Khoza, Nokuthula GKhosa, Marshal VMahlangu, Thabo VNdlovu, Nkosinathi2023-01-272023-01-272022-08Khoza, N.G., Khosa, M.V., Mahlangu, T.V. & Ndlovu, N. 2022. Plant seedling classification using machine learning. http://hdl.handle.net/10204/12594 .978-1-6654-8422-0978-1-6654-8421-3978-1-6654-8423-7DOI: 10.1109/icABCD54961.2022.9856067http://hdl.handle.net/10204/12594Precision agriculture is a farming approach that uses artificial intelligence and information technology to improve crop yield, preserve the environment and maximize profits. Farmers need to follow precision agriculture to improve their crop quality and production. Weed control is one of the challenges that agriculture faces. The growth of weed leads to a decrease in crop yield and to prevent that, weed must be identified and achieved earlier to avoid the adverse effects on the crops. Applying deep learning techniques has become an important field of study in precision agriculture. In this paper, we presented two deep learning models to classify crops and weeds in their early growth stages. From the comparison of the two models ResNet50 and MobileNetV2, MobileNetV2 with 500×500 pixel size gave the best performing results with average f1-score of 88% and accuracy score of 88% which shows that this deep learning model can successfully classify 12 segmented plant seedlings in their early growth stages and this tool can be useful to farmers in identifying weedsAbstractenArtificial intelligenceBig dataComputational modelingCrops productionData modelsDeep learningPlant seedling classificationPrecision agricultureConference PresentationKhoza, N. G., Khosa, M. V., Mahlangu, T. V., & Ndlovu, N. (2022). Plant seedling classification using machine learning. http://hdl.handle.net/10204/12594Khoza, Nokuthula G, Marshal V Khosa, Thabo V Mahlangu, and Nkosinathi Ndlovu. "Plant seedling classification using machine learning." <i>2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, 4-5 August 2022</i> (2022): http://hdl.handle.net/10204/12594Khoza NG, Khosa MV, Mahlangu TV, Ndlovu N, Plant seedling classification using machine learning; 2022. http://hdl.handle.net/10204/12594 .TY - Conference Presentation AU - Khoza, Nokuthula G AU - Khosa, Marshal V AU - Mahlangu, Thabo V AU - Ndlovu, Nkosinathi AB - Precision agriculture is a farming approach that uses artificial intelligence and information technology to improve crop yield, preserve the environment and maximize profits. Farmers need to follow precision agriculture to improve their crop quality and production. Weed control is one of the challenges that agriculture faces. The growth of weed leads to a decrease in crop yield and to prevent that, weed must be identified and achieved earlier to avoid the adverse effects on the crops. Applying deep learning techniques has become an important field of study in precision agriculture. In this paper, we presented two deep learning models to classify crops and weeds in their early growth stages. From the comparison of the two models ResNet50 and MobileNetV2, MobileNetV2 with 500×500 pixel size gave the best performing results with average f1-score of 88% and accuracy score of 88% which shows that this deep learning model can successfully classify 12 segmented plant seedlings in their early growth stages and this tool can be useful to farmers in identifying weeds DA - 2022-08 DB - ResearchSpace DP - CSIR J1 - 2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, 4-5 August 2022 KW - Artificial intelligence KW - Big data KW - Computational modeling KW - Crops production KW - Data models KW - Deep learning KW - Plant seedling classification KW - Precision agriculture LK - https://researchspace.csir.co.za PY - 2022 SM - 978-1-6654-8422-0 SM - 978-1-6654-8421-3 SM - 978-1-6654-8423-7 T1 - Plant seedling classification using machine learning TI - Plant seedling classification using machine learning UR - http://hdl.handle.net/10204/12594 ER -26208