Computers and Electronics in Agriculture最新文献

{"title":"A review of semantic segmentation methods and their application in apple disease detection","authors":"Masoumeh Keshavarzi ,&nbsp;Carl Mesarich ,&nbsp;Donald Bailey ,&nbsp;Martin Johnson ,&nbsp;Gourab Sen Gupta","doi":"10.1016/j.compag.2025.110531","DOIUrl":"10.1016/j.compag.2025.110531","url":null,"abstract":"<div><div>Semantic segmentation, with pixel-wise classification, enables the precise identification of different parts of plants, as well as the diseases that occur on them, in agricultural images. Apples, as one of the most important fruit crops worldwide, are susceptible to various diseases, causing decreased crop quality and increased crop loss. To prevent disease progression and ensure prompt treatment, semantic segmentation acts as an effective method in the context of apple disease detection. This review provides a comprehensive analysis of semantic segmentation methods applied in apple disease detection, ranging from traditional approaches to state-of-the-art techniques. By systematically examining the entire pipeline, from dataset preparation to the segmentation and evaluation stages, this work not only synthesises existing knowledge but also reviews applied solutions and highlights remaining research gaps to enhance segmentation performance. Additionally, it offers a forward-looking perspective by proposing future research directions. Overall, this review aims to advance plant disease detection through semantic segmentation, with a particular emphasis on apples.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110531"},"PeriodicalIF":7.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139581","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":"Quantifying the influencing factors and predictive analysis of cotton defoliation and maturation based on machine learning","authors":"Yukun Wang ,&nbsp;Chenyu Xiao ,&nbsp;Kexin Li ,&nbsp;Lu Meng ,&nbsp;Xinghu Song ,&nbsp;Haikun Qi ,&nbsp;Yao Wang ,&nbsp;Zhenwang Zhang ,&nbsp;Xinghua Yu ,&nbsp;Fangjun Li ,&nbsp;Sumei Wan ,&nbsp;Guodong Chen ,&nbsp;Dongyong Xu ,&nbsp;Xin Du ,&nbsp;Mingwei Du ,&nbsp;Xiaoli Tian ,&nbsp;Zhaohu Li","doi":"10.1016/j.compag.2025.110555","DOIUrl":"10.1016/j.compag.2025.110555","url":null,"abstract":"<div><div>Good defoliation and boll opening are essential for cotton mechanical harvesting. However, in actual production, many factors can affect the effect of defoliation and boll opening rate. Assessing the importance of factors affecting cotton defoliation and ripening, and predicting these factors, is critical to adjust the key influencing factors for achieving optimal defoliation and boll opening effect. This study, conducted from 2016 to 2022 across the three major Chinese cotton-producing regions – the Yellow River Valley, the Yangtze River Valley, and the Xinjiang Autonomous Region – aimed to evaluate the key factors influencing post-application defoliation and ripening processes and to develop models predicting defoliation and ripening progression using three machine learning methods: Random Forest, Support Vector Machines, and Gradient Boosting Machine. The results show that machine learning-selected variables accurately predict defoliation percentage (DP), boll opening percentage (BOP), and the increment of boll opening percentage (IBOP). Crucially for DP, considering the dosage of applied XSL, as well as other defoliants, is essential. Also important are the highest temperatures’ influence and pre-treatment boll opening percentage on defoliation in the Yellow River Valley and Xinjiang Autonomous Region. For BOP, enhancing the pre-treatment boll opening percentage is vital across all regions. Sunshine, humidity, and precipitation levels, especially in the Yellow River, Yangtze River, and Xinjiang Autonomous Regions, also require attention. Region-specific key factors affect IBOP; in the Yellow River Valley, these are minimum and average temperatures and pre-treatment boll opening percentage. In the Yangtze River Valley and Xinjiang Autonomous Region, the increment is mainly influenced by the pre-treatment boll opening percentage and the application of XSL. Having ranked the importance of influencing factors, these methods were applied to predict variables, with Random Forest demonstrating superior predictive performance (R² &gt; 0.7, rRMSE &lt; 15 %). In actual production, producers should pay special attention to the application of XSL to achieve effective defoliation and cultivate a favorable pre-treatment plant population structure to ensure a high boll opening rate post-treatment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110555"},"PeriodicalIF":7.