Abstract
Agriculture production is significantly affected by weeds. Improving production levels in agriculture is important, and getting only weeds sprayed accurately is crucial. Only weeds need to be sprayed accurately, while distinguishing them from crops. Several methods have been used by scholars in recent years to accomplish this goal. There are two approaches to solving weed detection problems: Traditional methods of classification and identification of images and deep learning methods. This review discusses both approaches, and their pros and cons. Recently, many methods for detecting weeds have been developed. This article reviews the methods that have been developed, as well as some pictures of related plant leaves. It also talks about the pros and cons of each method. Future prospects for weed detection research are discussed, as well as the problems with current methods.
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El Jgham, B., Abdoun, O., El Khatir, H. (2023). Review of Weed Detection Methods Based on Machine Learning Models. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_52
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