Results in Control and Optimization, Год журнала: 2024, Номер 17, С. 100489 - 100489
Опубликована: Ноя. 8, 2024
Язык: Английский
Journal of Industrial Information Integration, Год журнала: 2024, Номер unknown, С. 100699 - 100699
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
3
Internet of Things, Год журнала: 2025, Номер 30, С. 101479 - 101479
Опубликована: Янв. 5, 2025
Язык: Английский
Процитировано
0
SN Computer Science, Год журнала: 2025, Номер 6(3)
Опубликована: Фев. 24, 2025
Язык: Английский
Процитировано
0
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 13547 - 13557
Опубликована: Янв. 1, 2024
As a crucial economic crop, the health status of cotton directly impacts farmers' income and national economy. Therefore, timely accurate detection identification diseases pests are significant importance, aiding in reducing adverse effects on yield quality. Existing research struggles to address balance between resource consumption accuracy disease pest detection. Moreover, often occur beneath canopy, orthorectification drone imagery may result insufficient feature information prolonged processing time, among other issues. To aforementioned issues, this paper proposes precise method for Verticillium wilt based unmanned aerial vehicle multi-angle remote sensing guided by satellite time series monitoring model. Specifically, first, combining Sentinel-1 microwave Sentinel-2 optical images, we constructed model extreme gradient boosting algorithm identify areas affected invasion. Subsequently, after identifying blocks disease, collected multi-spectral data captured from multiple angles vehicles compared different combinations vegetation indices bands. Finally, classification support vector machine radial basis function method. Experimental results indicate that joint time-series achieved OA 81.73% Kappa coefficient 0.63, meeting requirements first stage. Based SVM with RBF optimal band combination, value comprehensive image at -58° angle reached 96.74%, 0.93, second
Язык: Английский
Процитировано
1
Опубликована: Янв. 1, 2024
In the domain of efficient management resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting modern times. We used two types data: spatial data temporal data. Spatial are gathered from imagery processed using ArcGIS to extract about crops based on several indices like NDVI NWDI. Temporal agricultural sensors such temperature sensors, rainfall sensor, precipitation sensor soil moisture sensor. our case we Sentinel 2 vegetation indices. have IoT systems, especially Raspberry Pi B+ collect process coming sensors. All collected then stored into NoSQL server be analysed processed. Several machine learning deep algorithms been processing recommendation system, logistic regression, KNN, decision tree, support vector machine, LSTM, Bi-LSTM through dataset. Bi-GRU model best performance, RMSE this was 3.07.The main contribution paper is development new system that can predict yields, wheat, maize, etc, IoT, use first combined yield algorithms, unlike other works uses only remote sensing or created an E-monitoring prediction helps farmers track all information show result mobile application. This more making enhance production. production regions wheat Morocco rainfed areas plains plateaus Chaouia, Abda, Haouz, Tadla, Gharb Saïs. studied three well known which Rabat-Salé, Fez-Meknes, Casablanca-Settat.
Язык: Английский
Процитировано
0
Results in Control and Optimization, Год журнала: 2024, Номер 17, С. 100489 - 100489
Опубликована: Ноя. 8, 2024
Язык: Английский
Процитировано
0