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent survey method of rice diseases and pests using AR glasses and image-text multimodal fusion model","authors":"Xiangfu Chen ,&nbsp;Baojun Yang ,&nbsp;Yongjian Liu ,&nbsp;Zelin Feng ,&nbsp;Jun Lyu ,&nbsp;Ju Luo ,&nbsp;Jian Wu ,&nbsp;Qing Yao ,&nbsp;Shuhua Liu","doi":"10.1016/j.compag.2025.110574","DOIUrl":"10.1016/j.compag.2025.110574","url":null,"abstract":"<div><div>The timely and accurate detection of the occurrence types and dynamics of rice diseases and insect pests in the field is a fundamental requirement for effective scientific prevention and control. Currently, survey methods rely heavily on the expertise and experience of surveyors, leading to challenges such as limited data traceability, high labor demands, and low efficiency. The complex environmental conditions in rice fields, coupled with the diversity of pests and diseases—many of which coexist and exhibit significant intraspecies variation and interspecies similarities—further complicate detection efforts. When identification models are trained using only a limited set of image samples, they often suffer from poor generalization, undermining the accuracy of pest and disease forecasts. To overcome these challenges, a rapid, efficient, and precise intelligent survey method using AR glasses and image-text multimodal fusion model to detect rice pests and diseases was proposed. The AR glasses have advantages of wearable, hands-free, voice-control functions, which is very convenient to collect rice images in paddy fields. An image-text multimodal fusion model with two stages, RDP-Detector, was developed to improve the detection accuracy rates of rice pest and disease lesions in the images. In the first stage, the improved YOLOv5X model with AF-FPN, Decoupled Head and Soft-NMS post-processing achieved improvements in detection ability. In the second stage, text modalities are introduced, and Prompt tuning is used to perform transfer learning for downstream tasks on the basis of the CLIP model. To improve the accuracy of pest detection, the detection boxes with low confidence in the first stage are subjected to reidentification in the second stage. Compared with the state of the art models, the RDP-Detector achieved an precision, recall, and mAP of 82.3 %, 86.5 %, and 87.4 %, respectively, on the detection of seven rice pests and diseases. Compared with the object detection models that do not incorporate text modalities, the proposed approach demonstrated a 14.6 percentage point improvement in precision. The intelligent survey method for rice pests and diseases established in our study, which using AR glasses and a multimodal model, represents a highly effective innovation. The method not only enhances survey efficiency but also reduces reliance on the professional expertise of surveyors, while achieving high accuracy in pest and disease identification.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110574"},"PeriodicalIF":7.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125398","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":"MixSegNext: A CNN-Transformer hybrid model for semantic segmentation and picking point localization algorithm of Sichuan pepper in natural environments","authors":"Pengjun Xiang ,&nbsp;Fei Pan ,&nbsp;Tao Liu ,&nbsp;Xiaoyu Zhao ,&nbsp;Mengdie Hu ,&nbsp;Dawei He ,&nbsp;Boda Zhang","doi":"10.1016/j.compag.2025.110564","DOIUrl":"10.1016/j.compag.2025.110564","url":null,"abstract":"<div><div>Precise identification of Sichuan pepper picking points is a prerequisite for the robotic harvesting of the crop. Picking robots typically operate in open, dynamic natural environments, which demands robustness in the Sichuan pepper picking point localization algorithm. Generally, the growth environment of Sichuan pepper is complex, and the growth posture varies. The branches of the pepper clusters are similar to the pepper branches, which can easily lead to misjudgment and omission in the localization process, making accurate visual picking point localization challenging. To rapidly and accurately locate target Sichuan pepper picking points in natural environments, this paper proposes a Sichuan pepper segmentation model and picking point localization algorithm based on MixSegNext. The algorithm is divided into three main parts. First, the MixSegNext network performs semantic segmentation on Sichuan pepper clusters and fruits to extract the picking targets. Then, by subtracting the extracted pepper fruit mask from the pepper cluster mask, the Sichuan pepper branch mask is