AgriEngineering

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

Agriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, including geospatial tools (e.g., Geographic Information Systems (GISs) and remote sensing (RS)), as well as artificial intelligence (AI), offer promising methods to support this transition. However, their individual capabilities, limitations, and appropriate applications are not always well understood or clearly delineated in the literature. A common issue is the frequent overlap between GISs and RS, with many studies assessing GIS contributions while concurrently employing RS techniques, without explicitly distinguishing between the two (or vice versa). In this sense, the objective of this review is to conduct a critical analysis of the existing state of the art in terms of the distinct roles, limitations, and complementarities of GISs, RS, and AI in advancing CRA, guided by an original definition we propose for CRA (structured around three key dimensions and their corresponding targets). Furthermore, this review introduces a synthesis matrix that integrates both the individual contributions and the synergistic potential of these technologies. This synergy-focused matrix offers not just a summary, but a practical decision support matrix that could be used by researchers, practitioners, and policymakers in selecting the most appropriate technological configuration for their objectives in CRA-related work. Such support is increasingly needed, especially considering that RS and AI have experienced exponential growth in the past five years, while GISs, despite being the more established “big brother” among these technologies, remain underutilized and is often insufficiently understood in agricultural applications. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

There are many products obtained from grapes. The early detection of diseases in an economically important fruit is important, and the spread of disease significantly increases financial losses. In recent years, it is known that artificial intelligence techniques have achieved very successful results in image classification. Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. The proposed hybrid model was compared with two texture-based and four CNN models. The features from the most successful CNN model and texture-based architectures were combined. The NCA method was used to select the best features from the obtained feature map, and the model was classified using the best-known ML classifiers. Our proposed model achieved an accuracy value of 99.1%. This value shows that our model can be used in the detection of grape diseases. Full article

Soil degradation and water scarcity pose major challenges to sustainable agriculture in semiarid regions, requiring innovative strategies to enhance water use efficiency (WUE) and soil fertility. This study assessed the effects of sewage sludge biochar (SSB) on soil properties, WUE, and common bean yield through a small-scale controlled field experiment under rainfed conditions in Northeast Brazil. Four SSB application rates (5, 10, 20, and 40 t ha⁻¹) were compared with conventional NPK fertilization, treated sewage sludge (SS), and chicken manure (CM). The application of 20 t ha⁻¹ (B20) significantly improved soil organic carbon, nitrogen content, water retention, and microbial biomass. B20 also increased WUE by 148% and grain yield by 146% relative to NPK, while maintaining safe levels of potentially toxic elements (PTE) in bean grains. Although 40 t ha⁻¹ (B40) enhanced soil fertility further, it posed a risk of PTE accumulation, reinforcing the advantage of B20 as an optimal and safe dose. These results highlight the potential of SSB to replace or complement conventional fertilizers, especially in sandy soils with limited water retention. The study supports SSB application as a sustainable soil management practice that aligns with circular economy principles, offering a viable solution for improving productivity and environmental resilience in semiarid agriculture. Full article

Cabbage (Brassica rapa L. ssp. Pekinensis) is an important vegetable crop in the Republic of Korea, due to its essential role in kimchi production. However, labor shortages and an aging population necessitate mechanization to sustain productivity. This study aimed to evaluate the field performance of a cabbage transplanter under development with a commercial transplanter and a cabbage collector under greenhouse conditions. This study evaluated transplanting efficiency, planting performance, and yield of cabbage using seedlings at three distinct age groups (30, 35, and 43 days). A cabbage transplanter (Transplanter A) under development, a commercial model (Transplanter B), and manual transplanting were used for comparative analysis. At harvest, a tractor-mounted cabbage collector was used to collect and pack all the cabbages. Transplanter A demonstrated a forward speed of 1.27 km/h and an average planting rate of 2365 seedlings/h, significantly higher than manual transplanting (513 seedlings/h). The effective field capacity (EFC) ranged from 0.11 to 0.13 ha/h, compared to 0.019–0.028 ha/h for manual planting. While Transplanter A showed a higher missing transplant rate (18.17%) than Transplanter B (7.67%), it maintained consistently lower bad planting rates (2.5–4.5%) compared to Transplanter B (3.3–8.8%). In addition, it produced significantly higher cabbage weights (6070 g/plant) and better root metrics than manual transplanting. The cabbage collector achieved 100% efficiency with no crop damage or contamination. The transplanter under development proved effective for greenhouse use, offering faster operation, better planting accuracy, and higher yields, supporting broader mechanization in Korean agriculture. Full article

