A comprehensive literature survey for deep learning approaches to agricultural applications

Abstract

Agriculture is the main economic activity in many parts of countries like India, China, and Africa. The agriculture division needs tremendous development to endure and to meet the demands of the growing population. But the production level of agriculture is decreasing nowadays because of many confronting issues like uncertain rainfall, lack of prediction in crop diseases, unavailability of labourer and improper price fixation for crops. The lack of knowledge in applying technology in the field of farming is also a major issue in countries like India. To address this challenge in the field of agriculture, smart farming is introduced. Deep learning technique which is a part of machine learning can be applied to bring remarkable changes in smart farming by using a prevailing dataset for increasing the productivity of crops. In this article, a study of 31 research works that engaged in deep learning and machine learning of various applications of agriculture have been studied. The key target of this work is to identify the root causes and to provide appropriate techniques to enrich the lives of farmers in agriculture and lead the next generation in a more resourceful manner.