Computers and Electronics in Agriculture最新文献

{"title":"Quantifying high-temperature-induced reproductive growth imbalance in citrus at anthesis: Insights from the CF-ASPM model","authors":"Xia Qiu ,&nbsp;Feiyu Zhu ,&nbsp;Ping Huang ,&nbsp;Hongwen Chen ,&nbsp;YaLi Li ,&nbsp;Changbing Pu ,&nbsp;Zongnan Li ,&nbsp;Dan Zhong ,&nbsp;Chao Xiang ,&nbsp;Jin Chen ,&nbsp;Si Wang","doi":"10.1016/j.compag.2025.110421","DOIUrl":"10.1016/j.compag.2025.110421","url":null,"abstract":"<div><div>Global climate change-induced environmental stress poses critical challenges to the stable development of economic crops such as citrus. High temperatures (HTs) at anthesis may cause poor pollination and excessive flower/fruit drop, seriously affecting fruit yield and quality. To comprehensively analyze the developmental dynamics and morphological responses of citrus to HT stress at anthesis, methods for precise whole-flower phenotypic extraction and stamen state classification were developed. A citrus flower automatic segmentation and phenotypic quantitative model (CF-ASPM) that combines the pre-trained Segment Anything Model (SAM) with a lightweight classification module was constructed to accurately identify and quantify key citrus flower structures. Phenotypic parameter extraction correlation coefficients were 0.90–0.98. A few-shot stamen classification method was also designed using a pre-segmentation strategy and differential features, and its classification accuracy was 96.39%. Experiments with <em>Ehime</em> mandarin were conducted to analyze dynamic citrus floral organ changes at different temperatures and the underlying physiological mechanisms. The results showed that citrus exhibits a distinct reproductive priority strategy under HTs. Floral organ growth is inhibited, blooming is accelerated, and an asynchronous compensation mechanism occurs between male and female organs. HTs accelerated flower aging and caused developmental imbalances in the ovary and nectar disc. This may lead to increased flower and fruit drop and altered fruit shape. This study revealed the reproductive priority strategy and growth imbalance of citrus floral organs under HTs using the CF-ASPM model. It provides important data for further exploring the molecular mechanisms and management strategies of HT stress.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110421"},"PeriodicalIF":7.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863888","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-silico optimization of peanut production in India through envirotyping and ideotyping","authors":"Amir Hajjarpoor ,&nbsp;Jan Pavlík ,&nbsp;Jan Hora ,&nbsp;Jakub Konopásek ,&nbsp;Janila Pusupuleti ,&nbsp;Vincent Vadez ,&nbsp;Afshin Soltani ,&nbsp;Til Feike ,&nbsp;Michal Stočes ,&nbsp;Jan Jarolímek ,&nbsp;Jana Kholová","doi":"10.1016/j.compag.2025.110383","DOIUrl":"10.1016/j.compag.2025.110383","url":null,"abstract":"<div><div>Peanut (<em>Arachis hypogaea</em> L.) is an important cash crop with significant yield gaps, especially in developing countries. Optimizing peanut production could foster economic growth for a significant number of smallholder farmers across the globe. In this study, we used an <em>in-silico</em> cropping system model to simulate and optimize genotype × crop management (G × M) across India that would narrow the existing peanut yield gaps. For that, we simulated diverse G × M combinations across range of environments (E) in India, considering three irrigation regimes typical for managing peanut production systems. Covering whole India in a 0.5°×0.5° resolution, we simulated 60,480 G × M combinations for each grid, summing up to a total of 2.3 billion simulations and 1.02 TB output data. This required well-structured high-performance computing (HPC) approaches, data management, and analytical capacities. For this, we present the concept of a re-usable HPC system with interoperable modules, which can be readily adapted for different simulation setups. We introduced the novel way of analyzing simulation outputs − “Index of Goodness” (IoG) − that aggregates key peanut production characteristics (grain and haulm production) and production risk failure. IoG is a simple way to evaluate the suitability of simulated GxM options from the perspective of end-users, including primary producers and crop improvement programs. The generated output was used to identify the geographic regions (environmental clusters, EC) with high degree of similarities within each of the tested irrigation regimes. For each cluster, we identified a specific suite of GxM to benefit peanut production and prioritize G targets for breeding. In principle, irrigated cropping systems would benefit from high planting densities, long duration and vigorous crop types. With diminishing water availability (particularly in the Thar Desert and SE India), the optimal production included shorter duration crop types which could quickly respond to drought stimuli (i.e. close stomata and conserve soil water upon soil and atmospheric drought exposure). These traits should also be considered in phenotyping strategies to support context-specific breeding.