Deep Learning Model for Early Weed Detection in Agriculture Application

Abstract

One of the current issues in agriculture is the lack of mechanized weed management, which is why weed detection technologies are so crucial. Detecting weeds is useful because it may lead to the elimination of pesticide usage, which in turn improves the surroundings, human health, and the sustainability of agriculture. As novel algorithms are developed and computer capacity increases, deep learning-based approaches are gradually replacing classic machine learning methods for real-time weed detection. Mixed machine learning designs, which combine the best features of existing approaches, are becoming more popular. So, the goal of this study, present the CNN model for early weed detection. The CNN model is applied to the weed dataset. The CNN model achieved 96% accuracy.