Application of Machine Learning and Data Analysis in Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 15004

Special Issue Editors


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Guest Editor
Ecodevelopment S.A., Filyro P.O. Box 2420, 57010 Thessaloniki, Greece
Interests: soil fertility; plant nutrition; crop growth modelling; machine learning; deep learning; plant and soil analysis

E-Mail Website
Guest Editor
Ecodevelopment S.A., Filyro, P.O. Box 2420, 57010 Thessaloniki, Greece
Interests: remote sensing and GIS; precision agriculture; erosion modelling; land use mapping; environmental impact assessment; fractal analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Hellenic Agricultural Organisation – DEMETER, Soil and Water Resources Institute, PO Box 60458, Thermi, 57001 Thessaloniki, Greece
Interests: spatial analysis; machine learning; geostatistics; soil data; environmental studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in machine learning (ML) enable the identification of patterns within data, facilitating automated predictions of future events and aiding decision-making. ML algorithms excel at detecting nonlinear relationships and complex data structures, frequently encountered in environmental and agricultural data. Concurrently, the integration of ML with data from modern agricultural technologies, such as yield mappers, unmanned aerial vehicles, and satellites, provides valuable information, difficult to model with traditional statistical techniques.

This Special Issue focuses on reporting advances in machine learning applications for agriculture. It encompasses the application of ML and deep learning algorithms for predictive modeling and pattern detection in agricultural data. The importance of enhancing data analysis for real-world ML applications in agriculture has grown significantly due to the increasing global demand for high-quality and safe food.

Research topics may include, but are not limited to:

  • Nitrogen, phosphorus, and potassium fertilization in field and greenhouse crops.
  • Prediction of food quality and factors influencing it using agricultural data.
  • Crop disease detection and prediction of disease risks.
  • Monitoring crop water conditions.
  • Predicting the impact of weather conditions on crop health and yield.
  • Precision agriculture applications based on data collected with agricultural equipment.
  • Monitoring and forecasting the effects of climate change on agriculture.
  • Weed detection using computer vision techniques.

Dr. Miltiadis Iatrou
Dr. Christos Karydas
Dr. Panagiotis Tziachris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • crop growth
  • yield prediction
  • food quality
  • precision agriculture
  • remote sensing
  • crop health
  • crop fertilization
  • deep learning
  • time series analysis

Published Papers (12 papers)

