Computers and Electronics in Agriculture最新文献

{"title":"Precision mapping of soil salinity in reclaiming salt-induced wasteland with UAV multispectral images and machine learning","authors":"Haisong Yao ,&nbsp;Tibin Zhang ,&nbsp;Songling Chen ,&nbsp;Yuxin Kuang ,&nbsp;Yu Cheng ,&nbsp;Qing Liang ,&nbsp;Hao Feng ,&nbsp;Gaoxiang Zhou ,&nbsp;Kadambot H.M. Siddique","doi":"10.1016/j.compag.2026.111532","DOIUrl":"10.1016/j.compag.2026.111532","url":null,"abstract":"<div><div>Identifying the spatial distribution and degree of soil salinity accurately and promptly is crucial for reclaiming salt-induced wasteland. It provides decision support for rapidly delineating the center of salt patches, zoning salinized areas, and rationally planning reclamation regions. This study investigated the potential of deriving Salinization-Feature Enhanced (SFE) spectral indices from multispectral (MS) band combinations for estimating field soil salinity content (SSC) in the Luohui Canal Irrigation District, Shaanxi China. The sensitivity of the SFE indices and conventional spectral indices (SIs) to SSC was analyzed using the Pearson correlation coefficient and the Variable Iterative Space Shrinkage Approach (VISSA). We assessed the accuracy of estimating surface SSC during the seedling stage under complex soil salinization environments, analyzing the effects of varying spatial resolutions and sampling densities. Extreme Learning Machine (ELM) and Random Forest Classifier (RFC) were employed to invert surface SSC and generate a digital soil salinity map. The results indicated that the SFE (|r| = 0.58) indices showed a stronger correlation with SSC compared with the SIs (|r| = 0.26), indicating superior performance in surface SSC inversion. The incorporation of environmental covariates enhanced the model’s inversion accuracy and stability. The sample dataset was divided into 70% training and 30% test subsets using stratified sampling. Surface SSC prediction models were developed using SFE indices and SIs with both the ELM and RFC algorithms. The ELM_SFE (accuracy = 0.68) and RFC_SIs (accuracy = 0.69) models achieved the best predictive accuracy. The ELM_SFE and RFC_SFE models demonstrated the best inversion performance in severely and moderately salt-induced wasteland, respectively. This study provides valuable insights for precision agricultural management in salt-induced wasteland reclamation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111532"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173948","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":"2026-03-15","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":"A sorting approach capable of ordering and discretizing stacked stalk-seeds by mechanical energy metering","authors":"Biao Zhang,&nbsp;Qiancheng Liu,&nbsp;Jishan Wen,&nbsp;Guangyi Liu","doi":"10.1016/j.compag.2026.111528","DOIUrl":"10.1016/j.compag.2026.111528","url":null,"abstract":"<div><div>For continuous row sowing, automatic cane-seed metering devices face the challenge of efficiently sorting stalk-seeds from a chaotically stacked state into an orderly discrete arrangement, but the geometric variability of such irregular stalks and their multiple interactions with flow channels complicate the controllability of seed posture and flow flux. To address this, a novel sorting approach based on metered mechanical energy charge-release was proposed to adaptively regulate the orientation and flux of high-mass-energy rod-particle flow. The diversion chains of ordering and discretization were thus sequentially integrated. Combining interactive DEM-MBD simulations with bench experimental behavior, the dynamic structure–activity relationship of energetic drive-constraint sequence was investigated as particle arrangement progressively transformed into ordered supply and quantitative discharge. This revealed the normalized contact mechanics mechanism for directional alignment and individual disengagement. The synergistic utilization of forked rolling, unbalanced collision and linear acceleration was demonstrated to produce a straightening flow effect, and the redundancy elimination effect achievable by the V-shaped turning lifting flow was elucidated. Logistics response models were developed to reflect the impact of various diversion factors on sequential sorting capacities. With the optimal regulation path coordinated for energy-flux, the results showed that arrangement orderliness and discrete uniformity were improved by 50.3% and 86.3%, respectively, implying more stable control over transfer posture consistency and discharge accuracy. Such improvements in sorting quality while maintaining efficiency provided direct evidence for the controllable transformation of irregular stalk-seed arrangement states.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111528"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173988","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":"Remote detection of root malformation disorder in Eucalyptus saligna using UAV multispectral imagery and U-Net++","authors":"Sally Deborah Pereira da Silva ,&nbsp;Vinicius Richter ,&nbsp;Norton Borges Junior ,&nbsp;Regiane Aparecida Ferreira ,&nbsp;Gustavo Vedooto Ferreira ,&nbsp;Telmo Jorge Carneiro Amado ,&nbsp;Luan Pierre Pott ,&nbsp;Lucio de Paula Amaral","doi":"10.1016/j.compag.2026.111522","DOIUrl":"10.1016/j.compag.2026.111522","url":null,"abstract":"<div><div>Root malformation disorder (RMD) is an abiotic stress that compromises the early development of <em>Eucalyptus saligna</em> plantations in Brazil, reducing growth, nutrient uptake, and canopy vigor. Despite its operational relevance, scalable tools for objective field detection remain limited. Remote sensing using unmanned aerial vehicles (UAVs) combined with deep learning offers a promising alternative