Computers and Electronics in Agriculture最新文献

{"title":"Segmentation of FHB infection in wheat ear using super-resolution of UAV image and reception enrichment gate network","authors":"Shizhuang Weng ,&nbsp;Yulong Liu ,&nbsp;Yehang Wu ,&nbsp;Tianle Liu ,&nbsp;Min Fan ,&nbsp;Shouguo Zheng ,&nbsp;Yahui Su","doi":"10.1016/j.compag.2025.110552","DOIUrl":"10.1016/j.compag.2025.110552","url":null,"abstract":"<div><div><em>Fusarium</em> head blight (FHB) is one of the most serious wheat diseases and mainly infects the ear, affecting the yield and quality of wheat worldwide. Segmentation of FHB infection in wheat ear based on unmanned aerial vehicle (UAV) images is feasible and significant in ensuring timely control measures and maintaining food security. The high flight altitude of UAV allows for rapid image acquisition but results in blurred textures and details, and the variability of field environment leads to missed and false segmentation. To address these problems, we first executed the super-resolution (SR) of high-altitude UAV images, and then FHB infection was segmented using a deep gate network. Specifically, an SR network called hierarchical context aggregation network (HCAN) was developed to generate clear textures and detailed characteristics of wheat efficiently through the successive fusion of various contexts. HCAN was superior to the current state-of-the-art methods with a peak signal-to-noise ratio of 29.056 dB and a structural similarity index of 0.9142. Meanwhile, a reception enrichment gate network (REGN) was applied to segment FHB infection in wheat ear through the integration of dual-gate mechanism and multi-scale convolution. REGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %. Indistinct edges, missed segmentation, and false segmentation were dramatically alleviated in high-density, overlapping, shaded and overexposed wheat because local and neighboring gate operations enhanced the representation and reception field, and multi-scale convolution could enrich the reception diversity. In sum, the proposed approach provided a reliable, efficient, and accurate determination of FHB infection in wheat on the basis of UAV images and could be extended to the analysis of other diseases or crops.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110552"},"PeriodicalIF":7.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transient sound signal analysis for watermelon ripeness detection using HHT and NMF","authors":"Yijie Li ,&nbsp;Chunhao Cao ,&nbsp;Mengke Cao ,&nbsp;Wenchuan Guo","doi":"10.1016/j.compag.2025.110543","DOIUrl":"10.1016/j.compag.2025.110543","url":null,"abstract":"<div><div>Harvesting watermelon at an inappropriate time can significantly impact its quality and flavor. To ensure rapid, reliable, and nondestructive determination of watermelon ripeness, this study focuses on analyzing the tapping sound of watermelons at various ripeness levels. The tapping sound, characterized as a transient acoustic signal, exhibits consistent resonance properties but varying frequency features across ripeness stages. A sound processing method was developed by integrating Nonnegative Matrix Factorization (NMF) filtering and Root-Mean-Square (RMS) normalization. Frequency characteristics and variations in watermelon tapping sounds were analyzed using the Hilbert-Huang Transform (HHT) and NMF-based feature extraction. Machine learning models, including Support Vector Machine (SVM), HHT combined with SVM (HHT + SVM), and NMF combined with SVM (NMF + SVM), were employed to classify watermelons of different ripeness levels. Experimental results, based on 100 samples each of unripe, ripe, and overripe watermelons, showed a gradual decrease in the average frequency distribution of tapping sounds from unripe to overripe stages. The classification accuracy of watermelon ripeness using SVM alone was 62.78 %, which improved to 74.44 % with HHT + SVM and further increased to 92.22 % with NMF + SVM. These findings demonstrate that feature extraction methods based on NMF and HHT effectively capture the frequency characteristics and time-decay properties of transient acoustic signals. This study offers an efficient and practical method for acoustic nondestructive detection of watermelon ripeness, providing a novel approach for processing transient and abrupt sound signals with broad potential applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110543"},"PeriodicalIF":7.