A Review of Application of Deep Learning for Weeds and Crops Classification in Agriculture

Abstract

Weeds are major cause due to which farmers get poor harvest of crops. Many algorithms are developed to classify weeds from crops to autonomously destroy weeds. Color-based, threshold-based and learning-based techniques are deployed in the past. From all techniques, deep-learning-based techniques stand out by showing the best performances. In this paper, deeplearning-based techniques are reviewed in the case where these are applied for weed detection in agricultural crops. Sunflower, carrot, soybean, sugar beet and maize are reviewed with respect to the weeds present in them. Deep learning structures and parameters are presented, and research Gaps are identified for further research.