Computers and Electronics in Agriculture最新文献

{"title":"Field roads passable area recognition via multi-sensor fusion based on segment anything model","authors":"Lili Yang ,&nbsp;Yuanbo Li ,&nbsp;Xiao Guo ,&nbsp;Mengshuai Chang ,&nbsp;Caicong Wu","doi":"10.1016/j.compag.2025.110685","DOIUrl":"10.1016/j.compag.2025.110685","url":null,"abstract":"<div><div>Rapid and effective recognition of the passable area of field roads is of great significance to unmanned agricultural machinery. This paper introduces FastL-SAM, a method that fuse 16-channel LiDAR point clouds and camera images based on Segment Anything Model (SAM). In this paper, a data acquisition platform was built to construct a multimodal dataset containing two road types and three exposure levels. We performed spatial synchronization on the multimodal data and extracted passable area point clouds based on road surface characteristics. These point clouds were then transformed and used as input prompts for SAM. After quantization and compression, the passable area of the field road was determined. Experimental results indicate that FastL-SAM achieved a Mean Intersection over Union (MIoU) of 91.75 % and an average Accuracy (Acc) of 96.34 %, outperforming the original SAM by 1.83 % and 2.43 % respectively, and demonstrating robust generalization. FastL-SAM achieved an average processing speed of 70 ms/frame with a recognition range of approximately 78 m for straight roads while also effectively recognizing forked roads. This performance meets the requirements of autonomous agricultural machinery systems for real-time processing and an extensive recognition range.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110685"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702509","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":"Highly transferable paddy field identification model based on SAR index and transformer","authors":"Xin Pan ,&nbsp;Jun Xu ,&nbsp;Xiaofeng Li ,&nbsp;Jian Zhao","doi":"10.1016/j.compag.2025.110790","DOIUrl":"10.1016/j.compag.2025.110790","url":null,"abstract":"<div><div>Artificial intelligence models are powerful tools for accurately identifying paddy fields from remote sensing data, which is crucial for effective agricultural management and decision-making. Due to historical and funding constraints, we often have very limited samples available, and samples’ features cannot fully cover all regions and time periods. This requires the corresponding model to have good transferable capabilities. However, building a transferable model also requires a large number of samples. This contradiction severely limits our ability to perform paddy field identification over large spatial and temporal ranges. To address these challenges, we propose a transferable index transformer based deep model for paddy field identification based on Sentinel-1 time series data (Transferability-Index). Transferability-Index constructs a novel structure that integrates existing SAR paddy field identification indexes into the neural network. This allows the model to inherit the “identification experience” contained in the index from the very beginning of its construction. This structure, combined with the transformer structure, can build a highly transferable model using only a small number of samples. In this study, experiments were conducted over a large area from 2018 to 2021, the training data only used samples from 2021. In comparison with seven traditional methods, for the 2021 test data, Transferability-Index achieved an overall accuracy of 92.50%, which is higher than other methods. For the 2018–2020 test data, Transferability-Index achieved 87.75%, 88.75% and 89.38%, which is significantly ahead of the compared methods. The high overall accuracy and stable performance of Transferability-Index demonstrate its strong transferable capabilities in paddy field identification. The Transferability-Index model can serve as a potent tool for paddy field mapping applications, especially when training data are scarce.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110790"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702511","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":"Fluorescence-based sensing for leaf nicotine prediction, nitrogen estimation and variable rate fertilization of tobacco","authors":"Lorenza Tuccio ,&nbsp;Stefania Matteoli ,&nbsp;Emanuele Ranieri ,&nbsp;Sara Antognelli ,&nbsp;Marco Miserocchi ,&nbsp;Guido Fastellini ,&nbsp;Enrica Bargiacchi ,&nbsp;Gilberto Milli ,&nbsp;Sergio Miele ,&nbsp;Linda Franceschetti ,&nbsp;Giovanni Agati","doi":"10.1016/j.compag.2025.110778","DOIUrl":"10.1016/j.compag.2025.110778","url":null,"abstract":"<div><div>Sustainable and low-nicotine production of tobacco requires rapid and accurate on-site assessment of the leaf nitrogen (N) status. This issue can be supported by fluorescence-based sensors, which are promising tools for precision N management.