Weed Detection in Agricultural fields using Deep Learning Process

Abstract

Weeds are aggressive, computing for light, water, nutrients and space for crops, garden plants or lawn grass. Management of weeds usually consists of spraying herbicides in the entire agricultural sector. Most are fast growers and can take over many of the fields in which they are located. A fast-growing area of research today is artificial intelligence, specifically deep learning. Object recognition, making use of computer vision, is one of its numerous applications. This work suggests a deep learning with image processing-based framework to classify, various crops and weeds. A deep convolutional neural network (CNN) architecture is developed to implement this classification with improved accuracy by increasing the deep layers as compared to the existing CNN.