The adoption of AI vision models in specialized industries is often hindered by the substantial requirement for extensive, manually annotated image datasets. Even when employing transfer learning, robust model development typically necessitates tens of thousands of such images, a process that is time-consuming, costly, and demands consistent expert annotation. This technical note introduces a semi-automated method to significantly reduce this annotation burden. The proposed approach utilizes two general-purpose vision-transformer-to-caption (GP-ViTC) models to generate descriptive text from images. These captions are then processed by a custom-developed semantic classifier (SC), which requires only minimal training to predict the correct image class. This GP-ViTC + SC system demonstrated exemplary classification rates in test cases and can subsequently be used to automatically annotate large image datasets. While the inference speed of the GP-ViTC models is not suited for real-time applications (approximately 10 s per image), this method substantially lessens the labor and expertise required for dataset creation, thereby facilitating the development of new, high-speed, custom AI vision models for niche applications. This work details the approach and its successful application, offering a cost-effective pathway for generating tailored image training sets. Full article

Background: Understanding canopy light interception is essential for optimizing forage production and improving the efficiency of grazing systems. Accurate quantification of photosynthetically active radiation (PAR) intercepted by the canopy allows for better estimation of crop coefficients and growth dynamics. This study aimed to assess the forage mass and nutritional value of Guinea grass pastures managed under two grazing frequencies, defined by 90% and 95% light interception (LI) measured using AccuPar equipment, and two post-grazing stubble heights (30 and 50 cm). Evaluations were conducted during both the rainy season and a dry year to capture seasonal variability in pasture performance. Methods: The experimental design was of completely randomized blocks with four replications. Results: The treatment whit 90% LI resulted in higher values of crude protein and digestible. However, 95% LI resulted in higher values of neutral detergent insoluble nitrogen and acid detergent insoluble nitrogen values in grass pastures Guinea. The highest value of forage mass in Guinea grass was reported with 95% LI in association with a post-grazing height of 30 cm. Conclusions: Management of light interception at 90% provided a reduced amount of forage with better nutritional value. Pasture management considering the light interception technology with the AccuPar equipment was efficient as a pattern for interrupting pasture regrowth in the vegetative phase. Full article

Strawberry post-harvest losses are estimated at 50%, due to improper handling and harvest timing, necessitating the use of non-invasive methods. This study develops a non-invasive in situ bioelectrical spectroscopy for strawberry peduncles. Based on traditional assessments and invasive metrics, 100 physiologically ripe (PR) and 100 commercially mature (CM) strawberries were distinguished. Spectra from their peduncles were measured from 1 kHz to 1 MHz, collecting four parameters (magnitude (Z(f)), phase angle (θ(f)), resistance (R(f)), and reactance (X(f))), resulting in 80,000 raw data points. Through systematic spectral preprocessing, Bode and Cole–Cole plots revealed a distinction between PR and CM strawberries. Frequency selection identified seven key frequencies (1, 5, 50, 75, 100, 250, 500 kHz) for deriving 37 engineered features from spectral, extrema, and derivative parameters. Feature selection reduced these to 6 parameters: phase angle at 50 kHz (θ (50 kHz)); relaxation time (τ); impedance ratio (|Z_1k/Z_250k|); dispersion coefficient (α); membrane capacitance (Cm); and intracellular resistivity (ρi). Four algorithms (TabPFN, CatBoost, GPC, EBM) were evaluated with Monte Carlo cross-validation with five iterations, ensuring robust evaluation. CatBoost achieved the highest accuracy at 93.3% ± 2.4%. Invasive reference metrics showed strong correlations with bioelectrical parameters (r = 0.74 for firmness, r = −0.71 for soluble solids). These results demonstrate a solution for precise harvest classification, reducing post-harvest losses without compromising marketability. Full article