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop height retrieval from polarimetric SAR data using machine learning: A comparative and validation study","authors":"Shuaifeng Hu ,&nbsp;Qinghua Xie ,&nbsp;Xing Peng ,&nbsp;Qi Dou ,&nbsp;Jinfei Wang ,&nbsp;Juan M. Lopez-Sanchez ,&nbsp;Jiali Shang ,&nbsp;Haiqiang Fu ,&nbsp;Jianjun Zhu ,&nbsp;Wenming Zhou","doi":"10.1016/j.compag.2025.110580","DOIUrl":"10.1016/j.compag.2025.110580","url":null,"abstract":"<div><div>Accurate and efficient crop height information retrieval is crucial for applications such as farmland management, growth monitoring, yield estimation, and pest monitoring. Polarimetric Synthetic Aperture Radar (PolSAR) is known for its high sensitivity to the shape, structure, and dielectric constant of vegetation, presenting great potential for crop height retrieval. In this study, we compare the performance of three machine learning algorithms, Random Forest Regression (RFR), Bagging Decision Tree (BAGTREE), and Extreme Gradient Boosting (XGBoost), in the retrieval of crop height from PolSAR data. Using a comprehensive approach, we constructed a set of 32 polarimetric features as the initial input for the model. Subsequently, feature selection is employed to generate a subset aimed at reducing redundancy and improving the final estimation accuracy. Multi-temporal C-band PolSAR RADARSAT-2 data collected over three distinct agricultural types (corn, wheat, and soybean) in Canada are chosen for this study. The results indicate that the optimal average Root Mean Square Error (RMSE) for height retrieval in corn, wheat, and soybean throughout their growth cycles is 43.69 cm, 10.78 cm, and 20.92 cm, respectively. Among the three algorithms, RFR consistently demonstrates stable retrieval performance, and the polarimetric decomposition parameters exhibit the highest sensitivity to crop height. This study offers a valuable technical reference for SAR-based crop height retrieval and remote sensing-based crop growth monitoring without interferometry.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110580"},"PeriodicalIF":7.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving pig audio signal recognition via integrated underdetermined blind source separation and deep learning","authors":"Weihao Pan ,&nbsp;Yi Fang ,&nbsp;Xiaobo Zhou ,&nbsp;ShunPi Yan ,&nbsp;Jun Jiao ,&nbsp;Guodong Wu ,&nbsp;Cheng Zhu","doi":"10.1016/j.compag.2025.110511","DOIUrl":"10.1016/j.compag.2025.110511","url":null,"abstract":"<div><div>To address the challenges of separating and identifying pig vocalizations in group-housing environments, this study proposes a novel method for pig audio signal recognition based on underdetermined blind source separation and ECA-EfficientNetV2. Four types of pig vocalizations in a simulated group-housing environment were used as observed signals captured by recording devices. It estimates the observed signal mixed matrix by hierarchical clustering after signal sparse representation. The l_p norm reconstruction algorithm is used to solve the minimum l_p norm to complete the audio signal reconstruction of four kinds of pigs. The reconstructed signals are converted into spectrograms, which are composed of eating sound, howling sound and humming sound, and then identified the audio signals by ECA-EfficientNetV2 network model. The results show that the minimum normalized mean square error (NMSE) estimated by mixed matrix is 3.2660e-04, and the reconstructed audio signal-to-noise ratio (SNR) is 3.254–4.267 dB. The accuracy of ECA-EfficientNetV2 model in recognizing spectrograms is as high as 98.35 %, which is improved by 2.88 % and 1.81 % compared to lightweight convolutional neural networks MobileNetV2 and ShuffleNetV2, while the model parameters are reduced by 35.67 % compared to the original EfficientNetV2. The results indicate that the pig audio signal recognition method based on blind source separation improves EfficientNetV2, realizes the separation and recognition of the audio signals of herd pigs in a light and efficient manner.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110511"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved pest and disease spread model for simulating the spread of invasive species: A case study of pine wilt disease","authors":"Zhuoqing Hao ,&nbsp;Wenjiang Huang ,&nbsp;Biyao Zhang ,&nbsp;Yifan Chen ,&nbsp;Guofei Fang ,&nbsp;Jing Guo ,&nbsp;Yanru Huang ,&nbsp;Xiangzhe Cheng ,&nbsp;Bohai Hu","doi":"10.1016/j.compag.2025.110561","DOIUrl":"10.1016/j.compag.2025.110561","url":null,"abstract":"<div><div>Biological invasion is a major environmental issue facing the world today. Controlling invasive species has become a key focus of governments globally. Pine wilt disease (PWD) is a significant invasive species in China. This study examines the invasion and expansion processes of PWD and provides control