for identifying early physiological stress. The objectives of this work were: (i) to characterize the biophysical attributes of plants affected by RMD; and (ii) to evaluate the feasibility of a deep learning–based approach to map different plant health conditions of <em>E. saligna</em> at the stand scale. Multispectral data from a RedEdge-MX sensor (Blue, Green, Red, Red-edge, NIR) were collected over an 11 ha, six-month-old <em>E. saligna</em> stand in Southern Brazil. Field measurements included plant height, diameter at breast height (DBH), chlorophyll content, and leaf nutrient concentrations. Ten vegetation indices (VIs) were computed, and Random Forest (Gini importance) identified two key predictors: the Canopy Chlorophyll Content Index (CCCI) and the Plant Senescing Reflectance Index (PSRI). U-Net++ was trained to classify four classes: healthy, unhealthy, dead plants, and soil/residues. RMD-affected trees showed significant reductions in height, DBH, chlorophyll content, and nutrient concentrations. The combination CCCI + PSRI yielded the best discrimination of unhealthy plants (precision = 98.75%, recall = 92.94%, F1-score = 95.76%), with an overall accuracy of 98.77%. Applied to the full stand, 93.41% of trees were classified as healthy, 3.70% as unhealthy, and 2.90% as dead. These findings demonstrate that UAV multispectral imagery integrated with U-Net++ enables accurate, low-cost detection of RMD-related stress, supporting early silvicultural decision-making and routine plantation monitoring.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111522"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173991","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":"Intelligent management of crop diseases and pests in multiscale and multimodal complex scenarios: Technologies, applications, and prospects","authors":"Chang Xu ,&nbsp;Lei Zhao ,&nbsp;Haojie Wen ,&nbsp;Yiding Zhang ,&nbsp;Lipo Wang ,&nbsp;Lingxian Zhang","doi":"10.1016/j.compag.2026.111443","DOIUrl":"10.1016/j.compag.2026.111443","url":null,"abstract":"<div><div>Efficient management and precise monitoring are essential for the sustainable control of crop diseases and pests. Traditional unimodal methods exhibit reduced reliability due to data gaps and environmental fluctuations. Multimodal artificial intelligence (AI) offers a promising alternative by integrating complementary data sources and enhancing robustness and adaptability. However, a comprehensive synthesis connecting multimodal AI with multi-scale disease and pest management is still lacking. Based on 950 publications from the past decade reflecting a 31.7% annual growth rate over the past five years, this review examines the evolution of AI-driven research and compares unimodal and multimodal approaches by summarizing major data modalities, fusion strategies, and modeling techniques. Deep learning emerges as the most widely used class of AI methods, and quantitative evidence indicates that multimodal systems achieve approximately 3–48.9% higher diagnostic accuracy than unimodal models. Evidence from 27 studies demonstrates the effectiveness of multimodal fusion across imaging, spectral, environmental, and sensor-based datasets. Building upon these findings, we propose a novel three-level management framework comprising point-level diagnosis, area-scale monitoring, and spatiotemporal forecasting, clarifying how multimodal AI strengthens each task. We further highlight the role of Plant Electronic Medical Records (PEMRs) and outline a conceptual virtual plant clinic to support continuous, data-driven crop health services. Finally, this review identifies key directions including advanced fusion strategies, lightweight and interpretable models, digital twin integration, and scalable decision-support systems, which are essential for intelligent and sustainable crop disease and pest management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111443"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080289","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":"2026-03-15","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":"Semantic segmentation–based detection of exposed soil regions in paddy fields for a floating-type puddling and leveling operation","authors":"Zian Liang ,&nbsp;Jun Zhou ,&nbsp;Yongpeng Chen ,&nbsp;Yinghua Zhang ,&nbsp;Tamiru Tesfaye Gemechu ,&nbsp;Lei Li ,&nbsp;Huayu Zhou ,&nbsp;Muhammad Aurangzaib","doi":"10.1016/j.compag.2026.111494","DOIUrl":"10.1016/j.compag.2026.111494","url":null,"abstract":"<div><div>Integrated puddling–leveling operation is a critical step in paddy field preparation, typically conducted between plowing and rice transplanting. However, the accuracy of elevation measurements in existing automatic leveling technologies is often constrained by limited operating ranges or susceptibility to electromagnetic interference, resulting in inconsistent leveling performance. Because the water surface naturally reflects terrain undulations in paddy fields, this study proposes a semantic segmentation–based approach to detect exposed soil regions for guiding a floating-type puddling and leveling implement. To this end, a lightweight semantic segmentation model, PL_DeepLabV3+_0.8, was developed specifically for integrated puddling–leveling operation. The model combines a MobileNetV2_S backbone, a Low-Level Feature Fusion Module (LFM), and structured pruning. These components collectively enable the rapid and accurate detection of exposed soil in paddy fields under computationally constrained conditions. The PL_DeepLabV3+_0.8 model was successfully deployed in the control system of a floating-type implement, and its effectiveness was validated through field tests conducted at different operating<!--> <!