Computers and Electronics in Agriculture最新文献

{"title":"Smart turtle farm: soft-shell turtle nesting behavior recognition and monitoring system","authors":"Alexander Munyaev ,&nbsp;Ben-Lin Jiang ,&nbsp;Ing-Jer Huang","doi":"10.1016/j.compag.2026.111477","DOIUrl":"10.1016/j.compag.2026.111477","url":null,"abstract":"<div><div>Soft-shell turtle farming, a growing aquaculture practice in East and Southeast Asia, involves a pond habitat and a sand-filled spawning area that serves as a nesting site for female turtles. Harvesting these eggs is a labor-intensive and time-consuming task, as farmers must excavate the entire spawning area to ensure all eggs are collected while avoiding damage to buried eggs. Additionally, turtles may inadvertently nest over existing sites, exposing previously buried eggs to a higher risk of damage or consumption by other turtles. Accurate identification of nest locations can significantly reduce farmers’ workload by eliminating unnecessary excavation, while early detection of exposed eggs allows for timely collection, minimizing egg loss, and improving productivity. To address these challenges, we present a real-time AI-based monitoring system to detect soft-shell turtle nesting behavior and exposed eggs. The system integrates YOLOv7 for detecting turtles and exposed eggs, object tracking to monitor turtle movements, and an LSTM-based spatio-temporal model to recognize nesting behaviors. YOLOv7 achieved an average precision (AP) of 98.9% for turtle detection and 88.8% for exposed egg detection, while the LSTM-based model demonstrated 95.73% accuracy and 98.64% recall for recognizing nesting activity. During the 2022 nesting season, spanning 245 days, the system identified 1,713 nests and saved 76.62% of the egg-harvesting efforts, as farmers no longer needed to excavate the entire spawning site. This efficient, non-invasive approach minimizes egg loss, optimizes farm management, and highlights the potential of precision livestock technologies to enhance productivity and sustainability in soft-shell turtle farming.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111477"},"PeriodicalIF":8.9,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173939","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":"Collaborative motion planning for multi-arm tea-picking robots","authors":"Jiangming Jia ,&nbsp;Yujie Li ,&nbsp;Xiang Wang ,&nbsp;Taojie Yu ,&nbsp;Jianneng Chen ,&nbsp;Yujie Zhou ,&nbsp;Chuanyu Wu","doi":"10.1016/j.compag.2026.111470","DOIUrl":"10.1016/j.compag.2026.111470","url":null,"abstract":"<div><div>To improve the picking efficiency of tea-picking robots, this study proposes a multi-arm collaborative motion planning approach designed for unstructured tea-plantation environments. The proposed approach consists of collaborative picking methods based on arm priority allocation and an improved AtRRT-Connect trajectory planning algorithm to achieve efficient multi-arm coordination and collision avoidance. The algorithm introduces the target gravity and adaptive parameter adjustment strategy, and employs Bézier curves for trajectory smoothing. Simulation results show that under the same tea shoot distributions, the proposed collaborative picking methods achieve full coverage of the workspace. In comparison, the baseline RRT-Connect algorithm exhibited a 100% trajectory generation rejection rate in certain dense segments, whereas the proposed AtRRT-Connect algorithm successfully generated feasible trajectories for all test samples, and significance analysis of the simulated experimental data revealed that both trajectory generation time and trajectory length differed significantly (p &lt; 0.01). In field experiments, the four-arm tea-picking robot picked a single shoot in a mean time of 1.22 s while ensuring collision-free. The comprehensive mean single-shoot picking time was 1.37 s, representing a 20.9% improvement over traditional methods, confirming the effectiveness of the proposed approach.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111470"},"PeriodicalIF":8.9,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174053","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":"Apple damages classification: Using the best convolutional neural network to discard low surface quality fruit in packing plants","authors":"Martín Molina ,&nbsp;Julio Godoy ,&nbsp;John W. Castro ,&nbsp;Vladimir Riffo","doi":"10.1016/j.compag.2026.111489","DOIUrl":"10.1016/j.compag.2026.111489","url":null,"abstract":"<div><div>In an era where global apple production exceeds 80 million tons annually, ensuring high fruit quality is essential for consumer satisfaction and economic success. However, surface defects like wounds, rot, and sunburn cause millions in losses through manual inspections, which are often subjective, inefficient, and costly in packing plants. This study fills important gaps in automated quality control by using advanced deep learning to classify apple damages with unmatched efficiency and industrial usefulness. Through a review of the literature and various web repositories that include information up to 2025, we constructed a novel, balanced dataset from scratch, capturing diverse real-world defects that were underrepresented in previous studies. We rigorously evaluated nine advanced convolutional neural network architectures –including VGG16/19, multiple ResNet variants, and YOLOv9c for classifying different types of damage in apples– before optimizing the top-performing ResNet101 through systematic hyperparameter tuning. Achieving an impressive 95% accuracy on unseen data for damage classification and 81% for preliminary detection, our optimized model aims to reduce waste and boost supply chain efficiency, setting a new standard for sustainable agriculture. Moving forward, this framework opens the door to multimodal integrations such as hyperspectral imaging and robotic sorting, adaptable to other fruits, transforming post-harvest processing and inspiring further innovations in AI-driven food security.