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110383"},"PeriodicalIF":7.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868727","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":"Improving spatial prediction of soil organic matter in typical black soil area of Northeast China using structural equation modeling integration framework","authors":"Xingnan Liu ,&nbsp;Mingchang Wang ,&nbsp;Ziwei Liu ,&nbsp;Yilin Bao ,&nbsp;Xiaoyan Li ,&nbsp;Fengyan Wang ,&nbsp;Xue Ji","doi":"10.1016/j.compag.2025.110404","DOIUrl":"10.1016/j.compag.2025.110404","url":null,"abstract":"<div><div>Soil organic matter (SOM) is a critical factor in determining soil fertility, and understanding its spatial distribution is essential for ensuring stable grain production and sustainable agricultural development. High-resolution remote sensing (RS) data offer significant potential for predicting the spatial distribution of SOM over large areas, and effective predictive models can achieve high mapping accuracy. In this study, Landsat 8 and Sentinel-2 combined with environmental variables as two distinct sets of predictive covariates were used to predict the SOM content of typical black soil areas in Northeast China using six machine learning (ML) models, while analyzing the effects of these covariates. The model averaging integration framework based on Structural Equation Modeling (SEM-MA) was proposed to combine the predictions of ML models to improve mapping accuracy, and it was compared with six traditional integration methods. The results show that the Landsat 8 covariate combination outperforms Sentinel-2 in prediction performance. Among single ML models, MLP achieves the best prediction accuracy with an R² of 0.64. The MA methods improve SOM prediction results, and SEM-MA provides the highest R² increase (12.5 %), explaining 72 % of the spatial variability in SOM and significantly improving SOM mapping. Additionally, the accuracy of SEM-MA remains unaffected by the performance of any single model. The SOM prediction map indicates higher content in the northeast and lower content in the west, with an average SOM content of 21.90 g·kg⁻¹. SOM is primarily explained by the difference index (DI) (46.08 %), followed by environmental covariates (19.29 %) and original bands (17.53 %). SOM is closely related to the environmental covariates used, with bulk density (BD) and sand having an inhibitory effect, while soil moisture (SM) and silt promote its accumulation. However, SOM is more sensitive to changes in BD and its distribution is also affected by sand and silt interactions. Overall, SEM-MA proves to be a feasible tool for extracting effective information from different models while avoiding their limitations in SOM mapping. This study provides a valuable reference for enhancing the spatial prediction of soil properties, and the proposed method can be applied to improve the accuracy of digital soil mapping.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110404"},"PeriodicalIF":7.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863886","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":"SHAPAttention: A novel approach to enhance model performance and interpretability in agricultural spectral data analysis","authors":"Ting Wu ,&nbsp;Longhui Zhu ,&nbsp;Lei Li ,&nbsp;Leian Liu ,&nbsp;Weidong Bai ,&nbsp;Li Lin ,&nbsp;Ling Yang","doi":"10.1016/j.compag.2025.110445","DOIUrl":"10.1016/j.compag.2025.110445","url":null,"abstract":"<div><div>This paper proposes an innovative deep learning method, SHAPAttention, aiming to enhance the performance and interpretability of models in spectral analysis. This method utilizes SHAP (SHapley Additive explanation) values as a dynamic attention mechanism to accurately capture the contributions of spectral features to the model output. The performance of SHAPAttention was evaluated on three different spectral datasets: near infrared, Raman, and hyperspectral band data. The results show that compared with the standard one-dimensional convolutional neural network, the determination coefficients of the predictions for the three datasets increased from 0.83, 0.81, and 0.59 to 0.87, 0.85, and 0.65 respectively. The ratio of performance to deviation values increased from 2.42, 2.40, and 1.57 to 2.88, 2.78, and 1.71 respectively. Compared with attention mechanisms (such as self_attention and squeeze-and-excitation attention), SHAPAttention improves the prediction performance of the model. The algorithm has a certain anti-interference ability against noise. In addition, this method also provides a dynamic feature importance analysis, enhancing the interpretability of the model. The research indicates that SHAPAttention has great potential in improving the performance and transparency of spectral analysis models, providing new ideas for precise detection and decision making in the agricultural field.