Computers and Electronics in Agriculture最新文献

{"title":"Construction and validation of a mathematical model for the pressure subsidence of mixed crop straw in Shajiang black soil","authors":"Dongbo Xie ,&nbsp;Zhiqiang Li ,&nbsp;Ce Liu ,&nbsp;Gang Zhao ,&nbsp;Liqing Chen","doi":"10.1016/j.compag.2024.109649","DOIUrl":"10.1016/j.compag.2024.109649","url":null,"abstract":"<div><div>The soil properties of mixed crop straw do not enable conventional pressure subsidence models to characterize the relationship between straw amount and pressure-bearing properties accurately. Based on the distribution of straw in the field, this study explored the effect of the amount of surface straw cover on the pressure subsidence relationship in Shajiang black soil. The quadratic rotated orthogonal combination test was used to quantify the mathematical relationships of Shajiang black soil pressure subsidence modeling with the amount of surface straw cover (SSC) and mass mixing ratio of soil to straw (MSS). Then, using the weighted least squares method, the pressure subsidence parameters (cohesive deformation modulus, friction deformation modulus, and subsidence index) were obtained, and the Bekker model was modified to construct a pressure subsidence model for the straw-containing soil. Finally, the modified model was verified under conditions of a water content of 18 %, the SSC of 2.5 t·ha⁻¹, and the MSS of 2.5 %. Results showed that the proposed pressure subsidence model predicted the value with a relative error of 2.21 % compared with the experimental measurements. The model’s predicted value accuracy improved by 10.65 % compared to the conventional model. From these results, this study proposes that a mixed crop straw Shajiang black soil pressure subsidence model can predict the soil’s internal stress transfer and stress–strain conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109649"},"PeriodicalIF":7.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661677","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":"Estimation of crop leaf area index based on Sentinel-2 images and PROSAIL-Transformer coupling model","authors":"Tianjiao Liu ,&nbsp;Si-Bo Duan ,&nbsp;Niantang Liu ,&nbsp;Baoan Wei ,&nbsp;Juntao Yang ,&nbsp;Jiankui Chen ,&nbsp;Li Zhang","doi":"10.1016/j.compag.2024.109663","DOIUrl":"10.1016/j.compag.2024.109663","url":null,"abstract":"<div><div>Accurate estimation of leaf area index (LAI) is hindered by challenges in capturing crop-specific spectral variability and integrating complex model-data relationships. To address these issues, this study proposes a novel framework based on Sentinel-2 images, coupling the PROSAIL physical model with a Transformer-based deep learning model. This framework incorporates three key features contributing to its effectiveness. Firstly, Sentinel-2 reflectance was generated using the PROSAIL model and refined through sample matching to ensure optimal alignment with Sentinel-2 imagery specific to each crop type. Secondly, the Maximum Information Coefficient (MIC) and Recursive Feature Elimination (RFE) were employed to identify the most relevant spectral feature combinations for different crop categories. Thirdly, a PROSAIL-Transformer coupling model was constructed based on selected feature combinations to generate accurate Sentinel-2 LAI products. To validate the proposed approach, field crop LAI measurements were collected at five plots within the study area. Quantitative assessments demonstrate a coefficient of determination (R²) of 0.87, root mean square error (RMSE) of 0.48, and mean absolute error (MAE) of 0.36. The proposed framework enables the production of time-series LAI maps at fine resolution, facilitating dynamic crop monitoring and management in areas of high spatial heterogeneity.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109663"},"PeriodicalIF":7.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661679","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":"Fish feeding behavior recognition using time-domain and frequency-domain signals fusion from six-axis inertial sensors","authors":"Pingchuan Ma ,&nbsp;Xinting Yang ,&nbsp;Weichen Hu ,&nbsp;Tingting Fu ,&nbsp;Chao Zhou","doi":"10.1016/j.compag.2024.109652","DOIUrl":"10.1016/j.compag.2024.109652","url":null,"abstract":"<div><div>In aquaculture, real-time recognition of fish feeding activities is important for enhancing feed conversion rate and reducing production costs. Therefore, this study uses a six-axis inertial sensor to collect water surface fluctuation caused by fish feeding, and proposes a time-domain and frequency-domain fusion model (TFFormer) for fish feeding behavior recognition, and identifies the feeding intensity of fish as four categories: Strong, Medium, Weak, and None. The implementation details are as follows: Firstly, the data collected by the six-axis inertial sensor is preprocessed using a sliding window to obtain time series data, and perform Fourier transform on it to obtain the frequency domain sequence. Then, the transformer is used to unify the time domain and frequency domain features respectively. A Mutual Promotion Unit (MPU) is established based on cross self-attention and a feedforward neural network (FFN). By integrating with a Global multimodal fusion (G) module, MPU establishes a global–local interactive learning framework to extract features from temporal and frequency domains, resulting in temporal-frequency interaction features. Finally, the introduction of supervised contrastive loss function supervises the training process, enhancing the accuracy of fish school feeding intensity classification. Experimental results demonstrate that the proposed TFFormer model effectively processes both temporal and frequency signals, achieving an accuracy of 91.52%, a 5.56% improvement over the baseline model and provides technical support for the development of intelligent feeding machines.