Deep Learning-based Agricultural Pest and Disease Recognition

Abstract

With the rapid development of artificial intelligence, deep learning technology has penetrated into a variety of different fields. Agricultural researchers have used deep learning's strong image and data processing capabilities to apply it to agricultural production, especially in the identification of agricultural pests and diseases, which has achieved remarkable results. The use of deep learning-based agricultural pest and disease identification technology can quickly and non-destructively identify crops in various complex environments, with high accuracy and good stability, so that farmers can quickly take effective control measures to minimize the loss of crops. This paper first explains the significance of deep learning-based agricultural pest identification technology to agricultural development, and makes a detailed analysis of the advantages and disadvantages of various pest identification technologies and their performance in pest and disease identification. Secondly, various key technologies of deep learning are introduced, including the concepts and applications of various core technologies such as data source, data preprocessing, data augmentation, network model selection, and transfer learning. Finally, based on the above research results, the problems encountered by deep learning in the identification of agricultural pests and diseases are analyzed, and its future development direction in the identification of pests and diseases is discussed.