obtained, and the main pepper branch mask is acquired through morphological operations and maximal connectivity analysis. Finally, edge extraction is performed on the main pepper branch mask, and the picking point is determined by finding the intersection between the central line of the contour and the edge. This paper compares MixSegNext with typical semantic segmentation networks and conducts picking point localization experiments. The results show that the network has better segmentation precision and high picking point localization accuracy. Furthermore, this paper deploys the network on embedded devices to perform Sichuan pepper inference segmentation, verifying the application effect of the algorithm, which can provide a reference for the visual positioning system of Sichuan pepper-picking robots.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110564"},"PeriodicalIF":7.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125331","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 double seed-taking cotton precision dibbler for resource-saving agriculture: A further research","authors":"Zibin Mao ,&nbsp;Bin Hu ,&nbsp;Luochuan Xu ,&nbsp;Mengyu Guo ,&nbsp;Junwei Li ,&nbsp;Xin Luo","doi":"10.1016/j.compag.2025.110584","DOIUrl":"10.1016/j.compag.2025.110584","url":null,"abstract":"<div><div>Aiming at the problem that the development of drip irrigation technology under a membrane is limited by the low operation speed (less than 4 km/h) of the machine used in membrane systems, this study started from the fact that the cottonseed cannot be separated and migrated from the population in time due to the seed flow disorder during the seed-filling process. Based on the TRIZ theory, a cotton precision dibbler with double seed-taking was developed. Improve seeding speed effectively under the premise of meeting the needs of seeding. Based on the established mechanical model of the target cottonseed filling hole during double seed-taking, the effects of enforced filling angle and greet filling angle on the local seed cluster and the migration and transport of target cottonseed were analyzed. Test schemes such as the Plackett-Burman and Box-Behnken response surface tests were designed, and the parameters were optimized through numerical simulation. The optimal parameter combination was as follows: When the rotational speed was 43 r/min (corresponding to the seeding speed of 4.14 km/h), the thickness of the seed tray was 6.69 mm, and the enforced filling angle was 40.73°, the qualified index was 88.19 %, the overfilled index was 10.02 %, and the leakage index was 1.8 %. Under these conditions, using the high-speed camera technology and the bench comparison experiment of two kinds of seed trays, the seed-filling performance of the oval pit tray is better than that of the plane pit, meeting the requirements of cottonseeds’ clean production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110584"},"PeriodicalIF":7.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131214","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":"Segmentation of FHB infection in wheat ear using super-resolution of UAV image and reception enrichment gate network","authors":"Shizhuang Weng ,&nbsp;Yulong Liu ,&nbsp;Yehang Wu ,&nbsp;Tianle Liu ,&nbsp;Min Fan ,&nbsp;Shouguo Zheng ,&nbsp;Yahui Su","doi":"10.1016/j.compag.2025.110552","DOIUrl":"10.1016/j.compag.2025.110552","url":null,"abstract":"<div><div><em>Fusarium</em> head blight (FHB) is one of the most serious wheat diseases and mainly infects the ear, affecting the yield and quality of wheat worldwide. Segmentation of FHB infection in wheat ear based on unmanned aerial vehicle (UAV) images is feasible and significant in ensuring timely control measures and maintaining food security. The high flight altitude of UAV allows for rapid image acquisition but results in blurred textures and details, and the variability of field environment leads to missed and false segmentation. To address these problems, we first executed the super-resolution (SR) of high-altitude UAV images, and then FHB infection was segmented using a deep gate network. Specifically, an SR network called hierarchical context aggregation network (HCAN) was developed to generate clear textures and detailed characteristics of wheat efficiently through the successive fusion of various contexts. HCAN was superior to the current state-of-the-art methods with a peak signal-to-noise ratio of 29.056 dB and a structural similarity index of 0.9142. Meanwhile, a reception enrichment gate network (REGN) was applied to segment FHB infection in wheat ear through the integration of dual-gate mechanism and multi-scale convolution. REGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %. Indistinct edges, missed segmentation, and false segmentation were dramatically alleviated in high-density, overlapping, shaded and overexposed wheat because local and neighboring gate operations enhanced the representation and reception field, and multi-scale convolution could enrich the reception diversity. In sum, the proposed approach provided a reliable, efficient, and accurate determination of FHB infection in wheat on the basis of UAV images and could be extended to the analysis of other diseases or crops.