</div><div>We then aimed to 1) evaluate the suitability of the Multiplex® fluorescence sensor (Mx) to predict, at an early stage, the final nicotine content of tobacco leaves; 2) develop a model for in-season tobacco foliar N estimation using the Partial Least Square (PLS) multivariate regression technique; and finally, 3) test the effectiveness of a Mx map-based Variable Rate Nitrogen Fertilization (VRNF) in reducing the spatial variability in leaf Nitrogen Balance Index (NBI), that is the N status, of a commercial field of Virginia Bright tobacco.</div><div>The NBI measured by the Mx about two months after transplanting was found to linearly relate to the nicotine content measured after curing (R² = 0.72, <em>P</em> &lt; 0.001) over a nicotine range of 0.25 – 4.12 %. NBI, defined as the ratio between the leaf chlorophyll (SFR_R) and Flavonoids (FLAV) indices better related to nicotine than the single SFR_R and FLAV indices (R² = 0.47, <em>P</em> &lt; 0.001 and R² = 0.52, <em>P</em> &lt; 0.001, respectively. Furthermore, the NBI estimated the actual leaf N content before flowering better (R² = 0.33) than single SFR_R and FLAV indices (R² = 0.28), over a range of 21 – 37.6 mgg⁻¹.</div><div>Leaf fluorescence sensor indices were thus combined with growth stages and weather variables across diverse varieties and sites. The resulting PLS model successfully predicted leaf N (R² = 0.72, RMSEP = 2.73 mgg⁻¹ and relMAE = 7.75 %) over a range of 20.6–28.0 mgg⁻¹. The most significant variables, primarily related to solar radiation, were identified for a robust general model development.</div><div>Finally, the spatial pattern of the NBI was mapped over a 2.04 ha commercial plot of the ITB 6118 variety, and used to produce a three-zone prescription map. Two weeks after the intervention of VRNF based on the defined prescription map, the overall NBI variability had dropped from 23.5 % coefficient of variation (CV) to 7.9 % CV.</div><div>Our results show the feasibility of using the Mx sensor for precision fertilization of Virginia Bright tobacco and highlight its potential to support future developments aimed at more sustainable production of plants with reduced nicotine content.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110778"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702512","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":"Monitoring the growth status of winter wheat by using the machine learning algorithm and the fusion of spectral and texture features derived from the UAV remote sensing","authors":"YiMing Su ,&nbsp;XiaoBin Yan ,&nbsp;Hao Li ,&nbsp;ZiHao Liang ,&nbsp;ShuangMei Zhao ,&nbsp;Ping Chen ,&nbsp;Zhen Zhang ,&nbsp;XingXing Qiao ,&nbsp;Yu Zhao ,&nbsp;MeiChen Feng ,&nbsp;Fahad Shafiq ,&nbsp;XiaoYan Song ,&nbsp;LuJie Xiao ,&nbsp;WuDe Yang ,&nbsp;Chao Wang","doi":"10.1016/j.compag.2025.110758","DOIUrl":"10.1016/j.compag.2025.110758","url":null,"abstract":"<div><div>Remote sensing via unmanned aerial vehicle (UAV) could provide critical data support for estimating the real-time growth status of crops. In this study, the vegetation indexes (VI) and texture features (T) from multispectral images were extracted, and the entropy method was used to construct a comprehensive growth index (CGI) which ultimately reflected growth of winter wheat. Later on, the predictive models of winter wheat growth were established and evaluated by using the machine learning modeling methods of BPNN, RF and SVM with the different combinations of spectral and texture features. The results revealed that the correlation of CGI was improved compared with other single growth indicators, and it also reached a significant correlation level (r &gt; 0.6) with the texture features based on the red edge band. For different input variables, the CGI estimation accuracy for most models based on the combination VI and T were superior than that of VI or T alone with the mean R² = 0.858; while the average values of R² of the models based on VI and T alone were 0.825 and 0.774 respectively. It also indicated that fusion of the spectral and texture features improved predictive performance of winter wheat growth. Among all the crop growth indicators, the CGI achieved the best performance as well by using the RF and VI + T variable (R² = 0.888, RMSE = 0.041, RPD = 2.989), which confirmed the application potential of RF machine learning method in estimating the winter wheat growth. Lastly, it also proved the feasibility of constructing comprehensive indicators to monitor wheat growth by entropy method as the fact that the estimated results of CGI models were also better than most of the single growth indicators with the mean R² = 0.819 for all the CGI models. This study is expected to offer both theoretical and practical references for monitoring the growth of winter wheat through UAV-based multispectral technology.