Artificial Intelligence in Agriculture最新文献

{"title":"Effective methods for mitigate the impact of light occlusion on the accuracy of online cabbage recognition in open fields","authors":"Hao Fu ,&nbsp;Xueguan Zhao ,&nbsp;Haoran Tan ,&nbsp;Shengyu Zheng ,&nbsp;Changyuan Zhai ,&nbsp;Liping Chen","doi":"10.1016/j.aiia.2025.04.002","DOIUrl":"10.1016/j.aiia.2025.04.002","url":null,"abstract":"<div><div>To address the low recognition accuracy of open-field vegetables under light occlusion, this study focused on cabbage and developed an online target recognition model based on deep learning. Using Yolov8n as the base network, a method was proposed to mitigate the impact of light occlusion on the accuracy of online cabbage recognition. A combination of cabbage image filters was designed to eliminate the effects of light occlusion. A filter parameter adaptive learning module for cabbage image filter parameters was constructed. The image filter combination and adaptive learning module were embedded into the Yolov8n object detection network. This integration enabled precise real-time recognition of cabbage under light occlusion conditions. Experimental results showed recognition accuracies of 97.5 % on the normal lighting dataset, 93.1 % on the light occlusion dataset, and 95.0 % on the mixed dataset. For images with a light occlusion degree greater than 0.4, the recognition accuracy improved by 9.9 % and 13.7 % compared to Yolov5n and Yolov8n models. The model achieved recognition accuracies of 99.3 % on the Chinese cabbage dataset and 98.3 % on the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge computing device, achieving an image processing speed of 26.32 frames per second. Field trials showed recognition accuracies of 96.0 % under normal lighting conditions and 91.2 % under light occlusion. The proposed online cabbage recognition model enables real-time recognition and localization of cabbage in complex open-field environments, offering technical support for target-oriented spraying.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 449-458"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach","authors":"Mehdi Fasihi ,&nbsp;Mirko Sodini ,&nbsp;Alex Falcon ,&nbsp;Francesco Degano ,&nbsp;Paolo Sivilotti ,&nbsp;Giuseppe Serra","doi":"10.1016/j.aiia.2025.03.003","DOIUrl":"10.1016/j.aiia.2025.03.003","url":null,"abstract":"<div><div>Predicting grapevine phenological stages (GPHS) is critical for precisely managing vineyard operations, including plant disease treatments, pruning, and harvest. Solutions commonly used to address viticulture challenges rely on image processing techniques, which have achieved significant results. However, they require the installation of dedicated hardware in the vineyard, making it invasive and difficult to maintain. Moreover, accurate prediction is influenced by the interplay of climatic factors, especially temperature, and the impact of global warming, which are difficult to model using images. Another problem frequently found in GPHS prediction is the persistent issue of missing values in viticultural datasets, particularly in phenological stages. This paper proposes a semi-supervised approach that begins with a small set of labeled phenological stage examples and automatically generates new annotations for large volumes of unlabeled climatic data. This approach aims to address key challenges in phenological analysis. This novel climatic data-based approach offers advantages over common image processing methods, as it is non-intrusive, cost-effective, and adaptable for vineyards of various sizes and technological levels. To ensure the robustness of the proposed Pseudo-labelling strategy, we integrated it into eight machine-learning algorithms. We evaluated its performance across seven diverse datasets, each exhibiting varying percentages of missing values. Performance metrics, including the coefficient of determination (R²) and root-mean-square error (RMSE), are employed to assess the effectiveness of the models. The study demonstrates that integrating the proposed Pseudo-labeling strategy with supervised learning approaches significantly improves predictive accuracy. Moreover, the study shows that the proposed methodology can also be integrated with explainable artificial intelligence techniques to determine the importance of the input features. In particular, the investigation highlights that growing degree days are crucial for improved GPHS prediction.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 550-563"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating challenges/opportunities in developing smart agricultural extension platforms: Multi-media data mining techniques","authors":"Josué Kpodo ,&nbsp;A. Pouyan Nejadhashemi","doi":"10.1016/j.aiia.2025.04.001","DOIUrl":"10.1016/j.aiia.2025.04.001","url":null,"abstract":"<div><div>Agricultural Extension (AE) research faces significant challenges in producing relevant and practical knowledge due to rapid advancements in artificial intelligence (AI). AE struggles to keep pace with these advancements, complicating the development of actionable information. One major challenge is the absence of intelligent platforms that enable efficient information retrieval and quick decision-making. Investigations