Artificial Intelligence in Agriculture最新文献

{"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}

{"title":"Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data","authors":"Mohammad Amin Razavi ,&nbsp;A. Pouyan Nejadhashemi ,&nbsp;Babak Majidi ,&nbsp;Hoda S. Razavi ,&nbsp;Josué Kpodo ,&nbsp;Rasu Eeswaran ,&nbsp;Ignacio Ciampitti ,&nbsp;P.V. Vara Prasad","doi":"10.1016/j.aiia.2024.11.005","DOIUrl":"10.1016/j.aiia.2024.11.005","url":null,"abstract":"<div><div>In this study, we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal. We analyze how these features influence crop yields by utilizing remotely sensed data. Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies, offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal. To optimize the model's performance and identify the optimal hyperparameters, we implemented a comprehensive grid search across four distinct machine learning regressors: Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient-Boosting Machine (LightGBM). Each regressor offers unique functionalities, enhancing our exploration of potential model configurations. The top-performing models were selected based on evaluating multiple performance metrics, ensuring robust and accurate predictive capabilities. The results demonstrated that XGBoost and CatBoost perform better than the other two. We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets. By achieving high similarity scores with real-world data, our synthetic samples enhance model robustness, mitigate overfitting, and provide a viable solution for small dataset issues in agriculture. Our approach distinguishes itself by creating a flexible model applicable to various crops together. By integrating five crop datasets and generating high-quality synthetic data, we improve model performance, reduce overfitting, and enhance realism. Our findings provide crucial insights for productivity drivers in key cropping systems, enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in data-scarce regions.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 99-114"},"PeriodicalIF":8.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742953","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":"Neural network architecture search enabled wide-deep learning (NAS-WD) for spatially heterogenous property awared chicken woody breast classification and hardness regression","authors":"Chaitanya Pallerla ,&nbsp;Yihong Feng ,&nbsp;Casey M. Owens ,&nbsp;Ramesh Bahadur Bist ,&nbsp;Siavash Mahmoudi ,&nbsp;Pouya Sohrabipour ,&nbsp;Amirreza Davar ,&nbsp;Dongyi Wang","doi":"10.1016/j.aiia.2024.11.003","DOIUrl":"10.1016/j.aiia.2024.11.003","url":null,"abstract":"<div><div>Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In this study, 250 raw chicken breast fillet samples (normal, mild, severe) were taken, and spatially heterogeneous hardness distribution was first considered when designing HSI processing models. The study not only classified the WB levels from HSI but also built a regression model to correlate the spectral information with sample hardness data. To achieve a satisfactory classification and regression model, a neural network architecture search (NAS) enabled a wide-deep neural network model named NAS-WD, which was developed. In NAS-WD, NAS was first used to automatically optimize the network architecture and hyperparameters. The classification results show that NAS-WD can classify the three WB levels with an overall accuracy of 95 %, outperforming the traditional machine learning model, and the regression correlation between the spectral data and hardness was 0.75, which performs significantly better than traditional regression models.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 73-85"},"PeriodicalIF":8.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699967","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":"Utility-based regression and meta-learning techniques for modeling actual ET: Comparison to (METRIC-EEFLUX) model","authors":"Fatima K. Abu Salem ,&nbsp;Sara Awad ,&nbsp;Yasmine Hamdar ,&nbsp;Samer Kharroubi ,&nbsp;Hadi Jaafar","doi":"10.1016/j.aiia.2024.11.001","DOIUrl":"10.1016/j.aiia.2024.11.001","url":null,"abstract":"<div><div>Estimating actual evapotranspiration (ETₐ) is crucial for water resource management, yet existing methods face limitations. Traditional approaches, including eddy covariance and remote sensing-based energy balance methods, often struggle with high costs, limited spatial and temporal coverage, and reduced predictive accuracy, particularly for classical empirical models. While machine learning has emerged as a promising alternative, it still presents challenges, notably in underestimating