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110445"},"PeriodicalIF":7.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859622","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 greenhouse strawberry seedbed image stitching method based on seedbed plane alignment and efficient point-line matching","authors":"Jiong Lin ,&nbsp;Xue Bai ,&nbsp;Mengen Yuan ,&nbsp;Dong Wang ,&nbsp;Shuqin Yang ,&nbsp;Jifeng Ning","doi":"10.1016/j.compag.2025.110416","DOIUrl":"10.1016/j.compag.2025.110416","url":null,"abstract":"<div><div>In the context of factory-based cultivation of strawberry seedlings in greenhouses, acquiring panoramic images of the seedbed is essential for monitoring the overall growth of the strawberry seedlings, including assessing the uniformity of growth and detecting the presence of pests and diseases. This paper presents a novel greenhouse seedbed image stitching method based on seedbed plane alignment and efficient point-line matching, using a rail-based inspection device mounted above the seedbed to capture sequential images covering the seedbed area, in order to obtain high-quality panoramic images of the strawberry seedlings. First, since the strawberry seedlings are located within the seedbed area, the Depth-Anything model is utilized to extract the seedbed plane, allowing the registration algorithm to focus on the precise alignment of the seedbed region. Secondly, to fully leverage the geometric structure in the strawberry seedbed images, a local registration method GlueStick based on point-line matching is applied to match feature points between overlapping seedbed images, significantly reducing the number of feature points while effectively enhancing matching accuracy. Finally, exploiting the equidistant imaging characteristics of the rail-based imaging device, a homography matrix optimization method is proposed, effectively mitigating the impact of a small number of inaccurate local matches on the global stitching performance. Comprehensive experiments, encompassing both subjective (qualitative scoring) and objective (RMSE, Image Distortion Degree) evaluations, conducted on the constructed strawberry seedbed image dataset, demonstrate that the proposed method achieves precise alignment of the seedbed and effectively preserves the overall naturalness, outperforming representative image stitching methods. The proposed method delivers high-quality panoramic images for seedbed monitoring, offering substantial support for precise monitoring of greenhouse crops, and provides valuable references for panoramic stitching methods of other greenhouse crops.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110416"},"PeriodicalIF":7.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859623","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":"Prediction of fruit shapes in F1 progenies of chili peppers (Capsicum annuum) based on parental image data using elliptic Fourier analysis","authors":"Fumiya Kondo ,&nbsp;Yui Kumanomido ,&nbsp;Mariasilvia D’Andrea ,&nbsp;Valentino Palombo ,&nbsp;Nahed Ahmed ,&nbsp;Shino Futatsuyama ,&nbsp;Kazuhiro Nemoto ,&nbsp;Kenichi Matsushima","doi":"10.1016/j.compag.2025.110422","DOIUrl":"10.1016/j.compag.2025.110422","url":null,"abstract":"<div><div>Fruit shape significantly impacts the quality and market value of chili peppers (<em>Capsicum annuum</em>). However, predicting their fruit shapes in F₁ hybrids remains challenging, often relying on skilled breeders. This study aimed to clarify the potential of elliptic Fourier descriptors (EFDs) to predict fruit shape of F₁ progeny in chili peppers based on parental data. Using images of 291 accessions (132 inbred and 159 F₁ from 20 parental inbreds), EFDs were extracted to reconstruct shape contours. The initial prediction method, PP_mid, used midpoint EFDs of the parents, achieving accuracies comparable to genomic methods. To improve accuracy, a new method, PP_δ, was developed. PP_δ incorporates dominance effects observed in F₁ progeny, yielding significantly better predictions. Over 80% of F₁ accessions showed improved accuracy with PP_δ, and the predicted contours aligned closely with real shapes. Cross-validation confirmed the reproducibility of PP_δ predictions. These findings suggest that combining parental EFDs with dominance effect ratios enables accurate fruit shape predictions without genetic data. This is the first study demonstrating EFD applicability in F₁ hybrid breeding for fruit shape, offering a promising tool for developing innovative breeding techniques in chili peppers.