Exploring Deep Learning architectures for Crop and Weed Identification

In agriculture, weeds always prove to be a major threat. It is tedious to do weeding at a later stage when the crops and the weeds are significantly grown. Incorporating technology in agriculture has revolutionized both industries in many ways. In the AI approach towards agriculture, neural networks are employed for identifying and classifying crops and weeds. However, what features they learn, how they learn the difference between crops and weeds is what we need to investigate. Many vegetable crop seedlings and weeds look similar, so a significantly large amount of data is necessary for the model to classify them. In this paper we are using CNN (Convolutional Neural Networks) and Efficientnet architectures to classify the crops and weeds. We further analyze the computational efficiency and model complexity of the architectures, providing insights into their practical applicability in real-world agricultural settings. This helps in better understanding of deep learning techniques and selecting features to perform this task. We have obtained an accuracy of 91% for CNN architecture and 99% for both B3, B4 of EfficientNet Architecture.