultural drones can affect their adoption decisions, and there is currently a situation where farmers with high levels of awareness are more hesitant. This is mainly because these farmers are more aware that the technology is not yet fully mature and there are certain risks, while farmers with insufficient awareness remain optimistic because they do not understand the technical details. To address this issue, policies need to establish a hierarchical technology cognitive education system. For farmers with limited knowledge, they can rely on grassroots agricultural technology promotion sites to focus on introducing the practical benefits of drones, such as improving work efficiency and reducing pesticide waste, through field practical teaching, simple and easy to understand short videos, etc., to avoid explaining complex technical principles and increasing farmers’ understanding burden. For farmers with high cognitive levels, such as new professional farmers and agricultural enterprise employees, technical seminars and expert Q&A activities can be organized to provide detailed explanations of drone operation safety, troubleshooting methods, and data application value. At the same time, their suggestions for technological improvement can be collected and fed back to R&D enterprises to promote the adaptation of technology to farmers’ needs. In addition, incorporating knowledge of agricultural drones into new vocational farmer training courses can enhance farmers’ understanding of technology from the source.

5.2.3. Improve Financial Support Measures and Reduce the Cost of Technology Application

The result points out that the current problems of high procurement costs, incomplete subsidy coverage, and limited exclusive insurance for agricultural drones have reduced farmers’ willingness to use them. Based on this, policies need to improve support from two aspects: subsidies and insurance. In terms of subsidies, expand the scope of central government subsidies, timely include high-performance and multi-purpose agricultural drones (such as models that can simultaneously complete sowing, fertilization, and monitoring) in the subsidy catalog, and increase the subsidy ratio for small-scale farmers and farmers in underdeveloped areas to alleviate their initial investment pressure. Specifically, subsidy support has a more prominent regulating effect on low-technical cognition groups, as it can directly reduce the economic risk perception caused by information asymmetry and technical uncertainty, thereby making up for the adoption hesitation brought by insufficient technical cognition. For high-technical cognition groups, subsidies mainly play a role in reducing the cost of technology application, while their adoption decisions are more affected by technical maturity and application efficiency, so the marginal effect of subsidies is relatively weaker.

In terms of insurance, it is necessary to design differentiated insurance products based on the regional risks of drone application and the protection needs of farmers.

For regions with a high level of agricultural development, insurance products should cover the entire chain of risks such as equipment damage and data security. While providing risk protection, these products should also be matched with rapid fault repair services to form a comprehensive support system. For regions with a low level of agricultural development, insurance products need to focus on the most core risk—equipment damage. By targeting this key risk point, such products can alleviate the concerns of farmers in these regions regarding the risks of technology application, thereby enhancing their willingness to adopt agricultural drones.

5.2.4. Breaking Down Institutional and Data Barriers, Building a Collaborative Application Environment

The cumbersome airspace approval, inconsistent data standards, and insufficient interdepartmental cooperation have limited the large-scale application of agricultural drones and the realization of data value. In this regard, policies need to break through bottlenecks from the perspectives of institutions and data. In terms of airspace management, relevant departments can establish a “filing dynamic approval” mechanism based on relevant flight management regulations and the characteristics of low altitude cross-regional operations of agricultural drones, and then jointly build a unified national agricultural drone flight service platform with air traffic control departments. For this, provinces with high levels of mechanization in agricultural production areas such as Heilongjiang and Jiangsu can be selected as pilot provinces for policies. Farmers or service organizations can simplify the approval process for cross-county agricultural plant protection and other operations by reporting flight plans (including work areas, times, and aircraft types) in advance on the platform, which automatically checks for airspace conflicts and provides feedback on the results. At the same time, priority flight airspace for agricultural drones will be designated in major grain producing areas and economically advantageous crop areas to ensure operational needs during critical agricultural seasons. In terms of data application, it is necessary to establish a unified national agricultural drone data standard to promote the integration of drone data from different manufacturers into the data platform. In addition, establishing an inter-departmental data sharing mechanism to combine the agricultural water and crop growth data collected by drones with meteorological warning, irrigation arrangements, agricultural loans, and other services is also a favorable policy direction.