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110422"},"PeriodicalIF":7.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859621","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":"SSOD-MViT: A novel model for recognizing alfalfa seed pod maturity based on semi-supervised learning","authors":"Fuyang Tian ,&nbsp;Yinuo Zhang ,&nbsp;Shakeel Ahmed Soomro ,&nbsp;Qiang Wang ,&nbsp;Shuaiyang Zhang ,&nbsp;Ji Zhang ,&nbsp;Qinglu Yang ,&nbsp;Yunpeng Yan ,&nbsp;Zhenwei Yu ,&nbsp;Zhanhua Song","doi":"10.1016/j.compag.2025.110439","DOIUrl":"10.1016/j.compag.2025.110439","url":null,"abstract":"<div><div>The current study was conducted to address the challenges of recognizing alfalfa seed pod maturity in complex field environments, and the significant impact of the quantity of labeled samples on the performance of object detection algorithms. A method for identifying the maturity of alfalfa seed pod clusters was proposed using an unmanned aerial vehicle (UAV) and a semi-supervised deep learning model SSOD-MViT (Semi-Supervised Object Detection based on the MViTNet). To enhance the model’s capability to extract key feature information, an improved lightweight general vision transformer MobileViT (Mobile Vision Transformer) was firstly employed as the backbone. The deep integration of ScConv (Spatial and Channel Reconstruction Convolution) was additionally employed to reduce redundant information within the channels, thereby decreasing the computational load of the model. Secondly, a small object detection layer was incorporated into the Neck, and the Efficient Multi-Scale Attention Module (EMA) was added to the C2f structure. The SAHI (Slicing Aided Hyper Inference) algorithm was integrated during the inference process, which improves the detection accuracy of small-sized alfalfa seed pod clusters and enhances the model’s resistance to interference. Finally, the concept of Consistency Regularization was incorporated into the model to reduce its dependency on sample data. The experimental results revealed that SSOD-MViT achieved a <em>mAP</em>_@0.5 of 92.23 %. When compared to the YOLOv8 object detection model, the <em>mAP</em>_@0.5 had improved by 12.31 %. When compared to the Faster R-CNN object detection model, the average detection time reduced by 175.81 ms. The proposed model MViTNet (MobileViT Network) had a storage size of 5.3 MB, and an average detection time of 82.34 ms, providing favorable conditions for subsequent deployment on embedded devices. This research effectively improved the detection performance of existing models in detecting alfalfa seed pod maturity in complex field environments. This advancement also aids in determining the optimal harvesting period for alfalfa seeds, thereby providing technical support to enhance productivity and reduce production costs in the alfalfa seed production industry.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110439"},"PeriodicalIF":7.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863889","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":"Performance of a real-time and wireless electrochemical gas sensor for monitoring ammonia concentration in a naturally ventilated dairy barn","authors":"E. Rosa ,&nbsp;R. Azevedo ,&nbsp;P. Merino","doi":"10.1016/j.compag.2025.110418","DOIUrl":"10.1016/j.compag.2025.110418","url":null,"abstract":"<div><div>One of the most widely used techniques for monitoring ammonia (NH₃) concentrations on livestock farms has been the photoacoustic gas analyser (PGA), which requires cabling, multiple repeated measurements at the same sampling point and lacks real-time remote capabilities. Electrochemical gas sensors (EGS) offer a promising alternative, allowing higher sampling frequencies, wireless operation and real-time data transmission. While EGS performance has been validated in laboratory settings, field testing remains limited. The objectives of this study were to determine the number of repeated PGA measurements required to achieve steady-state NH₃ concentrations and to compare them to EGS measurements in a naturally ventilated dairy barn. Ammonia concentrations were monitored at five sampling points on a commercial dairy farm using both technologies over 18 days. The results showed non-normal NH₃ concentration data distributions