Computers and Electronics in Agriculture最新文献

{"title":"PheMuT: A phenology-informed, multi-modal time-series model for strawberry yield forecasting","authors":"Zijing Huang ,&nbsp;Won Suk Lee ,&nbsp;Yiannis Ampatzidis ,&nbsp;Shinsuke Agehara ,&nbsp;Natalia A Peres","doi":"10.1016/j.compag.2026.111526","DOIUrl":"10.1016/j.compag.2026.111526","url":null,"abstract":"<div><div><em>Accurate yield forecasting is crucial in optimizing resource management and decision-making processes in agriculture, particularly in crops such as strawberries, which require precise predictions due to their rapid and continuous ripening cycles. This study introduces PheMuT, a novel phenology-informed, multi-modal time-series model that integrates visual and meteorological data streams to enhance strawberry yield forecasting. The proposed method employs advanced computer vision techniques, including two YOLOv11 detectors, an optimized ByteTrack tracker, Segment Anything (SAM), and Depth Anything v2 (DAv2), for precise fruit detection, canopy, and volume estimation. Concurrently, high-frequency weather data are processed using a self-supervised autoregressive Temporal Convolutional Network (TCN), resulting in concise and informative weather embeddings. These visual and weather features are fused within an LSTM-based model to produce weekly yield forecasts. PheMuT was validated using two strawberry cultivars at a Florida research facility over two consecutive seasons. Results indicated that PheMuT improved forecasting accuracy, reducing mean absolute error (MAE) by 10.7%, root mean squared error (RMSE) by 12.5%, and mean absolute percentage error (MAPE) by 18.6% compared to baseline manual methods. Additionally, the model exhibited a notable improvement of 17.2% in the coefficient of determination (R²). PheMuT offers an efficient, automated framework for yield forecasting. Code and data are available at</em> <span><span><em>https://github.com/Sycamorers/PheMuT</em></span><svg><path></path></svg></span><em>.</em> The full datasets used in this study are available from the authors upon request.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111526"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173937","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":"Comparison of soil property predictions in Lithuanian croplands using UAV, satellite, EMI data and machine learning","authors":"R. Žydelis ,&nbsp;L. Weihermüller ,&nbsp;L.C. Gomes ,&nbsp;A.B. Møller ,&nbsp;F. Castaldi ,&nbsp;J. Volungevičius ,&nbsp;A. Kavaliauskas ,&nbsp;T. Koganti ,&nbsp;J. Wetterlind ,&nbsp;İ. Cinkaya ,&nbsp;L. Borůvka ,&nbsp;F. van Egmond ,&nbsp;S. Higgins ,&nbsp;F. Liebisch ,&nbsp;V. Povilaitis ,&nbsp;A. Kazlauskaitė-Jadzevičė ,&nbsp;K. Amalevičiūtė-Volungė ,&nbsp;S. Pranaitienė ,&nbsp;E. Vaudour","doi":"10.1016/j.compag.2026.111543","DOIUrl":"10.1016/j.compag.2026.111543","url":null,"abstract":"<div><div>Combining remote and proximal sensing provides a cost-effective solution for mapping soil properties in croplands. This study assessed the potential of remote sensing based on high resolution multispectral UAV imagery (2.6 cm), satellite (Sentinel-2), and in-field measured electromagnetic induction (EMI) data for predicting six soil properties − soil organic carbon content (SOC), clay, sand, silt contents, pH, and soil water content (SWC) − across five Lithuanian agroclimatic zones. Seven modelling scenarios, using individual and combined sources of sensor data, employing a random forest model, were evaluated. To assess real-world applicability, sampling-reduction simulation were additionally performed. SOC and clay predictions achieved the highest accuracy, while silt, sand, and SWC showed acceptable accuracy only in a few sites or specific modelling scenarios. Soil pH predictions were poor across all scenarios. Prediction accuracy varied across study sites, likely influenced by climate, soil parent material, topography, and agricultural management. Sensor data resolutions (2.6 cm, 1.6 m, 10 m per pixel) significantly affected prediction accuracy. For SOC predictions, UAV and Sentinel-2 data performed best, while EMI alone was less effective. In contrast, for clay predictions, EMI data yielded the highest accuracy, emphasizing its role for soil texture assessment. Multi-sensor fusion improved model performance during training but did not consistently enhance validation accuracy across sites, highlighting important cost–accuracy trade-offs and the need for realistic performance evaluation. Overall, the results demonstrate that the benefits of multi-sensor soil mapping are property-specific and site-dependent, providing guidance for scalable and economically viable field-scale soil mapping strategies.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111543"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173949","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":"2026-03-15","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":"A new generation of embodied intelligent plant protection unmanned vehicle integrated with hydrostatic transmission and four-wheel drive technology: design, development and application","authors":"Zhigang Ren ,&nbsp;Jian Chen ,&nbsp;Mingjiang Sun ,&nbsp;Renzhong Fu","doi":"10.1016/j.compag.2026.111525","DOIUrl":"10.1016/j.compag.2026.111525","url":null,"abstract":"<div><div>Addressing the pain points of insufficient hardware performance, low adaptability to complex environments, and poor operational precision in domestic orchard spraying equipment, combined with the scene characteristics and agronomic requirements of domestic plain/hilly and mountainous orchards, this paper takes the core principle of Embodied Intelligence (real-time interaction and feedback