Computers and Electronics in Agriculture最新文献

{"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}

{"title":"Coupled effects of seed-discharge parameters and seedbed geometry on Allium chinense seeds–soil bounce","authors":"Qixin Kang ,&nbsp;Guozhong Zhang ,&nbsp;Chao Ji ,&nbsp;Zhuangzhuang Zhao ,&nbsp;Liming Chen ,&nbsp;Lingxiao Lan","doi":"10.1016/j.compag.2026.111456","DOIUrl":"10.1016/j.compag.2026.111456","url":null,"abstract":"&lt;div&gt;&lt;div&gt;&lt;em&gt;Allium chinense&lt;/em&gt; is a specialty crop valued for its distinctive flavor and medicinal properties, with substantial exports to Japan, South Korea, and international markets. Despite its economic importance, cultivation remains labor-intensive due to strict agronomic demands for precise seed orientation. A key obstacle to mechanization is collision-induced bouncing during sowing, which compromises placement quality. This study investigates the effects of seed-discharge parameters and seedbed geometry on directional sowing by analyzing seed–soil collision mechanisms. DEM (Discrete Element Method) models based on HM-JKR (Hertz-Mindlin with JKR) model were constructed, with parameters calibrated via P-B (Plackett-Burman) screening and B-B (Box-Behnken) optimization. Targeted at an AOR (angle of repose) of 41.67°, the optimal parameters were determined as restitution coefficient (&lt;em&gt;R&lt;sub&gt;e&lt;/sub&gt;&lt;/em&gt;) = 0.051, rolling friction coefficient (&lt;em&gt;C&lt;sub&gt;r&lt;/sub&gt;&lt;/em&gt;) = 0.027, and surface energy (&lt;em&gt;K&lt;sub&gt;i&lt;/sub&gt;&lt;/em&gt;) = 0.180 J/m&lt;sup&gt;2&lt;/sup&gt; between soil and seeds, yielding a relative error of 1.8 %. Single-factor simulation experiments were conducted to analyze seed-discharge height (&lt;em&gt;h&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;), velocity (&lt;em&gt;v&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;) and posture (&lt;em&gt;p&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;) on flat and V-shaped seedbeds from an impact-energy perspective. Uniform-design experiments were conducted to develop multivariate regression prediction models, which were then validated by simulations and bench tests. Results indicate that on flat seedbeds, &lt;em&gt;h&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;, &lt;em&gt;v&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;, and &lt;em&gt;p&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; significantly influence maximum bounce height (&lt;em&gt;h&lt;sub&gt;b&lt;/sub&gt;&lt;/em&gt;) and rebound velocity (&lt;em&gt;v&lt;sub&gt;b&lt;/sub&gt;&lt;/em&gt;), whereas planar displacement (&lt;em&gt;e&lt;sub&gt;p&lt;/sub&gt;&lt;/em&gt;) is primarily governed by &lt;em&gt;h&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; and &lt;em&gt;v&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;. On V-shaped seedbeds, &lt;em&gt;v&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; exhibits a highly significant effect on &lt;em&gt;v&lt;sub&gt;b&lt;/sub&gt;&lt;/em&gt; but no significant effect on &lt;em&gt;h&lt;sub&gt;b&lt;/sub&gt;&lt;/em&gt; or &lt;em&gt;e&lt;sub&gt;p&lt;/sub&gt;&lt;/em&gt;. As &lt;em&gt;v&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; increases, &lt;em&gt;h&lt;sub&gt;b&lt;/sub&gt;&lt;/em&gt; and &lt;em&gt;v&lt;sub&gt;b&lt;/sub&gt;&lt;/em&gt; follow a “first increase, then decrease” trend. In contrast, seed bounce generally intensifies on flat seedbeds. Validation tests showed no significant differences between model predictions, simulations, and bench tests (paired t-tests, p &gt; 0.05), supporting model reliability. Under sandy loam conditions, using lower seed-discharge kinetic energy combined with matched furrow geometry and seed-discharge posture effectively mitigates bounce and drift of &lt;em&gt;Allium chinense&lt;/em&gt; seeds. The optimized parameters are &lt;em&gt;h&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; = 180 mm and &lt;em&gt;v&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; = 0.36 m/s, with seeds oriented facing the furrow and aligned either parallel or perpendicular to the furrow axis. These findings provide actionable guidelines for mechanized oriented sowing systems targeting &lt;em&gt;Allium chinense&lt;/em&gt; and analogous bulbous ","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111456"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079878","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":"Frequency-aware deep learning for diarrheal feces and floor fouling monitoring in pig pens","authors":"Hao Wang ,&nbsp;Yixue Liu ,&nbsp;Bin Sun ,&nbsp;Juncheng Ma ,&nbsp;Chao Liang ,&nbsp;Xiao Yang ,&nbsp;Renli Qi ,&nbsp;Chaoyuan Wang","doi":"10.1016/j.compag.2026.111465","DOIUrl":"10.1016/j.compag.2026.111465","url":null,"abstract":"<div><div>Floor fouling monitoring in pig facilities is essential for early disease detection and environmental hygiene management, as diarrheal feces indicates digestive disorders while manure accumulation directly impacts animal health and welfare. Current manual inspection methods are labor-intensive and subjective, while existing computer vision approaches suffer from unstable color features under varying lighting conditions and misclassification of background textures as fouling patterns. To address these challenges, we propose FreCANet, a frequency-aware deep learning framework that achieves multi-level fouling classification through hierarchical visual interference suppression. The method integrates three key innovations: Mask R-CNN preprocessing that eliminates pig body occlusion (improving detection recall by up to 17.21%), Frequency Dynamic Convolution that separates manure contamination features from environmental noise across different frequency bands, and Efficient Channel Attention embedded within residual connections for selective feature enhancement. Using a comprehensive dataset of 25,228 images covering seven fouling categories across the complete growth cycle, FreCANet achieved 88.31% accuracy and 0.8679 F1-Score, outperforming ResNet-152 by 2.44% and 2.93% respectively. Diarrheal feces detection reached 95.9% precision on slatted floors and 89.3% recall on solid floors, enabling reliable early warning for digestive health issues. The four-level manure contamination classification achieved 77.2–87.4% precision across fouling gradients from clean to severely soiled conditions. These results demonstrate FreCANet’s effectiveness in transforming subjective manual inspection into quantitative pen hygiene assessment for precision livestock farming applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111465"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025153","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":"Biomimetic design and research of plough body surface for reducing draught force of a single large-width mouldboard plough","authors":"Hao Zhou,&nbsp;Zhiyu Qin,&nbsp;Xuezhen Wang,&nbsp;Shengsheng Wang,&nbsp;Kangtai Li,&nbsp;Guoliang Deng,&nbsp;Haibo Yan","doi":"10.1016/j.compag.2026.111461","DOIUrl":"10.1016/j.compag.2026.111461","url":null,"abstract":"<div><div>To reduce the draught force and energy consumption of the mouldboard plough, a biomimetic plough body surface (BPBS) was designed with the structures of pangolin scales and shark placoid scales as biomimetic coupling elements.<!--> <!-->A soil-mouldboard plough interaction model was constructed by EDEM and verified for accuracy through laboratory soil bin experiments under sandy loam conditions. The verified model was employed for optimising the biomimetic coupling plough body surface.<!--> <!-->Single-factor, steepest ascent and Box-Behnken experiments were designed to investigate the transverse spacing, longitudinal spacing, installation angle, scale length, and biomimetic structure height of the BPBS with the draught force and the soil disturbance area used as indicators. Results illustrated both errors of simulated and measured draught forces and soil disturbance areas were less than 5.8%. The transverse spacing, longitudinal spacing, installation angle, and biomimetic structure height of BPBS influenced the two indexes significantly. The optimal parameters from Box-Behnken response surface experiments were transverse spacing of 23 mm, longitudinal spacing of 15 mm, installation angle of 30°, and biomimetic structure height of 14 mm. BPBS with the optimal parameters reduced the draught force by 13.06% and increased the soil disturbance area by 5.29% compared with the conventional mouldboard plough. The errors of predicted values were small compared with the simulated. The design of the BPBS achieved the research objective of reducing the draught force without affecting the ploughing quality.