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109652"},"PeriodicalIF":7.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661678","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":"Design, integration, and field evaluation of a selective harvesting robot for broccoli","authors":"Shuo Kang ,&nbsp;Sifang Long ,&nbsp;Dongfang Li ,&nbsp;Jiali Fan ,&nbsp;Dongdong Du ,&nbsp;Jun Wang","doi":"10.1016/j.compag.2024.109654","DOIUrl":"10.1016/j.compag.2024.109654","url":null,"abstract":"<div><div>The agronomic characteristics of broccoli necessitate selective harvesting in multiple batches, highlighting an urgent need for a selective harvesting robot to alleviate labour constraints. However, current research has inadequately addressed the problems of maturity identification of broccoli heads, fast and safe movement of the manipulator, and efficient and stable end-effector. Therefore, we proposed a semantic segmentation network called Broccoli Segmentation (BroSeg) for the mature identification and localisation of broccoli. BroSeg incorporated a lightweight backbone network, attention mechanisms, densely connected atrous spatial pyramid pooling, and a post-processing module. BroSeg achieved a Mean Intersection over Union (mIoU) of 58.92 % and a mean category prediction accuracy of 81.63 %. Using a collaborative simulation based on the Robot Operating System (ROS) and conducting comparative experiments, we selected the Batch Informed Trees (BIT*) algorithm that was most suitable for broccoli harvesting tasks. The effectiveness of the proposed method was validated through collaborative simulation and field experiments. Based on morphological analysis and cutting experiments, we designed an integrated gripper-cutting end-effector that mimics human hand-pinching for broccoli harvesting. The success rate of field harvesting reaches 86.96 %. This research integrates the functionalities of perception, manipulation, and cognition to construct a broccoli selective harvesting robot. Field experiments demonstrate a selective harvesting success rate of 63.16 %, with an average time of 11.9 s, validating the effectiveness and potential of the system.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109654"},"PeriodicalIF":7.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661675","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":"A Novel Behavior Detection Method for Sows and Piglets during Lactation Based on an Inspection Robot","authors":"Jie Zhou ,&nbsp;Luo Liu ,&nbsp;Tao Jiang ,&nbsp;Haonan Tian ,&nbsp;Mingxia Shen ,&nbsp;Longshen Liu","doi":"10.1016/j.compag.2024.109613","DOIUrl":"10.1016/j.compag.2024.109613","url":null,"abstract":"<div><div>Accurately identifying behaviors exhibited by lactating sows and piglets is crucial for maintaining swine health and preventing farming crises. In the absence of dedicated swine behavior monitoring systems and the challenges of implementing cloud-based automated monitoring in large-scale farming, this study proposes a method utilizing inspection robots to detect behaviors of lactating sows and piglets. The inspection robot initially serves as a data acquisition and storage tool, collecting behavioral data such as sows postures (standing, sitting, lateral recumbency, and sternal recumbency) and activities of piglet groups (resting, suckling, and active behavior) within confined pens. The YOLOv8 series algorithms are then employed to identify static postures of sows, while the Temporal Shift Module (TSM) is used to recognize dynamic behaviors within piglet groups. These models are fine-tuned and deployed on the Jetson Nano edge computing platform. Experimental results show that YOLOv8n accurately identifies sow postures with a mean Average Precision (mAP) @0.5 of 97.08% and a frame rate of 36.4 FPS at an image resolution of 480 × 288, following TensorRT acceleration. For piglet behavior recognition, the TSM model, using ResNet50 as the backbone network, achieves a Top-1 accuracy of 93.63% in recognizing piglet behaviors. Replacing ResNet50 with MobileNetv2 slightly reduces the Top-1 accuracy to 90.81%; however, there is a significant improvement in inference speed on Jetson Nano for a single video clip with a processing duration of 542.51 ms, representing more than a 20-fold enhancement compared to TSM_ResNet50. The Kappa consistency analysis reveals moderate behavioral coherence among sows in different pens and piglet groups. The study offers insights into automated detection of behaviors lactating sows and piglets within large-scale intensive farming systems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109613"},"PeriodicalIF":7.