Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture

dc.authorid Khan, Muhammad/0000-0002-9195-3477
dc.authorid Alam, Muhammad Shahab/0000-0002-5653-2503
dc.authorwosid Khan, Muhammad/N-5478-2016
dc.authorwosid Alam, Muhammad Shahab/CAF-0412-2022
dc.contributor.author Alam, Mansoor
dc.contributor.author Alam, Muhammad Shahab
dc.contributor.author Roman, Muhammad
dc.contributor.author Tufail, Muhammad
dc.contributor.author Khan, Muhammad Umer
dc.contributor.author Khan, Muhammad Tahir
dc.contributor.other Mechatronics Engineering
dc.date.accessioned 2024-07-05T15:41:18Z
dc.date.available 2024-07-05T15:41:18Z
dc.date.issued 2020
dc.department Atılım University en_US
dc.department-temp [Alam, Mansoor; Roman, Muhammad; Tufail, Muhammad; Khan, Muhammad Tahir] Univ Engn & Technol, Dept Mechatron Engn, Peshawar, Pakistan; [Alam, Muhammad Shahab; Roman, Muhammad; Tufail, Muhammad; Khan, Muhammad Tahir] Natl Ctr Robot & Automat, Adv Robot & Automat Lab, Peshawar, Pakistan; [Khan, Muhammad Umer] Atilim Univ, Dept Mechatron Engn, Ankara, Turkey en_US
dc.description Khan, Muhammad/0000-0002-9195-3477; Alam, Muhammad Shahab/0000-0002-5653-2503 en_US
dc.description.abstract Traditional agrochemical spraying techniques often result in over or under-dosing. Over-dosing of spray chemicals is costly and pose a serious threat to the environment, whereas, under-dosing results in inefficient crop protection and thereby low crop yields. Therefore, in order to increase yields per acre and to protect crops from diseases, the exact amount of agrochemicals should be sprayed according to the field/crop requirements. This paper presents a real-time computer vision-based crop/weed detection system for variable-rate agrochemical spraying. Weed/crop detection and classification were performed through the Random Forest classifier. The classification model was first trained offline with our own created dataset and then deployed in the field for testing. Agrochemical spraying was done through application equipment consisting of a PWM-based fluid flow control system capable of spraying the desired amounts of agrochemical directed by the vision-based feedback system. The results obtained from several field tests demonstrate the effectiveness of the proposed vision-based agrochemical spraying framework in real-time. en_US
dc.identifier.citationcount 57
dc.identifier.doi 10.1109/iceee49618.2020.9102505
dc.identifier.endpage 280 en_US
dc.identifier.isbn 9781728167886
dc.identifier.startpage 273 en_US
dc.identifier.uri https://doi.org/10.1109/iceee49618.2020.9102505
dc.identifier.uri https://hdl.handle.net/20.500.14411/3441
dc.identifier.wos WOS:000717525500053
dc.institutionauthor Khan, Muhammad Umer
dc.language.iso en en_US
dc.publisher Ieee en_US
dc.relation.ispartof 7th International Conference on Electrical and Electronics Engineering (ICEEE) -- APR 14-16, 2020 -- Antalya, TURKEY en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Random forest classifier en_US
dc.subject variable-rate spraying en_US
dc.subject weed control en_US
dc.title Real-Time Machine-Learning Based Crop/Weed Detection and Classification for Variable-Rate Spraying in Precision Agriculture en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 70
dspace.entity.type Publication
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