and similar patterns between them, ranging from 0.5 to 9.6 ppm for PGA and 0.6 to 13 ppm for EGS. A stabilisation of NH₃ measurements was observed between the 4th, 5th and 6th repeated measurements of PGA (p &gt; 0.75). A strong agreement between PGA and EGS values was also observed (PC &gt; 0.7 and RMSE &lt; 0.9). This study demonstrates the feasibility of using EGS for real-time, wireless NH₃ monitoring in dairy barns, eliminating PGA operational requirements. In addition, EGS allows data to be collected at 1-min intervals, a significant improvement on the 1-h intervals typical of PGA systems. This resolution allows the detection of rapid NH₃ fluctuations, providing a practical solution for real-time NH₃ measurement in precision livestock farming applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110418"},"PeriodicalIF":7.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859717","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":"Study on blockage monitoring of differential-speed roller potato-soil separation device","authors":"Zheng Ma ,&nbsp;Chang Liu ,&nbsp;Jiaqi Zhang ,&nbsp;Shuai Wang ,&nbsp;Yaoming Li","doi":"10.1016/j.compag.2025.110424","DOIUrl":"10.1016/j.compag.2025.110424","url":null,"abstract":"<div><div>Soil blockage in potato-soil separation devices during operation significantly compromises harvesting efficiency, necessitating real-time monitoring solutions. This study developed a multi-sensor data acquisition system to capture<!--> <!-->strain signals from the comb teeth, vibration signals from the differential-speed roller and rod screen bearings, speed signals from the differential-speed roller and rod screen under different working conditions. Then, a genetic algorithm was used to optimize the Gauss kernel parameters, and a support vector machine model for identifying soil blockage was established based on the extracted features. The results show that the device is a risk of blockage if one or more of the following conditions occur: (1) the filtered peak strain of comb teeth 5 and 6 exceeds 0.3×<span><math><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>4</mn></mrow></msup></mrow></math></span>; (2) the amplitude of meshing frequency between rod screen and rubber wheel is reduced to 0.01; (3) the peak-to-peak value of soil skateboard vibration signal is lower than 70 % of the normal value; (4) the rod screen and the differential-speed roller speed are lower than 80 % of the normal value. The model with optimal kernel parameters exhibited high accuracy, with 96.7 % precision, 94.2 % recall rate and 95.4 % F1-score for the test set. This study establishes a theoretical framework for intelligent blockage monitoring in potato harvesters, with practical implications for improving harvesting efficiency.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110424"},"PeriodicalIF":7.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning and machine learning methods based on the NIRS dataset for rapid determination of the nutrients content and quality of oat hay","authors":"Li Wang ,&nbsp;Tao Wang ,&nbsp;Luming Dai ,&nbsp;Fei Li ,&nbsp;Tao Guo ,&nbsp;Fadi Li ,&nbsp;Zhiyuan Ma ,&nbsp;Kaidong Li ,&nbsp;Hui Xu ,&nbsp;Maimaiti Reshalaitihan","doi":"10.1016/j.compag.2025.110428","DOIUrl":"10.1016/j.compag.2025.110428","url":null,"abstract":"<div><div>Oat hay is characterized by a high content of neutral detergent fiber, elevated sugar levels, and exceptional palatability, rendering it an ideal forage option for ruminant animals. This study investigates the rapid classification of oat hay quality grades under different standards, utilizing a combination of 2DCOS and deep learning methods. The 2DCOS images distinctly exhibit the spectral discrepancies among oat hay of diverse qualities within the 1100–1800 nm range. The deep learning model demonstrated a 100 % accuracy rate in identification under different standards. Moreover, MPLS and SSA-Lasso were employed to predict the contents of dry matter (DM), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), Ash, ether extract (EE), water soluble carbohydrates (WSC), calcium (Ca), phosphorus (P) and kalium (K) in oat hay. The MPLS effectively predicted the content of DM, NDF, ADF, CP, Ash, WSC, Ca, P and K, with an RPD of ≥ 2.00. With an RPD of 2.01, the SSA-Lasso-based EE prediction model produced the best results. The successful outcomes demonstrated that machine learning applied to NIRS data is a suitable method for rapidly verifying the nutrient content and quality of oat hay.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110428"},"PeriodicalIF":7.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855718","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}