Weeds and Crop Image Classification using Deep Learning Technique

Growth of weeds in the agriculture land decrease the yield of the farm land. The automatic methods are very useful to detect the weeds and crops. Deep learning methods are being employed in a wide range of applications in agriculture and farming. In order to effectively manage weeds and increase yields, automatic weed detection and classification can be very helpful. Precision farming has the ability to identify weeds within the crop. Weed absorbs the nutrients, present in it and affects the crop. A key aspect in weed management is to contribute for better yields. The aim of the paper is to use deep learning techniques to increases the accuracy of weed detection and decrease farmer’s workload. In this work, classification of soybean crops and weeds (broadleaf, soil, grass) is done by using customized CNN approach. The proposed weeds and crop classification produced reliable results in terms of classification accuracy.