ETₐ during periods of high heat. We attribute this to insufficient learning on the rare but highly relevant ETₐ values of interest, or the not-so-big climatic datasets available for use. In this manuscript, we demonstrate how <em>few-shot, meta-learning models (MAML)</em> that are specifically designed for enhanced generalizability on not-so-big datasets can outperform basic machine learning models in upscaling ETₐ from two major in-situ towers, the Ameriflux and Euroflux. Using limited remotely sensed land surface data from the METRIC-EEFlux and limited climatic variables, we demonstrate that the chosen models can attain quantifiable utility within the <em>utility-based-regression</em> paradigm towards impactful practical considerations. Our initial explorations reveal that EEflux ETₐ deviates significantly from in-situ observations measured through the Ameriflux and EEflux towers (<span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>39</mn><mo>%</mo></math></span>). Instead, MAML shows best performance in approximating ETₐ than basic machine learning algorithms and EEFlux (<span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>71</mn><mo>%</mo></math></span> on entire testing dataset, <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.88</mn></math></span> on the Csa climate, <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.79</mn></math></span> on the Cfa climate, and <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.78</mn></math></span> on the CSH vegetation class), and continues to improve without overfitting even when exposed to a relatively small training dataset. Its high F2 score (96 %) indicates that MAML has very high precision and recall for rare cases, which is significant for irrigation. Of independent interest, this study confirms that limited remotely sensed EEflux products contribute significantly to knowledge about ground truth ETₐ and can thus be of valuable use in settings where access to good quality and high-volume data is compromised.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 43-55"},"PeriodicalIF":8.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699966","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":"Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull","authors":"Miklós Biszkup ,&nbsp;Gábor Vásárhelyi ,&nbsp;Nuri Nurlaila Setiawan ,&nbsp;Aliz Márton ,&nbsp;Szilárd Szentes ,&nbsp;Petra Balogh ,&nbsp;Barbara Babay-Török ,&nbsp;Gábor Pajor ,&nbsp;Dóra Drexler","doi":"10.1016/j.aiia.2024.11.002","DOIUrl":"10.1016/j.aiia.2024.11.002","url":null,"abstract":"<div><div>The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 86-98"},"PeriodicalIF":8.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699968","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":"Estimating TYLCV resistance level using RGBD sensors in production greenhouse conditions","authors":"Dorin Shmaryahu ,&nbsp;Rotem Lev Lehman ,&nbsp;Ezri Peleg ,&nbsp;Guy Shani","doi":"10.1016/j.aiia.2024.10.004","DOIUrl":"10.1016/j.aiia.2024.10.004","url":null,"abstract":"<div><div>Automated phenotyping is the task of automatically measuring plant attributes to help farmers and breeders in developing and growing strong robust plants. An automated tool for early illness detection can accelerate the process of identifying plant resistance and quickly pinpoint problematic breeding. Many such phenotyping tasks can be achieved by analyzing images from simple, low cost, RGB-D sensors. In this paper we focused on a particular case study — identifying the resistance level of tomato hybrids to the tomato yellow leaf curl virus (TYLCV) in production greenhouses. This is a difficult task, as separating between resistance levels based on images is difficult even for expert breeders. We collected a large dataset of images from an experiment containing many tomato hybrids with varying resistance levels. We used the depth information to identify the topmost part of the tomato plant. We then used deep learning models to classify the various resistance levels. For identifying plants with visual symptoms, our methods achieved an accuracy of 0.928, a precision of 0.934, and a recall of 0.95. In the multi-class case we achieved an accuracy of 0.76 in identifying the correct level, and an error of 0.278. Our methods are not particularly tailored for the specific task, and can be extended to other tasks that identify various plant diseases with visual symptoms such as ToBRFV, mildew, ToMV and others.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"14 ","pages":"Pages 31-42"},"PeriodicalIF":8.2,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699964","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}