A review of deep learning techniques used in agriculture

Abstract

Deep learning (DL) is a robust data-analysis and image-processing technique that has shown great promise in the agricultural sector. In this study, 129 papers that are based on DL applications used in agriculture are discussed, categorizing them into five areas: crop yield prediction, plant stress detection, weed and pest detection, disease detection, and smart farming. Smart farming is sub-categorized as water management, seed analysis, and soil analysis. This study highlights the potential of deep learning in enhancing agricultural productivity and promoting economic growth. The study found that supervised learning networks, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), AlexNet, and ResNet, are primarily used in agriculture to enhance economic growth. However, there is a need to develop new DL techniques that can improve model performance and reduce inference time for practical applications. In this review, critical research gaps, particularly in the development of new techniques, are analyzed. This study emphasizes the importance of continued research in this area to fully leverage DL's potential of DL for smart farming and to achieve sustainable agricultural development.