Artificial Intelligence in Agriculture最新文献

{"title":"High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges","authors":"Tao Cheng ,&nbsp;Dongyan Zhang ,&nbsp;Gan Zhang ,&nbsp;Tianyi Wang ,&nbsp;Weibo Ren ,&nbsp;Feng Yuan ,&nbsp;Yaling Liu ,&nbsp;Zhaoming Wang ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.aiia.2025.01.003","DOIUrl":"10.1016/j.aiia.2025.01.003","url":null,"abstract":"<div><div>High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years. This paper reviews the unique aspects and research priorities in forage phenotypic monitoring, highlights key remote sensing platforms, examines the applications of advanced sensing technology for quantifying phenotypic traits, explores artificial intelligence (AI) algorithms in phenotypic data integration and analysis, and assesses recent progress in phenotypic genomics. The practical applications of HTP technology in forage remain constrained by several challenges. These include establishing uniform data collection standards, designing effective algorithms to handle complex genetic and environmental interactions, deepening the cross-exploration of phenomics-genomics, solving the problem of pathological inversion of forage phenotypic growth monitoring models, and developing low-cost forage phenotypic equipment. Resolving these challenges will unlock the full potential of HTP, enabling precise identification of superior forage traits, accelerating the breeding of superior varieties, and ultimately improving forage yield.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 98-115"},"PeriodicalIF":8.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097558","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":"Crop-conditional semantic segmentation for efficient agricultural disease assessment","authors":"Artzai Picon ,&nbsp;Itziar Eguskiza ,&nbsp;Pablo Galan ,&nbsp;Laura Gomez-Zamanillo ,&nbsp;Javier Romero ,&nbsp;Christian Klukas ,&nbsp;Arantza Bereciartua-Perez ,&nbsp;Mike Scharner ,&nbsp;Ramon Navarra-Mestre","doi":"10.1016/j.aiia.2025.01.002","DOIUrl":"10.1016/j.aiia.2025.01.002","url":null,"abstract":"<div><div>In this study, we introduced an innovative crop-conditional semantic segmentation architecture that seamlessly incorporates contextual metadata (crop information). This is achieved by merging the contextual information at a late layer stage, allowing the method to be integrated with any semantic segmentation architecture, including novel ones. To evaluate the effectiveness of this approach, we curated a challenging dataset of over 100,000 images captured in real-field conditions using mobile phones. This dataset includes various disease stages across 21 diseases and seven crops (wheat, barley, corn, rice, rape-seed, vinegrape, and cucumber), with the added complexity of multiple diseases coexisting in a single image. We demonstrate that incorporating contextual multi-crop information significantly enhances the performance of semantic segmentation models for plant disease detection. By leveraging crop-specific metadata, our approach achieves higher accuracy and better generalization across diverse crops (F1 = 0.68, <em>r</em> = 0.75) compared to traditional methods (F1 = 0.24, <em>r</em> = 0.68). Additionally, the adoption of a semi-supervised approach based on pseudo-labeling of single diseased plants, offers significant advantages for plant disease segmentation and quantification (F1 = 0.73, <em>r</em> = 0.95). This method enhances the model's performance by leveraging both labeled and unlabeled data, reducing the dependency on extensive manual annotations, which are often time-consuming and costly.</div><div>The deployment of this algorithm holds the potential to revolutionize the digitization of crop protection product testing, ensuring heightened repeatability while minimizing human subjectivity. By addressing the challenges of semantic segmentation and disease quantification, we contribute to more effective and precise phenotyping, ultimately supporting better crop management and protection strategies.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 79-87"},"PeriodicalIF":8.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097561","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":"Knowledge-guided temperature correction method for soluble solids content detection of watermelon based on Vis/NIR spectroscopy","authors":"Zhizhong Sun ,&nbsp;Jie Yang ,&nbsp;Yang Yao ,&nbsp;Dong Hu ,&nbsp;Yibin Ying ,&nbsp;Junxian Guo ,&nbsp;Lijuan Xie","doi":"10.1016/j.aiia.2025.01.004","DOIUrl":"10.1016/j.aiia.2025.01.004","url":null,"abstract":"<div><div>Visible/near-infrared (Vis/NIR) spectroscopy technology has been extensively utilized for the determination of soluble solids content (SSC) in fruits. Nonetheless, the spectral distortion resulting from temperature variations in the sample leads to a decrease in detection accuracy. To mitigate the influence of temperature fluctuations on the accuracy of SSC detection in fruits, using watermelon as an example, this study presents a knowledge-guided temperature correction method utilizing one-dimensional convolutional neural networks (1D-CNN). This method consists of two stages: the first stage involves utilizing 1D-CNN models and gradient-weighted class activation mapping (Grad-CAM) method to acquire gradient-weighted features correlating with temperature. The second stage involves mapping these features and integrating them with the original Vis/NIR spectrum, and then train and test the partial least squares (PLS) model. This knowledge-guided method can identify wavelength bands with high temperature correlation in the Vis/NIR spectra, offering valuable guidance for spectral data processing. The performance of the PLS model constructed using the 15 °C spectrum guided by this method is superior to that of the global model, and can reduce the root mean square error of the prediction set (RMSEP) to 0.324°Brix, which is 32.5 % lower than the RMSEP of the global model (0.480°Brix). The method proposed in this study has superior temperature correction effects than slope and bias correction, piecewise direct standardization, and external parameter orthogonalization correction methods. The results indicate that the knowledge-guided temperature correction method based on deep learning can significantly enhance the detection accuracy of SSC in watermelon, providing valuable reference for the development of PLS calibration methods.