Sustainability, Volume 15, Issue 2
2023 January-2 - 853 articles
Cover Story: Drone images from a sugar beet field with a high diffusion of weeds were used to develop and validate a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing analysis, digital transformation by an image embedder, and evaluation by supervised learning. Six common machine learning algorithms were applied (logistic regression, k-nearest neighbours, decision tree, random forest, neural network, and support-vector machine) to precisely recognise crops and weeds throughout a wide cultivation field training from single partial images. The information has been designed to be easily integrated into autonomous weed management systems with the aim of reducing the use of water, nutrients, and herbicides for precision agriculture. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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