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Research

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22 pages, 11632 KiB  
Article
Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning
by Chenglin Wang, Qiyu Han, Chunjiang Li, Jianian Li, Dandan Kong, Faan Wang and Xiangjun Zou
Agriculture 2024, 14(4), 560; https://doi.org/10.3390/agriculture14040560 - 1 Apr 2024
Cited by 2 | Viewed by 867
Abstract
Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification [...] Read more.
Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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18 pages, 7331 KiB  
Article
Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference
by Miltiadis Iatrou, Miltiadis Tziouvalekas, Alexandros Tsitouras, Elefterios Evangelou, Christos Noulas, Dimitrios Vlachostergios, Vassilis Aschonitis, George Arampatzis, Irene Metaxa, Christos Karydas and Panagiotis Tziachris
Agriculture 2024, 14(4), 549; https://doi.org/10.3390/agriculture14040549 - 30 Mar 2024
Viewed by 1093
Abstract
Storm ‘Daniel’ caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by [...] Read more.
Storm ‘Daniel’ caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. The causal relationships between the soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. The results of the causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and the available phosphorus (P-Olsen), as well as those between the sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25, and 30 cm). Then, linear regression equations were fitted across the different levels of deposit depth to determine the effect of the deposit depth on CaCO3, P, and Mn. The results revealed quadratic equations for CaCO3, P, and Mn as follows: 0.001XCaCO32 + 0.08XCaCO3 + 6.42, 0.004XP2 − 0.26XP + 12.29, and 0.003XMn2 − 0.08XMn + 22.47, respectively. The statistical analysis indicated that corn growing in soils with a sediment over 10 cm requires a 31.8% increase in the P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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20 pages, 7888 KiB  
Article
Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables
by Shih-Lun Fang, Yi-Shan Lin, Sheng-Chih Chang, Yi-Lung Chang, Bing-Yun Tsai and Bo-Jein Kuo
Agriculture 2024, 14(4), 510; https://doi.org/10.3390/agriculture14040510 - 22 Mar 2024
Viewed by 778
Abstract
The reference evapotranspiration (ET0) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET0 calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years [...] Read more.
The reference evapotranspiration (ET0) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET0 calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years of meteorological data from 18 stations in four geographic regions of Taiwan to evaluate the effectiveness of an artificial intelligence (AI) model for estimating PM-calculated ET0 using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET0 forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET0 estimation. The artificial neural network using temperature, solar radiation, and relative humidity as input variables performed best, with the correlation coefficient (r) ranging from 0.992 to 0.998, mean absolute error (MAE) ranging from 0.07 to 0.16 mm/day, and root mean square error (RMSE) ranging from 0.12 to 0.25 mm/day. For short-term ET0 forecasting, the long short-term memory model using temperature, solar radiation, and relative humidity as input variables was the best structure to forecast four-day-ahead ET0, with the r ranging from 0.608 to 0.756, MAE ranging from 1.05 to 1.28 mm/day, and RMSE ranging from 1.35 to 1.62 mm/day. The percentage error of this structure was within ±5% for most meteorological stations over the one-year test period, underscoring the potential of the proposed models to deliver daily ET0 forecasts with acceptable accuracy. Finally, the proposed estimating and forecasting models were developed in regional and variable-limited scenarios, making them highly advantageous for practical applications. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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17 pages, 12515 KiB  
Article
Prediction Model of Flavonoids Content in Ancient Tree Sun−Dried Green Tea under Abiotic Stress Based on LASSO−Cox
by Lei Li, Yamin Wu, Houqiao Wang, Junjie He, Qiaomei Wang, Jiayi Xu, Yuxin Xia, Wenxia Yuan, Shuyi Chen, Lin Tao, Xinghua Wang and Baijuan Wang
Agriculture 2024, 14(2), 296; https://doi.org/10.3390/agriculture14020296 - 12 Feb 2024
Viewed by 789
Abstract
To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, [...] Read more.
To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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30 pages, 2439 KiB  
Article
Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures
by Dilip Kumar Roy, Mohamed Anower Hossain, Mohamed Panjarul Haque, Abed Alataway, Ahmed Z. Dewidar and Mohamed A. Mattar
Agriculture 2024, 14(2), 278; https://doi.org/10.3390/agriculture14020278 - 8 Feb 2024
Viewed by 1080
Abstract
This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models [...] Read more.
This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead Tmax and Tmin forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for Tmax and Tmin across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both Tmax and Tmin forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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27 pages, 14344 KiB  
Article
Intelligent Cotton Pest and Disease Detection: Edge Computing Solutions with Transformer Technology and Knowledge Graphs
by Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan
Agriculture 2024, 14(2), 247; https://doi.org/10.3390/agriculture14020247 - 2 Feb 2024
Cited by 1 | Viewed by 1045
Abstract
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disease feature recognition precision. With the application of edge computing technology, [...] Read more.
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disease feature recognition precision. With the application of edge computing technology, efficient data processing and inference analysis on mobile platforms are facilitated. Experimental results indicate that the proposed method achieved an accuracy rate of 0.94, a mean average precision (mAP) of 0.95, and frames per second (FPS) of 49.7. Compared with existing advanced models such as YOLOv8 and RetinaNet, improvements in accuracy range from 3% to 13% and in mAP from 4% to 14%, and a significant increase in processing speed was noted, ensuring rapid response capability in practical applications. Future research directions are committed to expanding the diversity and scale of datasets, optimizing the efficiency of computing resource utilization and enhancing the inference speed of the model across various devices. Furthermore, integrating environmental sensor data, such as temperature and humidity, is being considered to construct a more comprehensive and precise intelligent pest and disease detection system. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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21 pages, 1849 KiB  
