Weeds Classification using Convolutional Neural Network Architectures

Abstract

Agriculture is an important sector for both human survival and economic growth. It has to be managed efficiently. This can be done by the use of technology in order to minimize human effort. It can be managed efficiently by following crop management tasks. One such crop management task is the identification and removal of weeds. Weeds are considered to be plants which are not required to be grown with the agricultural crops, because the weeds also utilize the water and nutrients like the agricultural crop and cause impact on the growth of agricultural crops. In order to identify weeds, deep learning technology can be used. The proposed system helps to classify weeds using Convolutional Neural Networks. This system employs models like, ResNet50, MobileNetV2 and InceptionV3, which are used for better classification. The system is evaluated based on these models, and all the three models have resulted in better accuracy.