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 88-97"},"PeriodicalIF":8.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097560","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":"Enhancing citrus surface defects detection: A priori feature guided semantic segmentation model","authors":"Xufeng Xu ,&nbsp;Tao Xu ,&nbsp;Zichao Wei ,&nbsp;Zetong Li ,&nbsp;Yafei Wang ,&nbsp;Xiuqin Rao","doi":"10.1016/j.aiia.2025.01.005","DOIUrl":"10.1016/j.aiia.2025.01.005","url":null,"abstract":"<div><div>The accurate detection of citrus surface defects is of great importance for elevating the product quality and augmenting its market value. However, due to defect diversity and complexity, existing methods focused on parameter and data enhancement have limitations in detection and segmentation. Therefore, this study proposed a citrus surface defect segmentation model guided by prior features, named PrioriFormer. The model extracted texture features, boundary features, and superpixel features that were crucial for defect detection and segmentation, as priori features. A Priori Feature Fusion Module (PFFM) was designed to integrate the priori features, thereby establishing a priori feature branch. Then the priori feature branch was integrated into the baseline model SegFormer, with the objective of enhancing key feature learning capacity of the model. Finally, the effectiveness of the priori features in enhancing the performance of the model was demonstrated through the implementation of specific experiments. The result showed that PrioriFormer achieved an mPA (mean Pixel Accuracy), mIoU (mean Intersection over Union), and Dice Coefficient of 91.0 %, 85.8 %, and 91.0 %, respectively. Compared to other semantic segmentation models, the proposed model has achieved the best performance. The model parameters of PrioriFormer have only increase by 2.7 % in comparison to the baseline model, while the mIoU has improved by 3.3 %, indicating that the improvement of segmentation performance had less dependence on model parameters. Even when trained on few data, PrioriFormer maintained the high segmentation performance, with the reduction of mIoU not exceeding 4.2 %. This demonstrated the strong feature learning ability of the model in scenarios with limited data. Furthermore, validation on external datasets confirmed PrioriFormer's superior performance and adaptability to different tasks. The study found that the proposed PrioriFomer guided by priori features can effectively enhance the accuracy of the citrus surface defect segmentation model, providing technical reference for citrus sorting and quality assessment.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 67-78"},"PeriodicalIF":8.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097562","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":"PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets","authors":"Xue Xia ,&nbsp;Ning Zhang ,&nbsp;Zhibin Guan ,&nbsp;Xin Chai ,&nbsp;Shixin Ma ,&nbsp;Xiujuan Chai ,&nbsp;Tan Sun","doi":"10.1016/j.aiia.2025.01.001","DOIUrl":"10.1016/j.aiia.2025.01.001","url":null,"abstract":"<div><div>Aggressive behavior among piglets is considered a harmful social contact. Monitoring weaned piglets with intense aggressive behaviors is paramount for pig breeding management. This study introduced a novel hybrid model, PAB-Mamba-YOLO, integrating the principles of Mamba and YOLO for efficient visual detection of weaned piglets' aggressive behaviors, including climbing body, nose hitting, biting tail and biting ear. Within the proposed model, a novel CSPVSS module, which integrated the Cross Stage Partial (CSP) structure with the Visual State Space Model (VSSM), has been developed. This module was adeptly integrated into the Neck part of the network, where it harnessed convolutional capabilities for local feature extraction and leveraged the visual state space to reveal long-distance dependencies. The model exhibited sound performance in detecting aggressive behaviors, with an average precision (AP) of 0.976 for climbing body, 0.994 for nose hitting, 0.977 for biting tail and 0.994 for biting ear. The mean average precision (mAP) of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive behaviors. The model achieved a detection speed FPS of 69 f/s, with model complexity measured by 7.2 G floating-point operations (GFLOPs) and parameters (Params) of 2.63 million. Comparative experiments with existing prevailing models confirmed the superiority of the proposed model. This work is expected to contribute a glimmer of fresh ideas and inspiration to the research field of precision breeding and behavioral analysis of animals.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 52-66"},"PeriodicalIF":8.