strategies. Three regions in China with differing climatic, environmental, and socio-economic conditions were selected. Using real disaster records from 2018 to 2022, the K-means clustering method was applied to analyse the mechanisms of disease spread. Epidemic patches were classified into two groups: invasion-type and expansion-type. By considering both natural and human factors driving the invasion and expansion of PWD, the PoPS model was improved. The model was then used to analyse the effectiveness of control measures against PWD spread in China. The results indicate that the calculated boundary distances and the improved model effectively explained new outbreaks caused by the invasion and expansion processes. Simulation results, combined with China’s current control strategies, provide recommendations for disaster prevention. This study describes PWD spread as two processes: invasion and expansion. It reveals the transmission dynamics of PWD in China, offers a predictive model, and proposes control strategies. The findings provide scientific support for disaster prevention and research on invasive species.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110561"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning and improved ResNet34-based objective classification evaluation method for gastrodia elata quality","authors":"Jianxu Wang ,&nbsp;Yuyang Xiao ,&nbsp;Jian Feng ,&nbsp;Yang Fang ,&nbsp;Kaile Zhu ,&nbsp;Ming Yang ,&nbsp;Deguang Wang","doi":"10.1016/j.compag.2025.110563","DOIUrl":"10.1016/j.compag.2025.110563","url":null,"abstract":"<div><div>The gastrodia elata as a rare Chinese medicinal material planted in eastern Asia, which has high a medicinal value, and the classification evaluation is the key to promote its medicinal value and quality grading. However, the current classification evaluation mostly relies on the manual subjective experience judgment, the chemical detection, and the deep neural networks with lost feature extraction details, large environmental impact, poor robustness and generalization ability, and frequent gradient disappearance and explosion. To well solve the above problems, a transfer learning and improved ResNet34-based objective classification evaluation method for gastrodia elata quality is investigated. Firstly, experts classify the gastrodia elata sample images, and respectively obtains the excellent, good, medium and poor images of 812, 768, 816, and 820, which are divided into training, verification, and testing sets according to 6:2:2, and the image expansion and enhancement are performed so as to make gastrodia elata sample image dataset. Whereafter, the 7x7 convolution kernel of the network initial layer is changed into three consecutive 3x3 convolution cores to reduce the parameters and computational load and achieve the extraction of deep features, and the CBAM attention mechanism is added into the BasicBlock of four-layer to accomplish the aggregation of gastrodia elata sample feature information in space and channels. Finally, the number of BasicBlock in the four-layer is fine-tuned to eliminate redundant feature weights in the model and achieve a relative balance between accuracy and training speed. The experimental results compared with current LeNet5, AlexNet, VGG16, ResNet18 and traditional ResNet34 demonstrate that when the learning rate is 0.001, the Dropout rate is 0.3, and the weight attenuation parameter in the AdaMax optimizer is 0.0001, the investigated method has the best classification performance, the good robustness and consistency. The classification accuracy for verification and testing sets reaches to 97.79% and 93.16%, and the F1-scores of excellent, good, medium, and poor are as high as 0.91, 0.94, 0.88, and 0.99, respectively.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110563"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time semantic SLAM-based 3D reconstruction robot for greenhouse vegetables","authors":"Penggang Wang ,&nbsp;Wei Luo ,&nbsp;Jiandong Liu ,&nbsp;Yongxu Zhou ,&nbsp;Xuqing Li ,&nbsp;Shipeng Zhao ,&nbsp;Guoqing Zhang ,&nbsp;Yongxiang Zhao","doi":"10.1016/j.compag.2025.110582","DOIUrl":"10.1016/j.compag.2025.110582","url":null,"abstract":"<div><div>Eggplant harvesting involves processes such as planting, monitoring, and spraying. However, in large eggplant plantations, relying solely on manual labor to complete these tasks not only requires extensive time and high capital costs but also results in extremely low efficiency. By combining unmanned vehicles and visual simultaneous localization and mapping (SLAM) for the three-dimensional (3D) reconstruction of an eggplant orchard we can