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661727","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 composite deep learning and multi-scale signal features fusion enable intelligent and precise behaviors recognition of fattening Hu sheep","authors":"Mengjie Zhang ,&nbsp;Yanfei Zhu ,&nbsp;Jiabao Wu ,&nbsp;Qinan Zhao ,&nbsp;Xiaoshuan Zhang ,&nbsp;Hailing Luo","doi":"10.1016/j.compag.2024.109635","DOIUrl":"10.1016/j.compag.2024.109635","url":null,"abstract":"<div><div>The integration of artificial intelligence and advanced sensing technologies can improve the intelligence and precision level of livestock management. This study focuses on fattening Hu sheep as the object of study, and aims to assess the effectiveness of integrating multi-scale biological signals with improved composite deep learning model in identifying and classifying behaviors of fattening Hu sheep. The multi-scale biological signals were collected using the respiratory sensor and the multi-dimensional posture sensor (composed of an accelerometers, gyroscope, and magnetometer), and then, after data processing, extracted signal features and used the dimensionality reduction method of principal component analysis (PCA). Attention-based particle swarm optimized convolution and long short-term memory (APSO-CALM) model was developed using the feature fused dataset, and its performance was compared with other models. The results showed that: (1) The multi-scale biological signals were analyzed and categorized into five distinct behaviors based on experimental records: feeding, rumination, mating, free movement and running. Each of these behaviors exhibits unique characteristics in their signal images. (2) PCA was utilized to reduce the dimensionality of the feature fused dataset of the multi-scale biological signals, preserving principal components with a cumulative contribution rate of 98 %. Among all components of the first and second contribution rates, except for a few individuals, there are significant differences (P &lt; 0.05) between the data of different behaviors of the same component. (3) The improved composite deep learning model, APSO-CALM, demonstrates significant advantages over single models in behavior recognition. Its accuracy, precision, recall, and F1 score are 95.0 %, 94.8 %, 94.5 %, and 94.6 %, respectively. By utilizing the APSO-CALM model, the drawbacks of individual models are mitigated, enhancing overall performance and overcoming the limitations of single model applications. This study effectively identified five behaviors of fattening Hu sheep, providing theoretical and practical basis for intelligent and precise management of fattening Hu sheep.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109635"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661723","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":"Benchmarking of monocular camera UAV-based localization and mapping methods in vineyards","authors":"Kaiwen Wang ,&nbsp;Lammert Kooistra ,&nbsp;Yaowu Wang ,&nbsp;Sergio Vélez ,&nbsp;Wensheng Wang ,&nbsp;João Valente","doi":"10.1016/j.compag.2024.109661","DOIUrl":"10.1016/j.compag.2024.109661","url":null,"abstract":"<div><div>UAVs equipped with various sensors offer a promising approach for enhancing orchard management efficiency. Up-close sensing enables precise crop localization and mapping, providing valuable a priori information for informed decision-making. Current research on localization and mapping methods can be broadly classified into SfM, traditional feature-based SLAM, and deep learning-integrated SLAM. While previous studies have evaluated these methods on public datasets, real-world agricultural environments, particularly vineyards, present unique challenges due to their complexity, dynamism, and unstructured nature.</div><div>To bridge this gap, we conducted a comprehensive study in vineyards, collecting data under diverse conditions (flight modes, illumination conditions, and shooting angles) using a UAV equipped with high-resolution camera. To assess the performance of different methods, we proposed five evaluation metrics: efficiency, point cloud completeness, localization accuracy, parameter sensitivity, and plant-level spatial accuracy. We compared two SLAM approaches against SfM as a benchmark.</div><div>Our findings reveal that deep learning-based SLAM outperforms SfM and feature-based SLAM in terms of position accuracy and point cloud resolution. Deep learning-based SLAM reduced average position error by 87% and increased point cloud resolution by 571%. However, feature-based SLAM demonstrated superior efficiency, making it a more suitable choice for real-time applications. These results offer valuable insights for selecting appropriate methods, considering illumination conditions, and optimizing parameters to balance accuracy and computational efficiency in orchard management activities.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109661"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Hop LoRa-based underground network for monitoring soil moisture in agriculture","authors":"Reinaldo Cotrim ,&nbsp;Flávio Assis ,&nbsp;Alexsandro dos Santos Brito ,&nbsp;Yslai Silva Peixouto ,&nbsp;Leandro Santos Peixouto","doi":"10.1016/j.compag.2024.109592","DOIUrl":"10.1016/j.compag.2024.109592","url":null,"abstract":"<div><div>Wireless underground sensor networks (WUSN) have gained attention due to the benefits they can bring to many application areas, in particular, to agriculture. However, designing and evaluating WUSNs is more complex than conventional over-the-air wireless networks, especially when the WUSNs have buried nodes. The study of the possibilities and limits of these networks is an active area of research. In this paper we describe a LoRa-based multi-hop WUSN for monitoring soil moisture for an application in agriculture being developed to investigate the behaviour of different species of <em>mamona</em> (<em>Ricinus communis L.