Agriculture monitoring system based on internet of things by deep learning feature fusion with classification. (September 2022)

• Record Type: Journal Article
• Authors: Kumari, K. Sita; Haleem, S.L. Abdul; Shivaprakash, G.; Saravanan, M.; Arunsundar, B.; Pandraju, Thandava Krishna Sai
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: UAV -- Crop monitoring system -- IoT -- Live data -- Classification -- Machine Learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108197
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: This research aims to monitor the crop of remote areas where the cultivation is below average, and also, this can be able to oversee the climatic conditions of the region. This article proposes the crop monitoring system using machine learning-based classification using UAV. The data has been collected and classified for detecting crop abnormality based on climatic conditions and pre-historic data based on cultivation for the field also this monitoring system will differentiate the weeds and crops. The simulation results show accuracy, precision, specificity for trained data by detecting the crop abnormality. Abstract: This research proposed novel technique in crop monitoring system using machine learning-based classification using UAV. To monitor and operate activities from remote locations, UAVs extended their freedom of operation. For smart farming, it's significant to use UAV prospects. On the other hand, the cost and convenience of using UAVs for smart-farming may be a major factor in farmers' decisions to use UAVs in farming. The IoT-based module is used to update the database with monitored data. Using this method, live data should be updated soon, and it can help in crop cultivation identification. Research also monitor climatic conditions using live satellite data. The data is collected as well as classified for detecting crop abnormality based on climatic conditions and pre-historic data based on cultivation for the field also this monitoring system will differentiate weeds and crops. Simulation results show accuracy, precision, specificity for trained data by detecting the crop abnormality. Graphical abstract: Image, graphical abstract