Article
Impacts of Geographical Indications on Agricultural Growth and Farmers’ Income in Rural China
by Xiaoyu Yin, Jia Li, Jingyi Wu, Ruihan Cao, Siqian Xin and Jianxu Liu
Agriculture 2024, 14(1), 113; https://doi.org/10.3390/agriculture14010113 - 10 Jan 2024
Cited by 2 | Viewed by 1580
Abstract
Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers’ income. Utilizing a large county-level dataset and comprehensive [...] Read more.
Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers’ income. Utilizing a large county-level dataset and comprehensive official GI information, this study estimates the impact of GIs on agricultural output and rural income using panel-fixed-effects models. The results reveal that GIs significantly boost agricultural added value and rural per capita disposable income. A series of methods, including difference-in-differences, propensity score matching with difference-in-differences, and double machine learning combined with difference-in-differences using random forests verify the robustness of the results. Moreover, by categorizing GIs based on product types, the analysis reveals heterogeneous effects of different GI categories on agricultural growth and income gains for farmers. The research findings in this paper offer valuable insights to inform policymaking aimed at advancing rural development, raising farmers’ incomes, and promoting sustainable agri-food supply chains. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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14 pages, 2031 KiB  
Article
Winter Wheat Yield Estimation Based on Multi-Temporal and Multi-Sensor Remote Sensing Data Fusion
by Yang Li, Bo Zhao, Jizhong Wang, Yanjun Li and Yanwei Yuan
Agriculture 2023, 13(12), 2190; https://doi.org/10.3390/agriculture13122190 - 23 Nov 2023
Cited by 2 | Viewed by 1026
Abstract
Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, [...] Read more.
Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R2 of 0.83, an RMSE of 297.70 kg/hm2, and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R2 of 0.80 and 0.80, RMSE of 330.37 kg/hm2 and 328.95 kg/hm2, and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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19 pages, 3176 KiB  
Article
Deep Learning Tools for the Automatic Measurement of Coverage Area of Water-Based Pesticide Surfactant Formulation on Plant Leaves
by Fabio Grazioso, Anzhelika Aleksandrovna Atsapina, Gardoon Lukman Obaeed Obaeed and Natalia Anatolievna Ivanova
Agriculture 2023, 13(12), 2182; https://doi.org/10.3390/agriculture13122182 - 22 Nov 2023
Viewed by 1091
Abstract
A method to efficiently and quantitatively study the delivery of a pesticide-surfactant formulation in a water solution to plant leaves is presented. The methodology of measurement of the surface of the leaf wet area is used instead of the more problematic measurement of [...] Read more.
A method to efficiently and quantitatively study the delivery of a pesticide-surfactant formulation in a water solution to plant leaves is presented. The methodology of measurement of the surface of the leaf wet area is used instead of the more problematic measurement of the contact angle. A method based on a Deep Learning model was used to automatically measure the wet area of cucumber leaves by processing the frames of video footage. We have individuated an existing Deep Learning model, called HED-UNet, reported in the literature for other applications, and we have applied it to this different task with a minor modification. The model was selected because it combines edge detection with image segmentation, which is what is needed for the task at hand. This novel application of the HED-UNet model proves effective, and opens a wide range of new applications, the one presented here being just a first example. We present the measurement technique, some details of the Deep Learning model, its training procedure and its image segmentation performance. We report the results of the wet area surface measurement as a function of the concentration of a surfactant in the pesticide solution, which helps to plan the surfactant concentration. It can be concluded that the most effective concentration is the highest in the range tested, which is 11.25 times the CMC concentration. Moreover, a validation error on the Deep Learning model, as low as 0.012 is obtained, which leads to the conclusion that the chosen Deep Learning model can be effectively used to automatically measure the wet area on leaves. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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23 pages, 4648 KiB  
Article
Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model
by Yifang Zhou, Mingzhang Pan, Wei Guan, Changcheng Fu and Tiecheng Su
Agriculture 2023, 13(11), 2115; https://doi.org/10.3390/agriculture13112115 - 8 Nov 2023
Viewed by 1538
Abstract
As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast [...] Read more.
As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R2, MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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23 pages, 4305 KiB  
Article
LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases
by Jianlei Kong, Yang Xiao, Xuebo Jin, Yuanyuan Cai, Chao Ding and Yuting Bai
Agriculture 2023, 13(11), 2080; https://doi.org/10.3390/agriculture13112080 - 31 Oct 2023
Cited by 2 | Viewed by 1249
Abstract
In the realm of smart agriculture technology’s rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a [...] Read more.
In the realm of smart agriculture technology’s rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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Review

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19 pages, 1027 KiB  
Review
A Review of Machine Learning Techniques in Agroclimatic Studies
by Dania Tamayo-Vera, Xiuquan Wang and Morteza Mesbah
Agriculture 2024, 14(3), 481; https://doi.org/10.3390/agriculture14030481 - 16 Mar 2024
Viewed by 1156
Abstract
The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL [...] Read more.
The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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