have shown a shortage of AI-assisted solutions that effectively use AE materials across various media formats while preserving scientific accuracy and contextual relevance. Although mainstream AI systems can potentially reduce decision-making risks, their usage remains limited. This limitation arises primarily from the lack of standardized datasets and concerns regarding user data privacy. For AE datasets to be standardized, they must satisfy four key criteria: inclusion of critical domain-specific knowledge, expert curation, consistent structure, and acceptance by peers. Addressing data privacy issues involves adhering to open-access principles and enforcing strict data encryption and anonymization standards. To address these gaps, a conceptual framework is introduced. This framework extends beyond typical user-oriented platforms and comprises five core modules. It features a neurosymbolic pipeline integrating large language models with physically based agricultural modeling software, further enhanced by Reinforcement Learning from Human Feedback. Notable aspects of the framework include a dedicated human-in-the-loop process and a governance structure consisting of three primary bodies focused on data standardization, ethics and security, and accountability and transparency. Overall, this work represents a significant advancement in agricultural knowledge systems, potentially transforming how AE services deliver critical information to farmers and other stakeholders.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 426-448"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing particle application in multi-pass overlapping scenarios with variable rate centrifugal fertilizer spreaders for precision agriculture","authors":"Shi Yinyan,&nbsp;Zhu Yangxu,&nbsp;Wang Xiaochan,&nbsp;Zhang Xiaolei,&nbsp;Zheng Enlai,&nbsp;Zhang Yongnian","doi":"10.1016/j.aiia.2025.04.003","DOIUrl":"10.1016/j.aiia.2025.04.003","url":null,"abstract":"<div><div>Environmental impacts and economic demands are driving the development of variable rate fertilization (VRF) technology for precision agriculture. Despite the advantages of a simple structure, low cost and high efficiency, uneven fertilizer-spreading uniformity is becoming a key factor restricting the application of centrifugal fertilizer spreaders. Accordingly, the particle application characteristics and variation laws for centrifugal VRF spreaders with multi-pass overlapped spreading needs to be urgently explored, in order to improve their distribution uniformity and working accuracy. In this study, the working performance of a self-developed centrifugal VRF spreader, based on real-time growth information of rice and wheat, was investigated and tested through the test methods of using the collection trays prescribed in ISO 5690 and ASAE S341.2. The coefficient of variation (CV) was calculated by weighing the fertilizer mass in standard pans, in order to evaluate the distribution uniformity of spreading patterns. The results showed that the effective application widths were 21.05, 22.58 and 23.67 m for application rates of 225, 300 and 375 kg/ha, respectively. The actual fertilizer application rates of multi-pass overlapped spreading were generally higher than the target rates, as well as the particle distribution CVs within the effective spreading widths were 11.51, 9.25 and 11.28 % for the respective target rates. Field test results for multi-pass overlapped spreading showed that the average difference between the actual and target application was 4.54 %, as well as the average particle distribution CV within the operating width was 11.94 %, which met the operation requirements of particle transverse distribution for centrifugal fertilizer spreaders. The results and findings of this study provide a theoretical reference for technical innovation and development of centrifugal VRF spreaders and are of great practical and social significance for accelerating their application in implementing precision agriculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 395-406"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques","authors":"Zhen Chen ,&nbsp;Weiguang Zhai ,&nbsp;Qian Cheng","doi":"10.1016/j.aiia.2025.04.008","DOIUrl":"10.1016/j.aiia.2025.04.008","url":null,"abstract":"<div><div>The leaf area index (LAI) is crucial for precision agriculture management. UAV remote sensing technology has been widely applied for LAI estimation. Although spectral features are widely used for LAI estimation, their performance is often constrained in complex agricultural scenarios due to interference from soil background reflectance, variations in lighting conditions, and vegetation heterogeneity. Therefore, this study evaluates the potential of multi-source feature fusion and convolutional neural networks (CNN) in estimating maize LAI. To achieve this goal, field experiments on maize were conducted in Xinxiang City and Xuzhou City, China. Subsequently, spectral features, texture features, and crop height were extracted from the multi-spectral remote sensing data to construct a multi-source feature dataset. Then, maize LAI estimation models were developed using multiple linear regression, gradient boosting decision tree, and CNN. The results showed that: (1) Multi-source feature fusion, which integrates spectral features, texture features, and crop height, demonstrated the highest accuracy in LAI estimation, with the R² ranging from 0.70 to 0.83, the RMSE ranging from 0.44 to 0.60, and the rRMSE ranging from 10.79 % to 14.57 %. In addition, the multi-source feature fusion demonstrates strong adaptability across different growth environments. In Xinxiang, the R² ranges from 0.76 to 0.88, the RMSE ranges from 0.35 to 0.50, and the rRMSE ranges from 8.73 % to 12.40 %. In Xuzhou, the R² ranges from 0.60 to 0.83, the RMSE ranges from 0.46 to 0.71, and the rRMSE ranges from 10.96 % to 17.11 %. (2) The CNN model outperformed traditional machine learning algorithms in most cases. Moreover, the combination of spectral features, texture features, and crop height using the CNN model achieved the highest accuracy in LAI estimation, with the R² ranging from 0.83 to 0.88, the RMSE ranging from 0.35 to 0.46, and the rRMSE ranging from 8.73 % to 10.96 %.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 482-495"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality","authors":"Tingting Zhang ,&nbsp;Jing Li ,&nbsp;Jinpeng Tong ,&nbsp;Yihu Song ,&nbsp;Li Wang ,&nbsp;Renye Wu ,&nbsp;Xuan Wei ,&nbsp;Yuanyuan Song ,&nbsp;Rensen Zeng","doi":"10.1016/j.aiia.2025.02.003","DOIUrl":"10.1016/j.aiia.2025.02.003","url":null,"abstract":"<div><div>Seeds are essential to the agri-food industry. However, their quality is vulnerable to biotic and abiotic stresses during production and storage, leading to various types of deterioration. Real-time monitoring and pre-sowing screening offer substantial potential for improved storage management, field performance, and flour quality. This study investigated diverse deterioration patterns in wheat seeds by analyzing 1000 high-quality and 1098 deteriorated seeds encompassing mold, aging, mechanical damage, insect damage, and internal insect infestation. Hyperspectral imaging (HSI) and computer vision (CV) were employed to capture surface data from both the embryo (EM) and endosperm (EN). Internal seed quality was further assessed using scanning electron microscopy, dissection, and standard germination tests. Both conventional machine learning algorithms and deep convolutional neural networks (DCNN) were employed to develop discriminative models using independent datasets. Results revealed that each data source contributed valuable information for seed quality assessment (validation set accuracy: 65.1–89.2 %), with the integration of HSI and CV showing considerable promise. A comparison of early and late fusion strategies led to the development of an end-to-end deep fusion model. The decision fusion-based DCNN model, integrating HSI-EM, HSI-EN, CV-EM, and CV-EN data, achieved the highest accuracy in both training (94.3 %) and validation (93.8 %) sets. Applying this model to seed lot screening increased the proportion of high-quality seeds from 47.7 % to 93.4 %. These findings were further supported by external samples and visualizations. The proposed end-to-end decision fusion DCNN model simplifies the training process compared to traditional two-stage fusion methods. This study presents a potentially efficient alternative for rapid, individual kernel quality detection and control during wheat production.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 537-549"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping of soil sampling sites using terrain and hydrological attributes","authors":"Tan-Hanh Pham ,&nbsp;Kristopher Osterloh ,&nbsp;Kim-Doang Nguyen","doi":"10.1016/j.aiia.2025.04.007","DOIUrl":"10.1016/j.aiia.2025.04.007","url":null,"abstract":"<div><div>Efficient soil sampling is essential for effective soil management and research on soil health. Traditional site selection methods are labor-intensive and fail to capture soil variability comprehensively. This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. The proposed framework consists of two key components: an extractor and a predictor. The extractor, based on a convolutional neural network (CNN), derives features from spectral images, while the predictor employs self-attention mechanisms to assess feature importance and generate prediction maps. The model is designed to process multiple spectral images and address the class imbalance in soil segmentation.</div><div>The model was trained on a soil dataset from 20 fields in eastern South Dakota, collected via drone-mounted LiDAR with high-precision GPS. Evaluation on a test set achieved a mean intersection over union (mIoU) of 69.46 % and a mean Dice coefficient (mDc) of 80.35 %, demonstrating strong segmentation performance. The results highlight the model's effectiveness in automating soil sampling site selection, providing an advanced tool for producers and soil scientists. Compared to existing state-of-the-art methods, the proposed approach improves accuracy and efficiency, optimizing soil sampling processes and enhancing soil research.