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110758"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702508","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 dual-level interactive cascade network for agricultural machinery trajectory operation mode identification","authors":"Weixin Zhai ,&nbsp;Xinyu Zhang ,&nbsp;Jinming Liu ,&nbsp;Jiawen Pan ,&nbsp;Caicong Wu","doi":"10.1016/j.compag.2025.110788","DOIUrl":"10.1016/j.compag.2025.110788","url":null,"abstract":"<div><div>Agricultural machinery trajectory operation mode identification refers to the process of using the spatiotemporal features in massive trajectory data to identify the operation mode of agricultural machinery and assign corresponding semantic labels to each unknown trajectory point. However, existing research has explored mainly the feature interactions between adjacent trajectory points in local areas and has failed to explore the dependencies of agricultural machinery trajectories in different ranges fully. To achieve efficient identification of the agricultural machinery trajectory operation mode, we propose a dual-level interactive cascade network (DIANet) for agricultural machinery trajectory operation mode identification. First, we design a multi-view feature extraction (MFE) module to quickly expand the size of the feature set of the trajectory, fully exploring the potential information of trajectory points from two different perspectives, physics and statistics. Second, to mine the dependencies of agricultural machinery trajectories in different ranges, we propose a dual-level interactive autoencoder (DIA), which consists of two modules: the information attention context module (IAC) and the neighborhood mining context module (NMC). Finally, we design a dual-masked self-supervised learning (DSL) module to pretrain the model to learn more general trajectory feature representations to improve the generalization ability of the model. To verify the effectiveness of the proposed model, our model was compared with other methods on two trajectory datasets provided by the Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs. The dataset covers a total of 180 trajectory samples and more than 1,000,000 trajectory points. On the paddy harvester and tractor trajectory datasets, our model achieved F1 scores of 90.74 % and 94.54 %, respectively, which are improvements of 6.15 % and 7.2 % compared with those of the current state-of-the-art methods.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"238 ","pages":"Article 110788"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703226","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":"Time-space-angle scale effects and incorporation patterns in estimating rice LAI and leaf chlorophyll content by UAV multispectral remote sensing","authors":"Shanjun Luo ,&nbsp;Qian Li ,&nbsp;Lei Du ,&nbsp;Zhaocong Wu","doi":"10.1016/j.compag.2025.110792","DOIUrl":"10.1016/j.compag.2025.110792","url":null,"abstract":"<div><div>Estimation of leaf area index (LAI) and leaf chlorophyll content (LCC) of rice is of great scientific and practical value for precision agriculture and ecological research, and unmanned aerial vehicle (UAV) remote sensing technology offers an effective monitoring resource. Aiming at the current problems of time–space-angle scale effects and unclear fusion patterns, intensive UAV observations in the field were designed in this paper to acquire different time–space-angle multispectral data (observation altitude range of 50–250 m, time range of 9:00–16:00 local time, and the angles including east, west, south, north, and vertical perspectives) for rice in 12 periods. Through analyzing the effects of canopy normalized difference red edge index (NDRE) variations and time–space-angle scales on the accuracy of LAI and LCC estimation during typical rice periods, it was determined that the utilization of NDRE derived from strong sunlights (backscattering direction) is more conducive to the estimation of rice LAI, whereas NDRE from mild sunlights is more appropriate for the rice LCC estimation. Multi-period rice LAI and LCC were estimated using a deep regression model with multihead attention mechanisms (MHAR) through position embedding and understanding of global and local information. The results demonstrated that the highest model accuracy was achieved by the variable inputs of optimal strategy (the coefficients of determination (R²) = 0.89, the root mean square error (RMSE) = 1.29, relative RMSE (RRMSE) = 16.78 % for LAI estimation and R² = 0.85, RMSE = 2.06, RRMSE = 5.46 % for LCC estimation), significantly higher than shallow machine learning and deep neural network (DNN) models. Furthermore, the addition of other vegetation indices inputs did not substantially improve the model accuracy. The proposed time–space-angle fusion model provides valuable insights for UAV remote sensing crop monitoring.