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111461"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025262","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":"Generative data augmentation for AIoT-based smart pig farming: The PigTalk 2.0 system","authors":"Whai-En Chen,&nbsp;Shih-Che Lin,&nbsp;Hsin-Hung Cho","doi":"10.1016/j.compag.2026.111442","DOIUrl":"10.1016/j.compag.2026.111442","url":null,"abstract":"<div><div>Piglet mortality is a significant issue on pig farms. Often, piglets do not receive proper care after birth or are crushed by the sow, leading to the death of 2 to 4 piglets during each farrowing process. This represents a substantial loss for pig farmers. To address these issues, this paper presents the development of the PigTalk2.0 system, which is based on the Artificial Internet of Things (AIoT) and image recognition technology, aimed at preventing piglet deaths caused by crushing by the sow. Since collecting large-scale agricultural datasets is costly and labor-intensive, Since collecting large-scale agricultural datasets is costly, this study proposes a hybrid augmentation strategy. We combine conventional techniques (e.g., translation, flipping) with Generative AI-based data augmentation to effectively expand limited datasets and enhance model robustness, providing synthetic diversity that strengthens model training. PigTalk2.0 uses image recognition to monitor the posture of the sow. To prevent the sow from accidentally crushing the piglets when she suddenly lies down, the system activates an air blower to gently push the piglets away when the sow stands up. Additionally, PigTalk2.0 can detect when a sow is about to farrow for the first time, allowing the system to notify the farm administrator to check the piglets’ health and provide necessary care immediately after birth. The results demonstrate that introducing consumer IoT technology into family-run agriculture or medium- to large-scale enterprise-run pig farms can significantly improve productivity.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111442"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025267","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":"Developing a nitrogen management strategy for winter wheat to enhance economic profit and energy savings using satellite-based management zone mapping at the county scale","authors":"Jie Jiang ,&nbsp;Zhaopeng Fu ,&nbsp;Jiayi Zhang ,&nbsp;Jinpeng Yang ,&nbsp;Wanping Fang ,&nbsp;Qiang Cao ,&nbsp;Yongchao Tian ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Xiaojun Liu","doi":"10.1016/j.compag.2026.111527","DOIUrl":"10.1016/j.compag.2026.111527","url":null,"abstract":"<div><div>Management zone (MZ) mapping is the first step in precision nitrogen management (PNM) to improve crop management efficiency and optimize resource input. Satellite remote sensing is a suitable tool for collecting crop growth information to guide decision-making over large areas. This study aimed to develop a PNM strategy utilizing satellite image-based MZ mapping to improve crop nitrogen use efficiency and reduce energy costs. Four wheat field experiments were conducted from 2020 to 2022 in Xinghua County, China. Sentinel-2 satellite imagery was employed to delineate wheat growth MZs using the Fuzzy K-means clustering method prior to the nitrogen (N) topdressing period. Subsequently, a management zone-accumulated N deficit-based N topdressing algorithm (MZ-ANDA) was developed to calculate N recommendation rates and evaluate the effects of variable N application in comparison to individual ANDA and conventional farmer fixed N application practices. The results demonstrated that wheat growth MZs could be effectively delineated using multi-temporal Sentinel-2 satellite spectral images before the N topdressing period. The MZ-ANDA strategy showed comparable results to ANDA in single wheat fields. However, when considering the comprehensive benefits of variable N topdressing at large area farms, the MZ-ANDA strategy reduced energy input by 2.22–26.54 GJ, economic input by $511.91–2639.08, and CO₂ emissions by 0.31–2.05 tons across three farms ranging from 57.21 to 283.78 ha. Compared to conventional farmer fixed N practices, the MZ-ANDA reduced the N application rates with 12.67%–37.00% and increased the partial factor productivity with 9.47%–19.81% while maintained a high grain yield, meanwhile, the MZ-ANDA improved the net profit (9.23–139.48 $ ha⁻¹), energy use efficiency (4.10%–10.17%) and energy productivity (5.06%–11.66%) while reduced the CO₂ emission (2.95%–10.00%). In summary, the developed N management strategy using satellite images-based MZ map can significantly increase management efficiency and optimize resource input for in-season wheat management at the county scale. Future research could focus on refining the MZ-ANDA algorithm for different crops and regions to further enhance N management efficiency.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"244 ","pages":"Article 111527"},"PeriodicalIF":8.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173943","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}