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111489"},"PeriodicalIF":8.9,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174054","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":"CALDS-RTDETR: a robust forestry pest detection model for small targets in complex environments","authors":"Wenjun Luo ,&nbsp;Haiyan Zhang ,&nbsp;Limeng Xu","doi":"10.1016/j.compag.2026.111482","DOIUrl":"10.1016/j.compag.2026.111482","url":null,"abstract":"<div><div>The timely and accurate detection of forest pests is crucial for protecting ecosystems and maintaining ecological balance, as it directly affects the efficacy of pest control measures. Although deep learning is widely used for forest pest detection, challenges remain due to the small size of pests, complex environments, and their diverse morphologies across developmental stages. Traditional detection models often underperform in these environments. To overcome these challenges, we propose CALDS-RTDETR, an enhanced RT-DETR model designed specifically for detecting small pests in complex forest environments. We evaluated the model on a real-world dataset comprising 15 pest species. Compared to the RT-DETR-R18 baseline, CALDS-RTDETR achieved a precision of 75.5%, recall of 61.8%, mAP_0.5 of 63.8%, mAP_0.75 of 49.7%, and mAP_0.5:0.9₅ of 45.3%. It also attained an mAPs of 8.9%, mAPm of 31.1%, and mAPl of 54.4%, while maintaining a compact model size of 20.10 M parameters. These results show the model’s enhanced performance in complex forest environments, demonstrating the significant potential of CALDS-RTDETR for pest monitoring and practical deployment. Future work will expand the model to include additional species and optimize it for real-world applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111482"},"PeriodicalIF":8.9,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174055","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":"In-situ decomposition sensor output correlates with soil health indicators","authors":"Taylor J. Sharpe ,&nbsp;Madhur Atreya ,&nbsp;Shangshi Liu ,&nbsp;Mengyi Gong ,&nbsp;Nicole Luna ,&nbsp;Noah Smock ,&nbsp;Jessica Davies ,&nbsp;John N. Quinton ,&nbsp;Richard D. Bardgett ,&nbsp;Jason C. Neff ,&nbsp;Rebecca Killick ,&nbsp;Gregory L. Whiting","doi":"10.1016/j.compag.2026.111427","DOIUrl":"10.1016/j.compag.2026.111427","url":null,"abstract":"<div><div>Monitoring of soil microbiological processes can inform strategies to improve soil health and agricultural productivity. Biological soil health measurements are currently difficult to make in-situ and in real time, usually involving manual sampling and laboratory analysis. This is costly, time consuming, resource intensive, and cannot measure changes at high temporal and spatial resolution, limiting the ability to make prompt informed land management decisions. Low-cost soil sensors manufactured using printing techniques offer a potential scalable solution to these issues. Here, we tested the use of novel sensors for the proxy evaluation of soil microbial processes, hypothesizing that sensor decomposition rates may be related to manual soil sampling measurements. This is the first multi-plot field deployment of sensors which use a biodegradable composite conductor to transduce microbial decomposition of substrates to a change in electrical resistance, providing time-series decomposition rate data. Sensors were installed for 50 days across 44 experimental plots of a long-term grassland experiment with varying historical treatments and significant differences in soil microbial activity. Early failures and unresponsive substrates reduced the included sensor count to 31. Measurements commonly used as soil health indicators, including microbial biomass and enzymatic activities related to nutrient cycling, were determined using standard laboratory methods and compared to sensor responses. Three statistical approaches found positive correlations between the sensor signal and laboratory measurements of microbial biomass carbon and soil organic carbon, and some approaches found weaker correlations with enzymatic measurements. Although this experiment is limited in scope to a single experimental field and season, these initial findings show promise for enabling the proxy measurement of soil microbial processes in-situ using low-cost, scalable printed sensors.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111427"},"PeriodicalIF":8.