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 470-481"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven aquaculture: A review of technological innovations and their sustainable impacts","authors":"Hang Yang ,&nbsp;Qi Feng ,&nbsp;Shibin Xia ,&nbsp;Zhenbin Wu ,&nbsp;Yi Zhang","doi":"10.1016/j.aiia.2025.01.012","DOIUrl":"10.1016/j.aiia.2025.01.012","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector, underscoring the significant enhancements in production efficiency and environmental sustainability. Key AI-driven improvements, such as predictive analytics for disease management and optimized feeding protocols, are highlighted, demonstrating their contributions to reducing waste and improving biomass outputs. However, challenges remain in terms of data quality, system integration, and the socio-economic impacts of technological adoption across diverse aquacultural environments. This review also addresses the gaps in current research, particularly the lack of robust, scalable AI models and frameworks that can be universally applied. Future directions are discussed, emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture. This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 508-525"},"PeriodicalIF":8.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing computation resource exhaustion associated with deep learning training of three-dimensional hyperspectral images using multiclass weed classification","authors":"Billy G. Ram ,&nbsp;Kirk Howatt ,&nbsp;Joseph Mettler ,&nbsp;Xin Sun","doi":"10.1016/j.aiia.2025.02.005","DOIUrl":"10.1016/j.aiia.2025.02.005","url":null,"abstract":"<div><div>Addressing the computational bottleneck of training deep learning models on high-resolution, three-dimensional images, this study introduces an optimized approach, combining distributed learning (parallelism), image resolution, and data augmentation. We propose analysis methodologies that help train deep learning (DL) models on proximal hyperspectral images, demonstrating superior performance in eight-class crop (canola, field pea, sugarbeet and flax) and weed (redroot pigweed, resistant kochia, waterhemp and ragweed) classification. Utilizing state-of-the-art model architectures (ResNet-50, VGG-16, DenseNet, EfficientNet) in comparison with ResNet-50 inspired Hyper-Residual Convolutional Neural Network model. Our findings reveal that an image resolution of 100x100x54 maximizes accuracy while maintaining computational efficiency, surpassing the performance of 150x150x54 and 50x50x54 resolution images. By employing data parallelism, we overcome system memory limitations and achieve exceptional classification results, with test accuracies and F1-scores reaching 0.96 and 0.97, respectively. This research highlights the potential of residual-based networks for analyzing hyperspectral images. It offers valuable insights into optimizing deep learning models in resource-constrained environments. The research presents detailed training pipelines for deep learning models that utilize large (&gt; 4k) hyperspectral training samples, including background and without any data preprocessing. This approach enables the training of deep learning models directly on raw hyperspectral data.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 131-146"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitalizing greenhouse trials: An automated approach for efficient and objective assessment of plant damage using deep learning","authors":"Laura Gómez-Zamanillo ,&nbsp;Arantza Bereciartúa-Pérez ,&nbsp;Artzai Picón ,&nbsp;Liliana Parra ,&nbsp;Marian Oldenbuerger ,&nbsp;Ramón Navarra-Mestre ,&nbsp;Christian Klukas ,&nbsp;Till Eggers ,&nbsp;Jone Echazarra","doi":"10.1016/j.aiia.2025.03.001","DOIUrl":"10.1016/j.aiia.2025.03.001","url":null,"abstract":"<div><div>The use of image based and, recently, deep learning-based systems have provided good results in several applications. Greenhouse trials are key part in the process of developing and testing new herbicides and analyze the response of the species to different products and doses in a controlled way. The assessment of the damage in the plant is daily done in all trials by visual evaluation by experts. This entails time consuming process and lack of repeatability. Greenhouse trials require new digital tools to reduce time consuming process and to endow the experts with more objective and repetitive methods for establishing the damage in the plants.</div><div>To this end, a novel method is proposed composed by an initial segmentation of the plant species followed by a multibranch convolutional neural network to estimate the damage level. In this way, we overcome the need for costly and unaffordable pixelwise manual segmentation for damage symptoms and we make use of global damage estimation values provided by the experts.</div><div>The algorithm has been deployed under real greenhouse trials conditions in a pilot study located in BASF in Germany and tested over four species (GLXMA, TRZAW, ECHCG, AMARE). The results show mean average error (MAE) values ranging from 5.20 for AMARE and 8.07 for ECHCG for the estimation of PDCU value, with correlation values (R²) higher than 0.85 in all situations, and up to 0.92 in AMARE. These results surpass the inter-rater variability of human experts demonstrating that the proposed automated method is appropriate for automatically assessing greenhouse damage trials.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 280-295"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}