between hardware and the environment) as the guiding framework. It designs a new-generation embodied intelligent plant protection unmanned vehicle, providing a standardized carrier for the implementation of embodied intelligent algorithms. The equipment integrates a customized hydrostatic transmission (HST) module, four-wheel drive (4WD) and synchronous steering technology, equipped with an electro-hydraulic steering system and a dual-core control architecture (dual CAN bus communication) consisting of an industrial computer and a programmable logic controller (PLC). It features smooth torque adjustment, a maximum speed of 11 km/h, and a pesticide carrying capacity of 600 L, enabling unmanned autonomous operations. Field tests under half-load conditions show that at a driving speed of 0.8 m/s, the maximum lateral deviations of path tracking under different working conditions are 0.076 m, 0.118 m, and 0.182 m respectively. Compared with the traditional algorithm, on rough terrain, the maximum lateral deviation, average deviation, and standard deviation are reduced by 10.3%, 10.4%, and 9.5% respectively. This study constructs a hardware scheme adapted to domestic orchards, filling the gap in embodied intelligent hardware for orchards and providing key technical support for the intelligent iteration of equipment hardware and the engineering application of embodied intelligent algorithms.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111525"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174058","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":"Beyond Color: Advanced RGB-D data augmentation for robust semantic segmentation in crop farming scenes","authors":"Florian Kitzler ,&nbsp;Alexander Bauer ,&nbsp;Viktoria Kruder-Motsch","doi":"10.1016/j.compag.2026.111432","DOIUrl":"10.1016/j.compag.2026.111432","url":null,"abstract":"<div><div>The emergence of smart farming in recent years has substantially increased the importance of artificial vision systems in crop production. Data augmentation is essential for developing robust semantic segmentation models when dealing with small datasets, such as in selective weed control. Due to advances in multi-modal data fusion, RGB-D image datasets contribute substantially to improve model performance. However, most data augmentation techniques primarily modify the color channels, often neglecting the depth channel. Addressing this gap, we introduce three methods for augmenting RGB-D images: RGB-D-Aug, Recompose3D, and Compose3D. We conducted experiments utilizing a multi-modal fusion network tailored for semantic segmentation of different plant species, namely ESANet. RGB-D-Aug introduces artificial depth sensor noise in addition to commonly used geometric transformations and color variations. Recompose3D and Compose3D generate augmented RGB-D images and corresponding ground-truth labels by composing background images and a set of foreground plant snippets. Recompose3D rearranges plants from a given training image, while Compose3D employs all plant snippets available in the training dataset. In our experiments designed to evaluate generalization performance, we tested our three methods and compared them not only to the augmentation technique used in ESANet, which consists of geometric transformations and color channel variations, but also to an extended version of the Copy-Paste method, an image composition technique originally introduced for RGB images. All three of our proposed methods outperformed the ESANet augmentation. The image composition methods, Copy-Paste, Recompose3D, and Compose3D, performed significantly better, with Compose3D achieving the highest generalization performance of all methods tested. In addition to improving model robustness, Compose3D allows the creation of realistic agronomic image scenes. Our research is an important step towards developing robust and generalizable models for different applications in arable farming.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111432"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025086","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":"Dynamic gamma correction-guided CNN for low-light corn tassel enhancement in intelligent detasselling systems","authors":"Qirui Wang ,&nbsp;Yang Liu ,&nbsp;Shenyao Hu ,&nbsp;Yuting Yan ,&nbsp;Bing Li ,&nbsp;Hanping Mao","doi":"10.1016/j.compag.2026.111436","DOIUrl":"10.1016/j.compag.2026.111436","url":null,"abstract":"<div><div>The accuracy of intelligent corn detasselling systems is severely compromised by low-light conditions, which degrade image quality and impede tassel recognition. To address the limitations of existing methods, such as noise amplification, detail distortion, and inadequate global illumination modeling, a Low-Light Corn Plant Image Enhancement Model (L2CP-IEM) is proposed. The core of L2CP-IEM is an innovative closed-loop dynamic gamma correction mechanism. This mechanism, guided by the discriminator’s confidence, is embedded within a residual encoder-decoder architecture, enabling adaptive illumination adjustment and stable training. By using a green cardboard calibration method, a high-quality dataset consisting of 950 paired low-light and normal-light corn images was created. Experiments on the LOL-v1 benchmark dataset demonstrate that L2CP-IEM outperforms state-of-the-art methods such as GSAD and CIDNet in terms of the SSIM (0.908) and LPIPS (0.059). Ablation studies further validate the critical roles of residual connections and the dynamic gamma correction mechanism. In practical corn tassel image tests, L2CP-IEM achieves balanced performance in terms of brightness and colour restoration, significantly enhances the reconstruction of natural textures and hierarchical