Greenhouses are crucial for maintaining an ideal temperature and humidity level for plant growth; however, attaining ideal levels remains a challenge. Energy-efficient and sustainable alternatives are needed because traditional temperature/humidity control practices and vapor compression air conditioning systems depend on climate conditions and harmful refrigerants. Advanced alternative technologies like evaporative cooling and desiccant dehumidification have emerged that maintain the ideal greenhouse temperature and humidity while using the least amount of energy. This study reviews direct evaporative cooling, indirect evaporative cooling, and Maisotsenko-cycle evaporative cooling (MEC) systems and solid and liquid desiccant dehumidification systems. In addition, integrated desiccant and evaporative cooling systems and hybrid systems are reviewed in this study. The results show that the MEC system effectively reduces the ambient temperature up to the ideal range while maintaining the humidity ratio, and both dehumidification systems effectively reduce the humidity level and improve evaporative cooling efficiency. The integrated systems and hybrid systems have the ability to increase energy efficiency and controlled climatic stability in greenhouses. Regular maintenance, initial system cost, economic feasibility, and system scalability are significant challenges to implement these advanced temperature and humidity control systems for greenhouses. These findings will assist agricultural practitioners, engineers, and researchers in seeking alternate efficient cooling methods for greenhouse applications. Future research directions are suggested to manufacture high-efficiency, low-energy consumption, and efficient greenhouse temperature control systems while considering the present challenges. Full article

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

Accurate estimation of fruit quantity is essential for efficient harvest management, storage, transportation, and marketing in the agricultural industry. To address the limited generalizability of single-species models, this study presents a comprehensive deep learning-based framework for multi-species fruit-load estimation, leveraging the MetaFruit dataset, which contains images of five fruit species collected under diverse orchard conditions. Four representative object detection and regression models—YOLOv8, RT-DETR, Faster R-CNN, and a U-Net-based heatmap regression model—were trained and compared as part of the proposed multi-species learning strategy. The models were evaluated on both the internal MetaFruit dataset and two external datasets, NIHS-JBNU and Peach, to assess their generalization performance. Among them, YOLOv8 and the RGBH heatmap regression model achieved F1-scores of 0.7124 and 0.7015, respectively, on the NIHS-JBNU dataset. These results indicate that a deep learning-based multi-species training strategy can significantly enhance the generalizability of fruit-load estimation across diverse field conditions. Full article

The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim of this article is to review the use of such models and perform three key tasks: (1) identify topics in which ensemble models are used, (2) determine the most widely applied model and the predominant crops and regions, and (3) explore future applications and challenges. As a result, it was noted that the first studies, dating back to 2011, applied ensemble models to model invasive species. Since then, research has focused on changes in temperature and precipitation, with at least one study published every year. The most cited studies have dealt with land use classification, emphasizing its relevance to climate change studies. Notably, studies on carbon storage in soil and its capacity to remove CO₂ from the atmosphere have become increasingly relevant. This analysis highlights the growing importance of ensemble models in climate-related agricultural research, outlining trends and key areas for future exploration. Full article