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174057","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":"MCDA: a novel domain adaptation with multiple classifiers for crop mapping","authors":"Changhong Xu ,&nbsp;Maofang Gao ,&nbsp;Yuanwei Chen ,&nbsp;Jingwen Yan","doi":"10.1016/j.compag.2026.111450","DOIUrl":"10.1016/j.compag.2026.111450","url":null,"abstract":"<div><div>Accurate crop mapping is conducive to optimizing agricultural production, improving resource utilization efficiency, as well as supporting precision agriculture management and environmental monitoring. However, crop mapping often relies on a large amount of reference data as labels. Moreover, the generalization and scalability of approaches are challenging issues due to the influence of geographic locations, climatic conditions, and crop characteristics. Thereby, this study proposed the Domain Adaptation with Multiple Classifiers (MCDA) model that enables crop mapping in target domains without reference data, which consists of a feature extractor and three classifiers. Initially, the data from the source domain was trained using the feature extractor and individual classifier. Next, the feature extractor was fixed and two classifiers were trained. The decision boundaries of classifiers were maximized by calculating discrepancies between different classification results in target domain. Finally, the two classifiers were fixed and the feature extractor was trained to map the data from the source domain and target domain into a better feature space. In this study, three sets of experiments were designed to validate the stability and generalization of MCDA model by selecting different study areas in four countries to classify typical crops such as maize and winter wheat, rice, and soybeans using time series Vegetation Indices (VIs). Comparing the 1DCNN and LSTM models, as well as the four UDA models, the experimental results indicate that the MCDA model outperforms the other models in most transfer cases. With the appropriate selection of crop features, the method proposed in this study can achieve more accurate crop mapping effectively identifying crop types and field boundaries. The implementation code of our method has been publicly available at <span><span>https://github.com/abbyxuchanghong-cmd/MCDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111450"},"PeriodicalIF":8.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174052","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":"Operationalizing water rights reform in irrigation districts using a water accounting–based decision support system and automated operating strategies","authors":"S. Mehdy Hashemy Shahdany ,&nbsp;Amir Reza Khakpour ,&nbsp;Reza Rejaie","doi":"10.1016/j.compag.2026.111476","DOIUrl":"10.1016/j.compag.2026.111476","url":null,"abstract":"<div><div>An operating framework for surface-water distribution in irrigation districts was developed by linking SEEA-Water accounting Physical Supply–Use Tables (PSUTs) to canal control through a decision support system (DSS). The method included: (i) hydraulic simulation of canal flow using an Integral Delay (ID) model; (ii) development of decentralized Proportional-Integral (PI) and centralized Model Predictive Control (MPC) operating systems; (iii) integration of Standard Operating Procedures (SOPs) with hydraulic and operational models to simulate daily water distribution; and (iv) translation of SEEA-PSUT surface water-rights allocations into controller setpoints in the DSS. Spatio-temporal simulations, conducted under seven scenarios ranging from normal to severe water shortage conditions, assessed surface water distribution and groundwater abstraction for PI, MPC, and manual-based SOPs. Following modifications to water rights derived from PSUTs, simulations were repeated. Results demonstrated significant performance improvements with MPC-SOP (23–37% water recovery), notable enhancements with Automatic PI-SOP (12–18% water recovery), and marginal improvements with Manual-SOP (2–10% water recovery). The approach converts accounting reform into operational rules, delivering measurable efficiency gains and supporting sustainable irrigation management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111476"},"PeriodicalIF":8.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173947","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":"Innovative photosynthesis model twinning after intelligent interpretation of complex sensor analytics","authors":"Xiaotong Wang ,&nbsp;Xuejiao Tong ,&nbsp;Bingguang Han ,&nbsp;Zhulin Li ,&nbsp;Qingji Li ,&nbsp;Xianmin Liu ,&nbsp;Zhouping Sun ,&nbsp;Nick Sigrimis ,&nbsp;Tianlai Li","doi":"10.1016/j.compag.2026.111496","DOIUrl":"10.1016/j.compag.2026.111496","url":null,"abstract":"<div><div>Accurate canopy photosynthesis modeling is essential for understanding and optimizing crop growth and yield in greenhouse agriculture. Current models have limited predictive capability due to inadequate responsiveness to dynamic environments and delays in parameter acquisition, making accurate predictions challenging under the complex conditions of solar greenhouses. This study aimed to develop a dynamic canopy photosynthesis model for greenhouse tomatoes, leveraging an IoT sensor network for real-time biological feedback and parameterization. By integrating real-time monitoring with dynamic feedback, the model facilitates precision management of greenhouse tomato cultivation, thereby optimizing plant growth, resource use efficiency, and yield predictability. To achieve this, a non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAI_p, R² ≥ 0.91), continuously providing parameters for updating modelling (validated against destructive sampling and actual measurements for trait specifics). Based on accurate parameter acquisition, a dynamic canopy photosynthesis model was developed using LAI_p as the core variable, integrating above-canopy radiation. A newly developed parameter, which integrates the radiation component of transpiration, serves as a key factor for estimating photosynthesis. This innovative approach allows for accurate daily prediction and assessment of assimilated biomass. Experimental results from 2022 and 2023 showed that the LAI_p model performed better than the comparison model, showing higher accuracy and adaptability (R² = 0.87 and 0.89, NRMSE = 0.17 and 0.12 vs. R² = 0.70 and 0.80, NRMSE = 0.26 and 0.15). These results confirmed the reliability of the integrated modeling framework, which forms a closed-loop system connecting real-time plant monitoring, statistical parameter inversion, online model adaptation, and biomass feedback verification. This modeling approach provides a solid foundation for precise growth simulation, sustainably improving yield and quality in solar greenhouse tomatoes, and advancing digital twin-enabled intelligent production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111496"},"PeriodicalIF":8.