Cotton Crop Disease Detection using Decision Tree Classifier

Abstract

Smart Farming is a technique that offers high-ended application of modern farming by acquiring multiple data from sensors, robots, live stream, social media, etc. The concept of integrating these various data from multiple sources and processing it using multilevel databases is referred to as big data. Till today, there have been various developments in smart farming using image processing, data mining, IOT, etc. but as of now, Machine Learning is the industry which is emerging at a rapid pace. There has been an increasing demand related to real time applications in machine learning using supervised or unsupervised methods. The current scenario of traditional farming includes manually taking data, unpredictable weather conditions, sprinkling pesticides on diseases, etc. to yield productions, which is paving way to life threats for farmers especially in drought areas. With reference to the current scenario in traditional farming, there has been a dire need of predicated data in farming which can help farmers to know about their real time problems and act accordingly. To resolve their problems, we would like to propose a system which can predict cotton crop diseases using `Decision Tree Classifier' by taking parameters as temperature, soil moisture, etc. This would help farmers by providing better quality productions and we would be also focusing on building an android application which will give real time output to the farmers in efficient ways.