Weed Detection: A Vision Transformer Approach For Soybean Crops

Unwanted plants called weeds grow among agricultural crops, competing with them for nutrients, water, and sunshine, resulting in severe output losses. Machine learning algorithms, particularly Deep Learning models, have automated the weed detection process. By leveraging annotated image datasets, these algorithms can accurately classify and distinguish weeds from crops. They have the potential to develop real-time, autonomous weed detection systems, empowering farmers to make informed decisions regarding weed control measures. This study suggests using Vision Transformers to classify and identify weeds in soybean farms. Recently, the significance of Vision Transformers in the area of Computer Vision has grown as a result of their capability to identify remote dependencies in images. The method suggested in this paper makes use of Vision Transformer's benefits by using a Deep Learning framework, which improves the precision of weed identification and categorization. This study’s dataset comprises of 15,336 images encompassing soil, grass weeds, broadleaf weeds, and soybean crops. The experimental analysis of the proposed approach shows that this approach outperforms a number of cutting-edge techniques for the identification and categorization of weeds. The accuracy of the proposed approach is 98.83%. This accuracy outperforms that of other methods, such as Convolutional Neural Networks (CNNs), Support Vector Machines, and other Deep Learning models, by a significant margin. The proposed method has the capacity to be used to enhance the accuracy and dependability of weed identification and categorization in many different crops, not just soybean. Consequently, these findings can lead to improved weed management strategies and increased crop yield.