Survey on Deep Learning Application in Agricultural Innovation

The rapid advancement of deep learning technologies has markedly influenced numerous sectors, particularly agriculture. This survey paper presents an exhaustive review of contemporary trends, challenges, and future perspectives in the application of deep learning to agricultural tasks. By meticulously analyzing 95 research papers published in 2020, this review categorizes studies based on application areas, deep learning methodologies, data sources, targeted crops, and utilized frameworks. The findings highlight the predominance of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with Keras and TensorFlow emerging as the most frequently employed frameworks. Primary data sources include camera and satellite imagery. Key applications explored encompass plant disease detection, weather forecasting, crop yield prediction, and plant classification. Additionally, the paper underscores performance metrics and model accuracies, with disease detection models frequently surpassing 95% accuracy. Challenges such as data availability, model generalization, and computational costs are critically examined, alongside potential future directions for integrating emerging technologies to enhance agricultural productivity and sustainability. This survey aims to provide researchers and practitioners with a comprehensive understanding of the landscape of deep learning applications in agriculture, highlighting areas ripe for future research.