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097559","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":"A bibliometric analysis of research on remote sensing-based monitoring of soil organic matter conducted between 2003 and 2023","authors":"Xionghai Chen ,&nbsp;Fei Yuan ,&nbsp;Syed Tahir Ata-Ul-Karim ,&nbsp;Xiaojun Liu ,&nbsp;Yongchao Tian ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Qiang Cao","doi":"10.1016/j.aiia.2024.12.004","DOIUrl":"10.1016/j.aiia.2024.12.004","url":null,"abstract":"<div><div>Soil organic matter (SOM) is a key metric for assessing soil quality and crop yield potential. It plays a vital role in maintaining the ecological balance environment and promoting sustainable farming practices. This review examines the evolving trends in remote sensing (<em>RS</em>)-based SOM monitoring by analyzing 739 scholarly publications from the Web of Science database from 2003 to 2023 using a bibliometric approach. The study reveals that research on RS-based SOM monitoring has entered a rapid growth phase since 2018, with China and the United States as the main contributors and an extensive international cooperation network. In model construction, high frequency covariates such as soil pH, precipitation, temperature, and topography significantly improved the prediction accuracy. Data preprocessing methods such as Standard Normal Variables (SNV), Principal Component Analysis (PCA), and Multiple Scattering Correction (MSC) enhanced data consistency. Traditional statistical models are gradually being replaced by nonlinear machine learning and deep learning methods (CNN, XGBoost, andStacking), which are particularly good at handling complex high-dimensional data. Regional spectral libraries (OzSoil and AfSIS) excel in local accuracy, while global spectral libraries (ISRIC and LUCAS) are more suitable for cross-region modeling, and the migration learning technique effectively improves the model generalization ability in low data regions. Integrated models (CNN-LSTM and GAN) have significant advantages in capturing the spatial and temporal dynamics of SOMs, and uncertainty quantification methods (Bayesian inference, Monte Carlo simulation) enhance the reliability of the models in multi-source data and data-scarce scenarios. Future research should focus on further optimization of multi-source data fusion and uncertainty quantification to promote the development and applicability of RS-based SOM monitoring techniques for precision soil management and sustainable agriculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 26-38"},"PeriodicalIF":8.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149330","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":"Normalized difference vegetation index prediction using reservoir computing and pretrained language models","authors":"John Olamofe ,&nbsp;Ram Ray ,&nbsp;Xishuang Dong ,&nbsp;Lijun Qian","doi":"10.1016/j.aiia.2024.12.005","DOIUrl":"10.1016/j.aiia.2024.12.005","url":null,"abstract":"<div><div>In this study, we examined plant health prediction through the Normalized Difference Vegetation Index (NDVI) calculated from satellite image derived reflectance values in the near-infrared and red spectra. The problem is formulated as a temporal data prediction problem. Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset, we designed and implemented Reservoir Computing (RC) models and transformer-based models including pretrained language model, and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression, Decision Tree, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and DLinear. It is observed that the DLinear/LSTM model showed exceptional predictive accuracy, while the pretrained RC model significantly enhanced traditional RC model forecasts. Additionally, Frozen Pretrained Transformer (FPT), a pretrained language model, showed superior performance in predicting specific NDVI values (most often peak or lowest NDVI), suggesting its effectiveness in precise temporal predictions. Furthermore, transformer-based models, specifically PatchTST and FPT, demonstrated substantial mean squared error reductions, particularly in limited data scenarios (1 %, 5 %, 15 % and 50 % sample sizes), indicating their robustness in precise NDVI temporal predictions when data is limited. The findings in this study demonstrated the effectiveness of emerging machine learning techniques such as reservoir computing and pretrained language model for remote sensing and their contributions in precision agriculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 116-129"},"PeriodicalIF":8.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097557","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":"Development of automatic wheat seeding quantity control system based on Doppler radar speed measurement","authors":"Weiwei Wang ,&nbsp;Wenbing Shi ,&nbsp;Ce Liu ,&nbsp;Yuwei Wang ,&nbsp;Lu Liu ,&nbsp;Liqing Chen","doi":"10.1016/j.aiia.2024.12.001","DOIUrl":"10.1016/j.aiia.2024.12.001","url":null,"abstract":"<div><div>With advancements in agricultural technology, the full mechanization of rice straw wheat planting has been achieved. However, issues such as missed seeding, uneven row spacing, and poor uniformity of row replenishment often arise due to wheel slippage in wheeled wheat seeders. These problems manual replanting after emergence, reducing efficiency and increasing labor costs. To address these challenges, a speed-adaptive wheat seeding control system based on speed radar was developed. This system comprises a pneumatic wheat seeding device, an automatic speed-following control system, a human-machine interface, and a stepper motor. Leveraging an embedded controller, the system dynamically adjusts motor speed based on real-time forward speed to ensure precise seeding. Using fuzzy PID control, the system dynamically adjusts motor speed, achieving row spacing consistency below 3.9 % and seeding stability within 1.3 %, even at varying speeds. This system addresses critical challenges in precision agriculture, enhancing planting efficiency and reducing labor costs. This innovation enhances planting efficiency, reduces labor costs, and ensures adaptability to varying tractor speeds, meeting the precision requirements of wheat planting.