</em>) under different soil moisture levels. We first evaluate the use of LoRa for underground-to-underground (UG2UG) communication links and show how different values of the main LoRa parameters affect the quality of these links. Based on the results, we designed a network whose topology is a set of lines of buried sensor nodes covering the whole application area. In this paper we describe the behaviour of one of these lines in a real setting in terms of packet delivery ratio and delay and we estimate the energy consumed for communication. Our protocol provides an inherent level of fault-tolerance by exploring the linear topology. In our experiments, a 100% message delivery ratio was achieved. Additionally, the maximum round-trip delay was less than 200 s. The network satisfies the application message transmission requirement of one message per hour per node by scheduling communication over the six sensor lines needed to cover the whole experiment area in a round-robin fashion. Our main contributions lie in the evaluation of different parameters of LoRa in underground communication and in the development and analysis of a multi-hop routing protocol for a network of buried nodes in a real setting. We are not aware of any other work that addresses these specific issues.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109592"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661725","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":"Deep learning-based classification of visual symptoms of bacterial wilt disease caused by Ralstonia solanacearum in tomato plants","authors":"J.P. Vásconez ,&nbsp;I.N. Vásconez ,&nbsp;V. Moya ,&nbsp;M.J. Calderón-Díaz ,&nbsp;M. Valenzuela ,&nbsp;X. Besoain ,&nbsp;M. Seeger ,&nbsp;F. Auat Cheein","doi":"10.1016/j.compag.2024.109617","DOIUrl":"10.1016/j.compag.2024.109617","url":null,"abstract":"<div><div>Classification of plant diseases based on computer vision is a multidisciplinary challenge that involves technical and data-related complexities. Artificial Intelligence (AI) has increasingly found its application in plant pathology, disease, and anomaly visual characterization. Specifically, Machine Learning (ML) and Deep Learning (DL) algorithms have proven to be highly effective for tasks such as plant disease classification, detection, diagnosis, and management. In this work, we present a comparative analysis of multiple DL models based on Convolutional Neural Networks (CNNs) for visual symptoms classification of the phytopathogen <em>Ralstonia solanacearum</em> in tomato plants. We demonstrate that by implementing DL classification algorithms based on CNNs, it is possible to identify <em>Ralstonia solanacearum</em> potentially affected plants. This was possible due to the main virulence factor of <em>Ralstonia solanacearum</em>, the exopolysaccharide (EPS), which obstructs the plant’s xylem limiting water absorption and consequently inducing visual wilting symptoms. For this, we implemented, trained, and evaluated fourteen different CNN-based models. We evaluated the models by using different metrics such as precision, recall, accuracy, specificity, and F1-score. The models that obtained the best accuracy results were MobileNet-v2 and Xception, with an accuracy of 97.7% for both models. The presented findings significantly contribute to the visual symptoms classification of <em>Ralstonia solanacearum</em> in tomato plants, which may contribute to the control of this disease and its spread to healthy crops or other susceptible hosts in the future.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109617"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661771","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":"Integrating masked generative distillation and network compression to identify the severity of wheat fusarium head blight","authors":"Zheng Gong,&nbsp;Chunfeng Gao,&nbsp;Zhihui Feng,&nbsp;Ping Dong,&nbsp;Hongbo Qiao,&nbsp;Hui Zhang,&nbsp;Lei Shi,&nbsp;Wei Guo","doi":"10.1016/j.compag.2024.109647","DOIUrl":"10.1016/j.compag.2024.109647","url":null,"abstract":"<div><div>Fusarium head blight (FHB) is a severe disease, with implications for both crop quality and safety. The inability to accurately and rapidly determine diseases severity has resulted in increasing grain loss and the pesticide expenses. Furthermore, the complexity of many current models presents challenges in their deployment and utilization. Thus, this study introduces an improved lightweight model for efficient and rapid assessment of FHB severity. Firstly, we collected 2650 wheat images with different severities in natural environments. Second, we refined and compressed RepGhostNet, replacing the original ReLU function with LeakyReLU and using the AdamW optimizer during training to enhance model accuracy. Third, using the strategy of masked generative distillation, we further improved the accuracy of SlimRepGhostNet while ensuring model lightweight. The MGD-SlimRepGhostNet achieved an accuracy of 94.58% and a frames per second (FPS) of 152.17. This represents a 4.34% increase in accuracy and a 21.17 increase in speed compared to the original RepGhostNet. Lastly, we have designed a WeChat mini program that achieves the performance of MGD-SlimRepGhostNet in real environments, highlighting its practicality. The proposed method effectively addresses the inaccuracies and labor-intensive associated with nature of traditional visual assessment methods deployed for evaluating FHB severity in wheat, while its rapid inference capability renders it highly suitable for deployment and application on mobile devices.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109647"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661681","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}