-->speeds and modes. On a paddy field image dataset, PL_DeepLabV3+_0.8 achieved a mean Pixel Accuracy (mPA) of 92.23 ± 0.22%, a mean Intersection over Union (mIoU) of 84.18 ± 0.31%, and an inference speed of 7.73 frames per second (FPS), outperforming the original DeepLabV3 + model, which achieved 91.90%, 83.81%, and 0.88 FPS, respectively. In field tests at operating speeds of 1.1 m/s and 1.5 m/s, the surface flatness (standard deviation of elevation) in two paddy fields was improved from 3.61 cm and 4.07 cm to 2.11 cm and 2.42 cm, respectively. These results indicate that the deployed model not only satisfies the flatness requirement for rice transplanting (&lt; 3 cm) but also delivers a productivity increase of 0.28 ha/h compared with conventional manual operation. Overall, this study provides a useful reference for the development of intelligent puddling and leveling technologies in paddy field preparation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111494"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080286","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":"2026-03-15","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":"Combining physical models and machine learning for enhanced soil moisture estimation","authors":"Ming Li ,&nbsp;Hongquan Sun ,&nbsp;Ruxin Zhao","doi":"10.1016/j.compag.2026.111480","DOIUrl":"10.1016/j.compag.2026.111480","url":null,"abstract":"<div><div>Estimating soil moisture is crucial for agricultural management, water resource planning, and environmental monitoring. Traditional methods, whether based on physical models or machine learning, face limitations, with physical models suffering from reduced accuracy due to parameter sensitivity and environmental variability, and machine learning models struggling with interpretability and generalization. To address these challenges, this study introduces a novel hybrid approach that leverages the strengths of both physical modeling and machine learning to enhance soil moisture estimation. The hybrid model, ML-Phy-meteo, integrates the physical model (Hydrus-1D) results and meteorological data into a LightGBM framework, achieving optimal estimation accuracy across various soil depths. Quantitatively, ML-Phy-meteo exhibits superior performance across all depths, achieving an average root mean square error (RMSE) between 0.020 and 0.026 cm³/cm³, an average Nash–Sutcliffe Efficiency (NSE) ranging from 0.195 to 0.811, and an average Kling–Gupta Efficiency (KGE) from 0.623 to 0.860, thereby outperforming both standalone physical models and purely machine learning-based approaches. Notably, ML-Phy-meteo achieves high-precision predictions even in the absence of detailed soil texture and stratification data, with the machine learning component effectively compensating for the simplifications of the physical model. Among the machine learning methods used in the hybrid model, tree-based models (LightGBM and Random Forest) outperform deep learning models (LSTM) in terms of accuracy and robustness in handling noise and missing data, despite the latter’s smoother prediction profiles. These findings highlight the potential of hybrid models to overcome the inherent limitations of standalone physical or machine learning approaches, providing new ideas for future research and applications in soil moisture estimation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111480"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173986","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":"Non-destructive monitoring method for protected-lettuce yield using deep learning","authors":"Xiaodong Zhang ,&nbsp;Tingting Yu ,&nbsp;Mohamed Farag Taha ,&nbsp;Shenghan Zhou ,&nbsp;Jin Zhou ,&nbsp;Yixue Zhang ,&nbsp;Yiqiu Zhao ,&nbsp;Zongyao Cai ,&nbsp;Jingjing Sun ,&nbsp;Yuxiang Pan ,&nbsp;Jianfeng Ping","doi":"10.1016/j.compag.2026.111516","DOIUrl":"10.1016/j.compag.2026.111516","url":null,"abstract":"<div><div>Accurate acquisition of phenotypic characteristics in protected crops is a crucial prerequisite for intelligent control and digital breeding in greenhouses. To accurately assess the phenotypic traits of protected lettuce, a specialized in situ phenotypic detection method has been developed. The Multimodal Features and Attention Mechanism for Phenotype Detection Model (MFAMNet) was developed for protected lettuce, employing a segmented multi-source image dataset for synchronous regression testing. The results revealed that the predicted values generated by MFAMNet exhibited a strong correlation with the measured values, achieving coefficients of determination of 0.96, 0.92, 0.95, 0.94, and 0.95 for plant height, crown width, leaf area, fresh weight, and dry weight, respectively. Ablation tests demonstrated that the deep learning detection framework based on multi-modal feature fusion significantly outperformed single-feature detection models, highlighting the advantages of integrating diverse data modalities. In addition, the multi-modal feature attention mechanism (MMF) facilitates both inter-modality and intra-modality interactions by capturing the global correlations between modalities and employing dynamic sparse spatial attention. The effectiveness of MMF has been validated through comparative experiments, demonstrating its suitability for the phenotypic detection of artificially cultivated lettuce. In summary, the method proposed in this study facilitates real-time monitoring of facility crops, enabling precise control of environmental parameters in protected agriculture and optimizing resource allocation. This approach contributes to the development of a comprehensive intelligent agriculture system and establishes a foundation for unmanned farms.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111516"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173987","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}