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110792"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702510","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":"Multimodal information fusion and precision harvesting system for fruit growth driven by flexible optoelectronic sensing and hierarchical attention networks","authors":"Wenhao He ,&nbsp;Wentao Huang ,&nbsp;Yingsheng Li ,&nbsp;Nedeljko Latinović ,&nbsp;Yongjun Zhang ,&nbsp;Xiaoshuan Zhang","doi":"10.1016/j.compag.2025.110784","DOIUrl":"10.1016/j.compag.2025.110784","url":null,"abstract":"<div><div>To enhance fruit yield and quality, this study focuses on precise pre-harvest ripeness assessment and early disease detection. Addressing the limitations of conventional methods, we propose a multimodal flexible sensing and deep learning-based evaluation framework. The developed flexible optoelectronic in-situ sensing system integrates spectral (410–940 nm, 18 channels) and impedance (100  Hz–10  kHz) detection, allowing conformal attachment to mango surfaces for nondestructive monitoring throughout the growth cycle while collecting spectral, impedance, and physicochemical data. The proposed 1DCNN-ATT-BiLSTM-ATT network employs independent branches to extract local features from each modality, followed by attention mechanisms and temporal modelling for comprehensive feature fusion, achieving 97.5 % accuracy on test sets. Field experiments reveal systematic variations in soluble solid content (SSC), moisture content (MC), and optoelectronic signals during ripening. Correlation and Granger causality analyses underscore the necessity of multimodal fusion. This system supports intelligent harvesting and precision monitoring, advancing agricultural practices toward greater efficiency and sustainability while establishing a technical paradigm for precision agriculture. Future work will focus on improving environmental robustness and cross-cultivar applicability.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"238 ","pages":"Article 110784"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703927","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":"An underwater image segmentation model for complex scenes in aquaculture using vision Transformer","authors":"Dashe Li ,&nbsp;Siwei Zhao ,&nbsp;Jingzhe Hu ,&nbsp;Yufang Yang ,&nbsp;Jinqiang Ding","doi":"10.1016/j.compag.2025.110764","DOIUrl":"10.1016/j.compag.2025.110764","url":null,"abstract":"<div><div>Underwater image processing holds significant importance for aquaculture. Traditional segmentation models have primarily been trained and tested in terrestrial environments. However, substantial differences exist between underwater and terrestrial environments, particularly in lighting conditions and color distribution, and other underwater physical phenomena. The unique underwater conditions render traditional segmentation models unsuitable for direct application, and obtaining ample data becomes challenging. Consequently, underwater image segmentation techniques encounter issues of diminished segmentation performance in practical applications. Therefore, this paper constructs an underwater image segmentation model for complex scenes in aquaculture. This paper first proposes a data preprocessing method based on Mosaic data enhancement technology to process limited datasets and increase their richness and diversity. Second, a residual feature enhancement module is constructed to capture global context information and enhance local feature extraction so local and global dependencies are effectively combined. Finally, a multiscale feature residual fusion module is proposed, which uses residual convolution to enhance the efficient fusion of low- and high-level semantic features and solves the semantic gap between the encoder and decoder. This paper conducts extensive experiments on the three datasets FishData, the underwater images segmentation dataset, and the large-scale fish dataset to verify the effectiveness of the underwater image segmentation model proposed in this paper. Compared with the existing classical network model, the <span><math><mrow><mi>M</mi><mi>I</mi><mi>o</mi><mi>U</mi></mrow></math></span>, <span><math><mrow><mi>A</mi><mi>c</mi><mi>c</mi></mrow></math></span>, <span><math><mrow><mi>C</mi><mi>P</mi><mi>A</mi></mrow></math></span> and <span><math><mrow><mi>F</mi><mn>1</mn><mo>−</mo><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></math></span> of the proposed model reached 92.20%, 97.85%, 92.47% and 92.9%, respectively. Experimental results show that the proposed model performs better in underwater image segmentation tasks.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"238 ","pages":"Article 110764"},"PeriodicalIF":7.