The adsorption performance of maize seeds in air-suction seed metering devices directly affects the operational quality of maize seeders. The suction holes on the seed metering disc play a crucial role in determining the device’s ability to adsorb maize seeds and serve as a key design parameter for air-suction seed metering systems. Existing research has primarily focused on seed posture control and suction force models for standard particles, while experimental studies on the actual adsorption performance of maize seeds remain scarce. To further investigate the adsorption characteristics of maize seeds under different suction hole geometries, this study employed a self-developed adsorption force measurement platform to conduct experiments on maize seeds in various adsorption postures. The resulting force–displacement curves reveal the variation of adsorption force as seeds detach from the suction holes. To assess the applicability of conventional suction force calculation models, computational fluid dynamics (CFD) simulations were performed to analyze the adsorption mechanism of standard particles. The simulation results indicate significant limitations in commonly used suction force estimation methods. For instance, in experiments evaluating the effect of equivalent adsorption area, the relative error between the suction force estimated by the traditional pressure-based method for triangular holes and the actual measured force reached 40.82%. Similarly, the relative error between the force estimated by the airflow drag method for square suction holes and the actual measured force under the same conditions was 17.14%. Therefore, when evaluating actual seed adsorption, it is essential to comprehensively consider factors such as suction hole geometry, blocked suction area, seed shape, vacuum pressure, and the overlap depth between the seed boundary and the suction hole, all of which significantly influence the adsorption effect. Full article

Emitter clogging in drip irrigation systems is a recurring issue, affecting water application uniformity and system lifespan. This study investigated the anti-clogging performance of emitters and the accumulation patterns of solid particles in dripper labyrinths with varied geometric configurations, combining laboratory experimentation and computational fluid dynamics simulations. Fifteen labyrinth models were tested, divided into two groups: (Model A) emitters with well-defined vortexes and (Model B) emitters with uniform flow. The tests were conducted with solid particle concentrations of 125 and 500 mg L⁻¹ over 200 h of operation. The results showed that none of the emitters became clogged, even under severe particle concentration conditions. However, distinct deposition patterns were observed. Emitters with vortex formation accumulated particles in low-velocity zones, especially in the first baffles of the labyrinth. In contrast, emitters with uniform flow minimized sediment buildup, maintaining high velocities throughout the channel section. Simulations confirmed that the relationship between labyrinth geometry and flow velocity directly influences particle deposition. Dripper design strategies aimed at reducing low-velocity zones in the channel could help mitigate clogging risks. The findings of this study provide valuable guidelines for developing more clogging-resistant emitters, contributing to the improvement of drip irrigation systems. Full article

Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two machine learning (ML) techniques, such as multi-layer perceptrons (MLPs) and support vector machines (SVMs), in the estimation of Hg in 20 meteorological stations with 40 different input combinations involving insolation, air temperature, air relative humidity, photoperiod, and extraterrestrial radiation. It is also compared with three empirical models based on insolation, temperature, and a hybrid combination. In general, the greater the number of input variables, the better the performance of ML techniques, especially in combinations involving insolation that reduced the dispersion of estimated Hg on days with high atmospheric transmissivity and air temperature on days with low atmospheric transmissivity. The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is achieved using SVM, MLP, and empirical models. Therefore, due to their easy implementation and generation of good results, the use of SVM models is recommended to estimate daily global radiation in the Brazilian Amazon. Full article

Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. Full article

Iron deficiency is a widespread public health concern, particularly in regions where rice (Oryza sativa) and beans (Phaseolus spp.) are staple foods with naturally low bioavailable iron content. Agronomic biofortification is a practical strategy to increase micronutrient levels in crops through soil, foliar, and seed-based fertilization techniques. This review synthesizes scientific studies published between 2014 and 2024 that evaluated the effectiveness of agronomic iron biofortification methods in rice and beans. The results demonstrate that site-specific interventions, such as the selection of iron sources and application methods, can improve iron concentration in grains and contribute to more nutritious and resilient food systems. However, challenges remain. There is limited information about human iron bioavailability, and the response to fertilization varies depending on soil and environmental conditions. To address these gaps, future research should include bioavailability assessments and field validation. Even so, integrating iron biofortification into standard fertilization practices is a promising approach to improve food quality and combat hidden hunger in vulnerable populations. Full article