Deep learning for crop classification: a comprehensive study

Abstract

Agriculture forms the backbone of many economies, and the integration of modern technologies into farming practices has the potential to revolutionize crop monitoring, classification, and weed management. This study concentrates on employing advanced deep learning methods to accurately classify crop types and detect weeds, a vital task in precision agriculture. Conventional crop classification techniques depend largely on manual inspection, which is labor-intensive and susceptible to human errors. To overcome these limitations, the research investigates a range of deep learning models, both custom-designed and pretrained, to automate and improve the classification process. A custom Convolutional Neural Network (CNN) was developed from scratch, consisting of four convolutional layers followed by a fully connected network. In addition, transfer learning approaches were employed using pretrained architectures such as VGG16, InceptionV3, and Vision Transformer (ViT). These models were evaluated based on metrics such as precision, accuracy, F1-score, and recall to assess their effectiveness in multi-class classification.