5.3. Limitations and Future Research Directions

5.3.1. Limitation of the Study

Although this study has achieved certain results in exploring the preferences and technology adoption mechanisms of Chinese farmers towards agricultural drones, there are still several limitations. Firstly, at the data sample level, although the study used 526 questionnaire data covering multiple provinces across the country, and the samples reflected certain representativeness in dimensions such as occupation, income, and region, the sample collection relied on the “Wenjuanxing” platform, which may have insufficient coverage of elderly farmers and small farmers in remote mountainous areas with low acceptance of online surveys, resulting in a sample structure that did not fully match the overall distribution characteristics of Chinese agricultural practitioners, especially in capturing the preferences of non-scale operating entities, which may affect the universality of research conclusions. Secondly, in terms of research methods, although the ACO-DT model outperforms traditional machine learning models in classification accuracy, recall rate, and other indicators, model optimization only focuses on global search of hyperparameters and splitting rules, and does not fully consider the dynamic impact of seasonal and regional differences on farmers’ preferences in agricultural production, such as changes in drone application requirements under different crop planting cycles and climate conditions, resulting in room for improvement in the model’s adaptability to complex agricultural scenarios. In addition, in terms of variable selection, the research mainly focuses on explicit factors such as farmers’ cognition, promotion cycle, and policy support. The consideration of implicit factors such as social network dissemination, intergenerational conceptual differences, and regional agricultural industry structure is insufficient, and the interaction mechanism between these factors and explicit factors has not been deeply explored, which may lead to incomplete explanations of farmers’ adoption decisions.

5.3.2. Future Research Directions

Based on the limitations of this study and existing research gaps, further exploration can be conducted in the following directions in the future. Firstly, in terms of data and sample design, a combination of multi-stage stratified sampling and on-site interviews can be used to expand the sample size and optimize the sample structure, with a focus on supplementing sample data of elderly farmers, small farmers in remote mountainous areas, and specialty crop growers. At the same time, longitudinal tracking data can be introduced to reveal the dynamic evolution of drone adoption preferences by observing farmers’ attitudes and behavior changes at different time points (such as after policy adjustments and technological iterations) over the long term. Secondly, in terms of model and method innovation, the adaptability of the ACO-DT model can be further optimized by incorporating variables such as seasonal factors and regional crop types into the model input, and combining spatiotemporal analysis methods (such as GIS spatial interpolation) to quantify the impact of geographical environment on drone applications. At the same time, it can be attempted to integrate multiple model ensemble strategies, such as combining ACO-DT with deep learning models, to utilize the processing capabilities of deep learning for high-dimensional unstructured data (such as drone operation images and farmland environment data), and improve the accuracy and generalization ability of farmer preference prediction. Thirdly, in terms of expanding research perspectives, we can delve deeper into the mechanisms of implicit influencing factors, such as analyzing the driving effect of “opinion leaders” on drone adoption in farmers’ social networks, or comparing the perceived differences in technological risks among farmers of different age groups based on intergenerational difference theory. In addition, cross regional comparative research can be conducted to compare the drone preferences of Chinese farmers with those of other developing countries, analyze the differential impact of factors such as resource endowment, policy system, and industrial foundation on technology adoption, and provide more targeted experience references for global agricultural drone promotion.

6. Conclusions

This study explores Chinese farmers’ preferences for agricultural drones and their adoption mechanisms in sustainable agriculture, addressing gaps in existing research—insufficient focus on the application end, narrow scope, and lack of quantitative analysis of adoption intentions. By collecting nationwide agricultural practitioner survey data and integrating the Ant Colony Optimization-Decision Tree (ACO-DT) model with SHAP value analysis, it identifies key influencing factors and verifies the methodology’s effectiveness. The survey tool ensures data reliability, and the ACO-DT model outperforms traditional machine learning models in classifying adoption intentions, effectively capturing complex factor-preference relationships to identify potential users. In addition, SHAP analysis shows promotion cycle and farmers’ understanding of agricultural drones are the most critical factors, with appropriate cycles balancing technological maturity and acceptance cultivation, and better understanding helping recognize drone value. Advanced technology awareness and overall attitude also matter, while economic benefits and farm size differences have weaker impacts—reflecting farmers’ priority on technological stability over direct returns. Differentiated cognitive levels correlate with distinct promotion timeline preferences, supporting targeted strategies.