details, and fully restores the confidence of the Mask R-CNN in image segmentation. By synergizing physical principles with data-driven approaches, this method significantly improves the quality of low-light images and the robustness of recognition, thus offering a reliable and efficient solution for agricultural visual automation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111436"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025259","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":"Calculating flow rate for ceramic emitters in subsurface infiltration irrigation under various soil types based on fractal capillary bundle model","authors":"Xuanyue Tong ,&nbsp;Pute Wu ,&nbsp;Lin Zhang ,&nbsp;Xufei Liu ,&nbsp;Shoujun Wu","doi":"10.1016/j.compag.2026.111473","DOIUrl":"10.1016/j.compag.2026.111473","url":null,"abstract":"<div><div>Accurately calculating the flow rate of the emitters for subsurface infiltration irrigation is essential to match crop water requirements and provide a suitable water amount. However, drip emitters usually have a single flow path and are assumed to have a constant flow rate, which does not apply to subsurface infiltration irrigation represented by ceramic emitters (CEs). Therefore, a flow model was developed to accurately calculate the flow rate of CEs considering ceramic pore flow channels and soil types. Five experiment sites were selected for validation, with the average values of <em>MAE</em>, <em>RMSE</em>, and <em>R²</em> being 0.0064, 0.0095 and 0.8138, respectively. Meanwhile, the relationship between different soil types, porosities, and working water heads on the flow rate of CEs was explored using a dataset with 403 points of all soil types. Results show that the flow rate of CEs increased with the sand content, and the average flow rates of CEs in sand, silt, loam, and clay soil were 0.0525, 0.0453, 0.0257, and 0.003 L h⁻¹, respectively. Furthermore, the flow rate of CEs in more sandy soil was significantly affected by the porosity, which in clay soil was mainly determined by soil type. In addition, the influence of the working water head on the coefficient of variation (CV) for the flow rate of CE diminished significantly beyond approximately 74 cm. The model can accurately calculate the flow rate of CEs in practice, which is conducive to the practical application of subsurface infiltration irrigation systems and the efficient utilization of irrigation water.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111473"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079955","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":"2026-03-15","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":"Modifying water absorption process to enhance model performance on biomass accumulation under soil water and salt stresses","authors":"Yanzhe Hu ,&nbsp;Shaozhong Kang ,&nbsp;Risheng Ding ,&nbsp;Herman N.C. Berghuijs ,&nbsp;Iris Vogeler ,&nbsp;Yuan Qiu ,&nbsp;Leonardo A. Monteiro ,&nbsp;Marcos Lana","doi":"10.1016/j.compag.2026.111440","DOIUrl":"10.1016/j.compag.2026.111440","url":null,"abstract":"<div><div>Irrigation is expected to playing a pivotal role against climate change and cropping systems intensification, whilst the secondary soil salinization caused by imprecise irrigation is posing a serious challenge to crop production. Despite increasing attention has been paid to food crops, a more profound understanding of water deficit and soil salinity constraints on forage production is greatly desired, and the response of forage growth to enhanced water stress raised by salinity needs to be considered in crop models. For this purpose, the water absorption module in the APSIM-Lucerne model was extended with two modules that calculate the reduction of the water extraction coefficient (<em>KL</em>) from the chloride concentration (<em>Cl</em>) in soil to enable the simulation of inhibited plant growth under enhanced water stress due to soil salinity. Both modules assume that <em>KL</em> decreases with <em>Cl</em> above a threshold <em>Cl</em>. In the first module, the decrease is exponential (exponential <em>KL</em> modifier), whilst in the second module, <em>KL</em> decreases according to a power law (power <em>KL</em> modifier) until it reaches zero at another higher threshold <em>Cl</em>. In field experiments, soil water content, leaf area index and biomass were measured for alfalfa grown under different combinations of irrigation amounts and salinity levels. The performance of the modified models (exponential and power <em>KL</em> modifiers) and the original model (no <em>KL</em> modifier) to reproduce these data were compared. Results reveal that both modified models showed improved prediction of canopy development and biomass accumulation, while the modified model with the power <em>KL</em> modifier exhibited a comparatively higher predictability under high salinity level, with a relative root mean square error of 23%-27% for biomass, better than 24%-31% of the exponential model and 43%-45% of the original model. The soil water dynamics were not well predicted by the modified models due to an underestimation of soil evaporation which requires further investigation. The study improved the predictability of crop models for forage crop development and production under coupling soil water and salt stresses via the optimization of the dynamic plant water extraction process, thus can be used to chart more reliable irrigation strategies under various pedoclimatic conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111440"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079801","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":"2026-03-15","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}