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110552"},"PeriodicalIF":7.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115462","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":"Transient sound signal analysis for watermelon ripeness detection using HHT and NMF","authors":"Yijie Li ,&nbsp;Chunhao Cao ,&nbsp;Mengke Cao ,&nbsp;Wenchuan Guo","doi":"10.1016/j.compag.2025.110543","DOIUrl":"10.1016/j.compag.2025.110543","url":null,"abstract":"<div><div>Harvesting watermelon at an inappropriate time can significantly impact its quality and flavor. To ensure rapid, reliable, and nondestructive determination of watermelon ripeness, this study focuses on analyzing the tapping sound of watermelons at various ripeness levels. The tapping sound, characterized as a transient acoustic signal, exhibits consistent resonance properties but varying frequency features across ripeness stages. A sound processing method was developed by integrating Nonnegative Matrix Factorization (NMF) filtering and Root-Mean-Square (RMS) normalization. Frequency characteristics and variations in watermelon tapping sounds were analyzed using the Hilbert-Huang Transform (HHT) and NMF-based feature extraction. Machine learning models, including Support Vector Machine (SVM), HHT combined with SVM (HHT + SVM), and NMF combined with SVM (NMF + SVM), were employed to classify watermelons of different ripeness levels. Experimental results, based on 100 samples each of unripe, ripe, and overripe watermelons, showed a gradual decrease in the average frequency distribution of tapping sounds from unripe to overripe stages. The classification accuracy of watermelon ripeness using SVM alone was 62.78 %, which improved to 74.44 % with HHT + SVM and further increased to 92.22 % with NMF + SVM. These findings demonstrate that feature extraction methods based on NMF and HHT effectively capture the frequency characteristics and time-decay properties of transient acoustic signals. This study offers an efficient and practical method for acoustic nondestructive detection of watermelon ripeness, providing a novel approach for processing transient and abrupt sound signals with broad potential applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110543"},"PeriodicalIF":7.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115460","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":"Crop height retrieval from polarimetric SAR data using machine learning: A comparative and validation study","authors":"Shuaifeng Hu ,&nbsp;Qinghua Xie ,&nbsp;Xing Peng ,&nbsp;Qi Dou ,&nbsp;Jinfei Wang ,&nbsp;Juan M. Lopez-Sanchez ,&nbsp;Jiali Shang ,&nbsp;Haiqiang Fu ,&nbsp;Jianjun Zhu ,&nbsp;Wenming Zhou","doi":"10.1016/j.compag.2025.110580","DOIUrl":"10.1016/j.compag.2025.110580","url":null,"abstract":"<div><div>Accurate and efficient crop height information retrieval is crucial for applications such as farmland management, growth monitoring, yield estimation, and pest monitoring. Polarimetric Synthetic Aperture Radar (PolSAR) is known for its high sensitivity to the shape, structure, and dielectric constant of vegetation, presenting great potential for crop height retrieval. In this study, we compare the performance of three machine learning algorithms, Random Forest Regression (RFR), Bagging Decision Tree (BAGTREE), and Extreme Gradient Boosting (XGBoost), in the retrieval of crop height from PolSAR data. Using a comprehensive approach, we constructed a set of 32 polarimetric features as the initial input for the model. Subsequently, feature selection is employed to generate a subset aimed at reducing redundancy and improving the final estimation accuracy. Multi-temporal C-band PolSAR RADARSAT-2 data collected over three distinct agricultural types (corn, wheat, and soybean) in Canada are chosen for this study. The results indicate that the optimal average Root Mean Square Error (RMSE) for height retrieval in corn, wheat, and soybean throughout their growth cycles is 43.69 cm, 10.78 cm, and 20.92 cm, respectively. Among the three algorithms, RFR consistently demonstrates stable retrieval performance, and the polarimetric decomposition parameters exhibit the highest sensitivity to crop height. This study offers a valuable technical reference for SAR-based crop height retrieval and remote sensing-based crop growth monitoring without interferometry.