intuitively obtain information regarding the orchard while providing a foundation for automating the aforementioned tasks. This study proposes a 3D semantic mapping and navigation solution for eggplant orchard based on visual SLAM. The baseline model of this system uses ORB-SLAM2, with improvements made by incorporating SCTNet-B, a semantic segmentation network, to enrich the representation of the point cloud map. In addition, in the tracking thread, a direct method is employed for feature extraction and matching, reducing computational load and enhancing real-time processing speed. Moreover, the point cloud map is converted into an OctoMap to reduce storage consumption. The system is also equipped with the A* algorithm and EGO-Planner algorithm for autonomous navigation of the unmanned vehicle. We implement the proposed system on an unmanned vehicle for field testing and evaluation. The experimental results demonstrate that the SCTNet-B achieves a mean intersection over union of 88.74% for semantic segmentation on a custom eggplant image dataset, with a frame rate of 28.36 FPS. After adopting the direct method, the system’s front-end tracking thread reduces the average processing time per frame by 55.68%. Using an OctoMap for storage of the semantic point cloud map results in an average reduction in memory consumption of 95.20%. The proposed system, integrated with the unmanned vehicle, meets the requirements for real-time semantic mapping and storage in large-scale eggplant orchard.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110582"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer vision-based animal phenotyping and analysis in presence of uncertain identification","authors":"Mina Shumaly ,&nbsp;Yunsoo Park ,&nbsp;Saif Agha ,&nbsp;Santosh Pandey ,&nbsp;Juan Steibel","doi":"10.1016/j.compag.2025.110560","DOIUrl":"10.1016/j.compag.2025.110560","url":null,"abstract":"<div><div>Animal identification (ID) is key for implementing precision livestock farming technologies. Animal ID algorithms typically generate a probability vector representing the likelihood of each potential individual. Conventionally, the individual with the highest probability is selected as the putative ID. However, this practice may reduce the precision of subsequent downstream analysis by disregarding the inherent uncertainty in the probability distribution. In this study, a mixture model is proposed to incorporate the uncertainty of the ID assignment into downstream analysis, aiming to investigate the impact of ignoring/incorporating the uncertainty of assignment in the subsequent estimations.</div><div>We applied our method on two datasets: 1) a publicly available dataset of 3226 images from 30 thoroughbred horses, classified based on body morphometrics using Linear Discriminant Analysis (LDA) with an accuracy of 88 %, where we simulated independent phenotypes with varying group effect sizes and variances, and 2) a dataset comprising 1770 images from 59 Holstein cattle, classified using Support Vector Machines (SVM) with an accuracy of 95 %, where phenotypes were extracted as measurements of body area from each image.</div><div>We analyzed the phenotypic data for both datasets to estimate group means and variance components using three approaches: 1) using the correct IDs, 2) using the top probability assignments, and 3) incorporating ID uncertainty through mixture models. Our results indicate that incorporating the mixture model improved the accuracy of variance component estimation and significantly increased the confidence of ID predictions across both datasets.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110560"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil porosity in integrated and non-integrated grazing systems in a Brazilian Ferralsol assessed by 3D X-ray computed tomography","authors":"Gustavo Pereira Valani ,&nbsp;Aline Fachin Martíni ,&nbsp;Cássia Bezerra Machado ,&nbsp;Daniel Amancio Duarte ,&nbsp;Marcio Fernando Paixão de Brito ,&nbsp;José Ricardo Macedo Pezzopane ,&nbsp;Alberto Carlos de Campos Bernardi ,&nbsp;Antonio Celso Dantas Antonino ,&nbsp;Richard John Heck ,&nbsp;Daniel Giménez ,&nbsp;Miguel Cooper","doi":"10.1016/j.compag.2025.110557","DOIUrl":"10.1016/j.compag.2025.110557","url":null,"abstract":"<div><div>Integrated grazing systems enhance soil quality; however, little is known about the impact of these systems on the properties and spatial density of soil pores. This study evaluates three-dimensional pore characteristics in a Brazilian Ferralsol under integrated and non-integrated grazing systems. Six soil management systems were studied: continuous grazing (CONT), rotational grazing (ROT), integrated crop-livestock system (ICL), integrated livestock-forest system (ILF), integrated crop-livestock-forest system (ICLF), and native vegetation (NV). Samples from four depths, 0 – 12 cm, 12 – 24 cm, 26 – 38 cm and 84 – 96 cm, were scanned with an X-ray computed tomography instrument at a spatial resolution of 50  μm. The scanned volumes were reconstructed and segmented. Image analyses included the percentage, size, shape, orientation and connectivity of image-based pores, the fractal dimensions <em>D0</em>, <em>D1</em>, <em>D2</em>, and the parameters Δ<em>α</em>, <em>α</em>_ratio and <em>f</em>(<em>α</em>)_ratio from multifractal analysis, as well as entropy assessments. Differences between soil management systems and soil depths were compared by one-way ANOVA on ranks. Soil compaction reduced image-based porosity down to 38 cm in all grazing systems in relation to NV. Compaction also altered pore morphology, decreasing triaxial, prolate, oblate, and equant pores while increasing complex-shaped pores. The two most common pore orientation classes were near-horizontal and inclined, which accounted for over 70 % of the distribution in each system. The only system with negative Euler numbers for all soil layers studied was NV, which implies that grazing reduces pore connectivity when compared to natural vegetation. Pore systems tended to have more clearly defined multifractal properties near the surface than deeper into the soil. Most noticeable differences in pore entropy were found between 26 and 38 cm, being highest in NV, intermediate in integrated grazing systems with trees (ILF and ICLF) and lowest in other grazing systems (CONT, ROT and ICL). The outlook of this work is that soil management strategies for integrated systems must avoid soil compaction by adjusting stocking rates, controlling traffic, maintaining soil cover and diversifying crop rotation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110557"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting intra-field yield variations for winter wheat using remote sensing and Graph Attention Networks","authors":"Oskar Åström ,&nbsp;Simon Månsson ,&nbsp;Isac Lazar ,&nbsp;Magnus Nilsson ,&nbsp;Joakim Ekelöf ,&nbsp;Andreas Oxenstierna ,&nbsp;Alexandros Sopasakis","doi":"10.1016/j.compag.2025.110499","DOIUrl":"10.1016/j.compag.2025.110499","url":null,"abstract":"<div><div>Accurate prediction of spatial yield variations within individual fields is crucial for precision agriculture, as it enables optimized resource allocation and targeted crop management. In this study, we propose a novel framework that leverages remote sensing data and Graph Attention Networks (GATv2) to predict fine-scale yield variations for winter wheat at a high resolution (10<!--> <!-->m <span><math><mo>×</mo></math></span> 10<!--> <!-->m). The objectives of our research are twofold: (i) to develop an integrated, multi-modal prediction model that embeds temporal information directly into a graph-based architecture to capture both global and local spatiotemporal dependencies, and (ii) to rigorously evaluate the model’s performance in post-harvest yield estimation and pre-harvest yield forecasting.</div><div>Our approach fuses high-resolution Sentinel-2 imagery, spectral indices, soil characteristics, and weather dynamics within a unified graph structure, eliminating the need for separate temporal models while dynamically adjusting the influence of neighboring nodes via attention mechanisms. Experimental results demonstrate competitive performance, with normalized RMSE values of 11.5% for absolute yield and 9.6% for yield variation, alongside R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> scores of 80.7% and 86.9%, respectively, in post-harvest yield estimation. Moreover, our framework successfully forecasts intra-field yield variability up to a year in advance (nRMSE of 11.4%), underscoring its robustness and stability across diverse data conditions. By identifying stable, field-specific factors governing spatial yield variability, the model highlights the separability of yield variation from overall yield levels. This capability provides actionable insights for both immediate interventions and strategic planning, enabling optimized resource allocation, reduced waste, and minimized environmental impacts from over-fertilization. These results further underscore the potential of graph-based machine learning to transform precision agriculture through scalable, and high-resolution yield prediction.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110499"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}