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079802","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":"WeedCAM: An edge-computing camera system for multi-species weed detection in sugar beet production fields","authors":"Zonglin Yang ,&nbsp;Wei-Zhen Liang ,&nbsp;Nevin Lawrence ,&nbsp;Xin Qiao ,&nbsp;Benjamin Riggan ,&nbsp;Robert Harveson ,&nbsp;Chi-En Chiang ,&nbsp;Joseph Oboamah ,&nbsp;Diwenitissiou Philipine Andjawo","doi":"10.1016/j.compag.2026.111498","DOIUrl":"10.1016/j.compag.2026.111498","url":null,"abstract":"<div><div>This study introduces WeedCAM, a low-cost, near real-time, edge-computing camera system for multi-species weed detection, built on a Raspberry Pi 5 and integrated with a GPS module and LoRa board for geolocation and data transmission. A three-phase framework, including data acquisition, model fine-tuning, and deployment, is proposed to implement detection models on WeedCAM. A total of 5734 high-resolution (4K) images were automatically collected using WeedCAM, producing a dataset with a long-tail distribution that poses challenges for model training. To address this, Repeat Factor Sampling and Focal Loss were applied during the fine-tuning. Seven object detection models were evaluated, including YOLOX-S, YOLOX-L, Faster R-CNN, Cascade R-CNN, Deformable-DETR, and DINO. Finally, WeedCAMs with embedded trained models were deployed in the field on pivot-mounted and ground-based installations, detecting weeds at 30-min intervals and transmitting results to a customized gateway via LoRa. The gateway parsed and mapped these results to our custom-designed website for visualization. Our best model, DINO-Swin/L, set the performance benchmark with a 76.0 overall mAP (IoU = 0.5) and strong per-species scores for kochia (76.4 mAP), Palmer amaranth (77.3 mAP), and volunteer corn (75.9 mAP) at 4K resolution image. Despite this, YOLOX-L was deployed on the WeedCAM, as its efficient 8-min processing cycle represented the better trade-off between accuracy and speed. Field evaluation confirmed that WeedCAM effectively identified weed species and quantities under varying lighting conditions, camera angles, and soil moisture levels during irrigation events. These results demonstrate the practicality of deploying WeedCAM edge-computing deep learning systems for near real-time, multi-species weed detection under sugar beet fields.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111498"},"PeriodicalIF":8.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080287","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":"Detection of Potato Virus Y in plant foliage using convolutional neural network classifiers and hyperspectral imagery","authors":"L.M. Griffel,&nbsp;D. Delparte","doi":"10.1016/j.compag.2026.111499","DOIUrl":"10.1016/j.compag.2026.111499","url":null,"abstract":"<div><div><em>Solanum tuberosum</em> (potato) is one of the most important global food crops relative to economic opportunities and food security. <em>Potato Virus Y</em> (<em>Potyviridae</em>, PVY), a detrimental plant pathogen propagated by insect vectors, negatively affects tuber yield and quality. This has forced industry stakeholders to adopt many different types of mitigation strategies including pesticide applications, manual field scouting, and potato seed certification programs. Despite these efforts, PVY continues to disrupt industry production regions resulting in significant economic losses due to the lack of robust diagnostic tools. Machine learning algorithms trained on remotely sensed spectral features show promise as a diagnostic tool for many plant diseases including PVY. This study proposes a novel Convolutional Neural Network (CNN) architecture to detect potato plant canopy regions of plants infected with PVY based on unmanned aerial system (UAS) hyperspectral pixel features comprised of bands matching the center wavelengths of nine spectral channels captured by the European Space Agency’s Sentinel 2 multispectral instrument. Accuracy and F1 metrics of 0.815 and 0.766 respectively were achieved on test data collected over multiple growing seasons and locations. Additionally, efforts were made to identify optimal combinations of spectral bands that are most beneficial for the CNN classifier by evaluating every possible combination of the nine spectral wavelengths in groups ranging from 3 to 9 channels. Results show that hyperspectral channels centered on 783 nm, 739 nm, and 560 nm are the most important features for the CNN architecture. Additionally, six hyperspectral features consisting of the three previously mentioned along with 665 nm, 704 nm, and 864 nm yielded the best results of all possible combinations achieving accuracy and F1 Score metrics of 0.833 and 0.791 respectively.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111499"},"PeriodicalIF":8.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079823","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}