Automated Weed Detection in Crop Fields Using Convolutional Neural Networks: A Deep Learning Approach for Smart Farming

Abstract

Deep learning is a part of modern machine learning that includes deep belief networks, deep neural networks, and recurrent neural networks. Computer vision, audio processing, and language comprehension are the most important sectors of deep learning. In many instances, these applications exceed human performance. In smart agriculture, deep learning gives novel ideas for increasing productivity and efficiency. Weed identification is an important application in crop areas that improves farming. This technology improves crop yields by identifying weeds. Also, it reduces resource wastage in agricultural practices. This paper presents a Convolutional Neural Network (CNN) model specifically designed to accurately identify and classify weeds using images of crop fields, augmented by the ImageNet dataset for enhanced feature extraction and model training. The model identifies essential characteristics, such as dimensions, form, spectral reflectance, and texture, to distinguish between crops and weeds. Unlike existing systems, our CNN-based approach achieves a high accuracy of 98%. This improvement enhances weed identification efficiency and reduces pesticide usage, therefore it minimising environmental impact.