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 12-25"},"PeriodicalIF":8.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097979","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":"Robotization of banana de-handing under multi-constraint scenarios: Challenges and future directions","authors":"Jie Guo ,&nbsp;Zhou Yang ,&nbsp;Manoj Karkee ,&nbsp;Jieli Duan ,&nbsp;Yong He","doi":"10.1016/j.aiia.2024.12.002","DOIUrl":"10.1016/j.aiia.2024.12.002","url":null,"abstract":"<div><div>Banana de-handing is an important part of banana post-harvesting operation. The traditional artificial de-handing model has problems such as labor intensity, inaccurate cutting, uneven cutting surface, unstable catching, and damage of banana fruit, etc. The mapping relationship between the characteristic parameters of the movement posture of the cutter and the influencing factors of the contact stress of banana crown cutting in unstructured environments, and the changing rules of the bumping contact stress of complex multi-shaped banana fruit with the physical property parameters of the cushioning material are the theoretical and technical difficulties that urgently need to be solved in the realization of banana mechanical de-handing. The future research (research on the coupling mechanism of visual cognition-mechanism cutting and low-destructive catching method of full-field continuous de-handing of bananas under multi-constraint scenarios) should: (1) create a database of banana crown, obtain the optimal banana crown recognition model, develop a recognition and locating system of the cutting line of banana crown and obtain its spatial location information; (2) establish the discrete element mechanical model of banana crown and the interaction model between banana crown and the cutter, clarify the stress change and the force wave transmission characteristics of the cutting process, construct the multi-objective optimization equation of the cutting performance, obtain the best combination of cutting parameters, and ascertain the mechanisms of synergistic locating and continuous cutting of banana crown; (3) establish the contact mechanical model of banana fruit drop-bump, parse the bumping characteristics between banana fruit and cushioning material, construct mathematical equations to quantitatively assess damage results, and determine the detract catching method of banana fruit that matches the de-handing mode in multi-constraint scenarios. This study showed that the real-time identification and spatial positioning of fruit, the mechanical properties of crown and the optimization of cutting performance, the damage mechanism of fruit and its loss-reducing harvesting method are the three key breakthroughs in realizing the robotization of de-handing. The current bottleneck problems and future research directions of intelligent banana de-handing were pointed out in this paper, which can provide a theoretical basis for the optimal design of banana de-handing devices and provide technical support for promoting the practical application of intelligent de-handing equipment.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 1","pages":"Pages 1-11"},"PeriodicalIF":8.2,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097983","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":"A salient feature establishment tactic for cassava disease recognition","authors":"Jiayu Zhang ,&nbsp;Baohua Zhang ,&nbsp;Zixuan Chen ,&nbsp;Innocent Nyalala ,&nbsp;Kunjie Chen ,&nbsp;Junfeng Gao","doi":"10.1016/j.aiia.2024.11.004","DOIUrl":"10.1016/j.aiia.2024.11.004","url":null,"abstract":"<div><div>Accurate classification of cassava disease, particularly in field scenarios, relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning, thereby enabling targeted classification while suppressing irrelevant noise and focusing on key semantic features. The advancement of deep convolutional neural networks (CNNs) paved the way for identifying cassava diseases by leveraging salient semantic features and promising high returns. This study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease classification. First, a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature maps. Second, instance batch normalization (IBN) was employed after the residual unit to construct salient semantic features using the mutualattention method, representing high-quality semantic features in the foreground. Finally, the RSigELUD activation method replaced the conventional ReLU activation, enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance. This approach significantly aided in distinguishing subtle disease manifestations in cassava leaves. The proposed neural network, MAIRNet-101 (Mutualattention IBN RSigELUD Neural Network), achieved an accuracy of 95.30 % and an F1-score of 0.9531, outperforming EfficientNet-B5 and RepVGG-B3g4. To evaluate the generalization capability of MAIRNet, the FGVC-Aircraft dataset was used to train MAIRNet-50, which achieved an accuracy of 83.64 %. These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 115-132"},"PeriodicalIF":8.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153819","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}