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110580"},"PeriodicalIF":7.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125399","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 pig audio signal recognition via integrated underdetermined blind source separation and deep learning","authors":"Weihao Pan ,&nbsp;Yi Fang ,&nbsp;Xiaobo Zhou ,&nbsp;ShunPi Yan ,&nbsp;Jun Jiao ,&nbsp;Guodong Wu ,&nbsp;Cheng Zhu","doi":"10.1016/j.compag.2025.110511","DOIUrl":"10.1016/j.compag.2025.110511","url":null,"abstract":"<div><div>To address the challenges of separating and identifying pig vocalizations in group-housing environments, this study proposes a novel method for pig audio signal recognition based on underdetermined blind source separation and ECA-EfficientNetV2. Four types of pig vocalizations in a simulated group-housing environment were used as observed signals captured by recording devices. It estimates the observed signal mixed matrix by hierarchical clustering after signal sparse representation. The l_p norm reconstruction algorithm is used to solve the minimum l_p norm to complete the audio signal reconstruction of four kinds of pigs. The reconstructed signals are converted into spectrograms, which are composed of eating sound, howling sound and humming sound, and then identified the audio signals by ECA-EfficientNetV2 network model. The results show that the minimum normalized mean square error (NMSE) estimated by mixed matrix is 3.2660e-04, and the reconstructed audio signal-to-noise ratio (SNR) is 3.254–4.267 dB. The accuracy of ECA-EfficientNetV2 model in recognizing spectrograms is as high as 98.35 %, which is improved by 2.88 % and 1.81 % compared to lightweight convolutional neural networks MobileNetV2 and ShuffleNetV2, while the model parameters are reduced by 35.67 % compared to the original EfficientNetV2. The results indicate that the pig audio signal recognition method based on blind source separation improves EfficientNetV2, realizes the separation and recognition of the audio signals of herd pigs in a light and efficient manner.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110511"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115461","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":"Improved pest and disease spread model for simulating the spread of invasive species: A case study of pine wilt disease","authors":"Zhuoqing Hao ,&nbsp;Wenjiang Huang ,&nbsp;Biyao Zhang ,&nbsp;Yifan Chen ,&nbsp;Guofei Fang ,&nbsp;Jing Guo ,&nbsp;Yanru Huang ,&nbsp;Xiangzhe Cheng ,&nbsp;Bohai Hu","doi":"10.1016/j.compag.2025.110561","DOIUrl":"10.1016/j.compag.2025.110561","url":null,"abstract":"<div><div>Biological invasion is a major environmental issue facing the world today. Controlling invasive species has become a key focus of governments globally. Pine wilt disease (PWD) is a significant invasive species in China. This study examines the invasion and expansion processes of PWD and provides control strategies. Three regions in China with differing climatic, environmental, and socio-economic conditions were selected. Using real disaster records from 2018 to 2022, the K-means clustering method was applied to analyse the mechanisms of disease spread. Epidemic patches were classified into two groups: invasion-type and expansion-type. By considering both natural and human factors driving the invasion and expansion of PWD, the PoPS model was improved. The model was then used to analyse the effectiveness of control measures against PWD spread in China. The results indicate that the calculated boundary distances and the improved model effectively explained new outbreaks caused by the invasion and expansion processes. Simulation results, combined with China’s current control strategies, provide recommendations for disaster prevention. This study describes PWD spread as two processes: invasion and expansion. It reveals the transmission dynamics of PWD in China, offers a predictive model, and proposes control strategies. The findings provide scientific support for disaster prevention and research on invasive species.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110561"},"PeriodicalIF":7.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106576","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}