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703928","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":"Screening Verticillium wilt-resistant germplasm by monitoring the time-series chlorophyll content of cotton canopies via a UAV-based high-throughput platform","authors":"Bowei Xu ,&nbsp;Jiajie Yang ,&nbsp;Deyong Chen ,&nbsp;Xuwen Wang ,&nbsp;Xiantao Ai ,&nbsp;Le Liu ,&nbsp;Rumeng Zhao ,&nbsp;Jieyin Chen ,&nbsp;Xiaomei Ma ,&nbsp;Fuguang Li ,&nbsp;Zuoren Yang ,&nbsp;Liqiang Fan","doi":"10.1016/j.compag.2025.110791","DOIUrl":"10.1016/j.compag.2025.110791","url":null,"abstract":"<div><div>Verticillium wilt (VW) is a highly detrimental disease of cotton that causes significant reductions in yield and fiber quality. Efficient and accurate screening of VW-resistant varieties is essential for cotton breeding and production. However, traditional identification methods, such as manual observation, are inefficient and costly. Unmanned aerial vehicle (UAV) and remote sensing technologies have opened new insights into the screening of field crops for disease-resistant germplasm. This study utilized a UAV multispectral platform to collect data from five growth stages of 150 cotton varieties with different VW resistances. The normalized difference vegetation index (NDVI) was identified as a reliable predictor of chlorophyll levels through hierarchical segmentation analysis. We further compared four deep learning models for chlorophyll monitoring: 1D-CNN, CNN-BiLSTM, CNN-BiLSTM-Adaboost, and CNN-BiLSTM-Attention, with the CNN-BiLSTM-Attention model performing best (R² = 0.92). The optimum model was then used to invert the extent of VW infection using single- and multi-period chlorophyll, and the latter was found to have the best results with the highest R² value of 0.96. Multidimensional clustering of chlorophyll content over multiple periods was used to screen different cotton VW-resistant germplasm, and the ISODATA cluster method outperformed the other three methods (K-means, K means++, and GMM). This study highlights that combining a UAV multispectral platform with an accurate chlorophyll inversion model can enable high-throughput assessment of the cotton VW infection in the field, providing a powerful tool for screening cotton VW-resistant germplasm and thus supporting cotton breeding efforts.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"238 ","pages":"Article 110791"},"PeriodicalIF":7.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695338","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":"Standard nutrient-solution-based fish-tail-water-fertilizer machine: Design, control, and decision","authors":"Xiangnan Li ,&nbsp;Haitao Wang ,&nbsp;Xiaoling Yang ,&nbsp;Zhao Li ,&nbsp;Yanru Cheng ,&nbsp;Jishu Zheng ,&nbsp;Xin Han ,&nbsp;Jiandong Wang","doi":"10.1016/j.compag.2025.110774","DOIUrl":"10.1016/j.compag.2025.110774","url":null,"abstract":"<div><div>Although using fish tail water as a replacement for nitrogen fertilizers has excellent application prospects, the nitrogen content of fish tail water fluctuates greatly and is uncertain. Therefore, realizing intelligent allocation and organic substitution under dynamic nitrogen changes in fish tail water is pivotal for its application. This study proposes a nitrogen-replacement-fertilizer-dispensing strategy for fish tail water by using a standard nutrient solution as the reference. To this end, an intelligent fertilizer-dispensing machine based on the dynamic intelligent fertilizer-dispensing requirements is designed. The effects of three parameters, i.e., the inlet pipe flow of fish tail water, concentration ratio, and suction flow rate, of the proposed machine on the electrical conductivity (EC) stability time and liquid-level target time of the nutrient solution are investigated using a response surface method, and a double-objective function decision model is established. Results show that the root mean square error and mean square error of the nutrient- and reference-solution EC values based on the configuration of the machine are less than 0.07246 ms/cm. This validates the proposed nitrogen-replacement strategy for fish tail water. Moreover, the EC stability time and liquid-level target time are significantly affected by the three parameters, among which the fertilizer absorption flow rate and fish tail water inlet pipeline flow are identified as the most critical parameters. Subsequently, the optimal working parameters of the intelligent fertilizer-distribution machine are obtained using the established response surface model, as follows: the inlet pipe flow of fish tail water = 1 m³/h, concentration ratio = 66, and fertilizer absorption flow = 102 L/h. Thus, this study aids in realizing the efficient use of fish tail water and organic substitution of nitrogen fertilizer.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"238 ","pages":"Article 110774"},"PeriodicalIF":7.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695339","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}