Pest Identification and Control of Diseases in Crop Fields through Image Processing and Tracking of Atmospheric Parameters

Abstract

Atmospheric parameters like the temperature, humidity, soil moisture and PH of the soil in the crop-fields are very much important to be minutely observed and noted to maintain the appropriate crop growth without which crop production may hamper in a large scale. With the Advent of Embedded Based Technologies such as IoT, Robotics, Machine Learning etc. in agriculture has created a great impact to make the farming habits smart and intelligent. But, to design an intelligent system with optimization, cost as well as energy efficiency, and more user-friendliness is an idealistic challenge now-a-days. The system that has been proposed is designed with these ideal constraints in mind. A particular season is targeted to survey the pest condition and growth in crop-fields and how the atmospheric conditions are readily involved in this case. If any abrupt changes in the atmosphere have influenced the pest nature, then it has to be studied and tracked properly to form a research database that can be used for precautionary measures in the next season. The sensor network is formed with the atmospheric parameters and interfaced with Raspberry Pi3 which acts as gateway here to store all the data in the cloud. The data can be accessed by any computer or mobile devices from the cloud. By that method, pest identification is easier by accessing this low cost and energy efficient system which is beneficial in a large scale for the agricultural scientists, pest technicians and also farmers if they are trained about the usage and benefits of the system.