Machine learning regression technique for cotton leaf disease detection and controlling using IoT

Abstract

Cotton is one of the most important cash crops in India. Every year the production of cotton is reducing due to the attack of the disease. Plant diseases are generally caused by pest insect and pathogens and decrease the productivity to large-scale if not controlled within time. This paper presents a system for detection and controlling of diseases on cotton leaf along with soil quality monitoring. The work proposes a Support Vector Machine based regression system for identification and classification of five cotton leaf diseases i.e. Bacterial Blight, Alternaria, Gray Mildew, Cereospra, and Fusarium wilt. After disease detection, the name of a disease with its remedies will be provided to the farmers using android app. The Android App is also used to display the soil parameters values such as humidity, moisture and temperature along with the water level in a tank. By using Android app farmers can ON/OFF the relay to control the motor and sprinkler assembly according to need. All this leaf disease detection system and sensors for soil quality monitoring are interfaced using Raspberry Pi which make it independent and cost effective system. The overall classification accuracy of this proposed system is 83.26%.