Insect and Pest Detection in Stored Grains: Analysis of Environmental Factors and Comparison of Deep Learning Methods

Abstract

Majority of the world’s population depends on agro-based economy for their income and survival. In developing and under-developed countries, due to reasons like basic farming techniques, less educational and technological exposure, lack of technological advancements and recent agricultural knowledge, yield of the crops is very low and moreover there is a huge loss during storage also. Insects, pests and diseases more often affect the stored grains and cause heavy damage to the quantity and quality of the grains. Insecticides and pesticides cannot provide better solution all the times and hence there is an acute need for computer vision based techniques capable of monitoring the spread of insects in the initial stages of storage and protecting the stored grains from further damages and losses. Hence, this paper provides analysis of various factors which can cause damage to the stored grains natural ways to protect crops. It provides the comparison results of various standard deep learning methods that are used to detect the insects and pests in stored grains.