APPLICATION OF MACHINE LEARNING AND DEEP LEARNING IN SMART AGRICULTURE

Abstract

— In the modern era of industrial revolution Artificial intelligence, Machine learning, Deep learning, IoT and Robotics have become more and more popular in research and also used in many applications such as natural Language processing, visual data processing, social network analysis, drug discovery, image classification, text mining and so forth. Nowadays deep learning has applied in many applications of smart agriculture such as water and soil management, crop cultivation, crop disease detection, weeds removal, crop distribution, robust fruits counting and yield prediction. This paper is focusing on how the deep learning is used for smart agriculture. Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The purpose is to develop Drone which carries pesticides to spray all over the farm which reduces the work of farmers as well as it finishes his work soon. The application of pesticides and fertilizers in Agricultural areas is of prime importance for crop yields. This is to develop a user friendly interface for the farmers. The Drone is a pesticide spraying hexa copter for agricultural purpose which helps the farmer to spray the pesticides all over his land so that it reduces his work which can evenly spray all over his farm. Here the farmer can control the drone using an android app and he can connect to the app using Wi-Fi module (ESP 8266) which is interfaced in the drone. It will precisely route the land area of that particular farmer and using GPS. Here we have used the Arduino board which is the open source electronics prototype platform which is interfaced with the Wi-Fi module and GPS. The Drone can balance the directions and orientations. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.