AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers
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RESEARCH ARTICLE-COMPUTER ENGINEERING AND COMPUTER SCIENCE
AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers Ishita Dasgupta1 · Jayit Saha1 · Pattabiraman Venkatasubbu1 · Parvathi Ramasubramanian1 Received: 8 May 2020 / Accepted: 29 August 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract Agriculture is undoubtedly one of the biggest and most important professions in the world. Optimization of agriculture and aiming gradually and extensively toward smart agriculture are the need of the hour. IOT (Internet of Things) technology has already been successful in easing people’s lives with its wide range of applications in almost all arenas. In this paper, our work takes the help of IOT devices, wireless sensor network (WSN) and AI techniques and combines them for faster and effective recommendation of suitable crops to farmers based on a list of factors such as temperature, annual precipitation, total available land size, past crop grown history and other resources. Additionally, detection of unwanted plants on crops, namely weed detection, is implemented with frame-capturing drone and deep learning methods. Naïve Bayes algorithm for crop recommendation based on several factors detected by WSN sensor nodes has been used, resulting in an accuracy of 89.29%, which has proved to be better than several other discussed algorithms in the paper, like regression or support vector machine. Deep learning using neural network successfully identifies weeds present in a specific area of crop growth extending an additional protective measure to farmers. The comprehensive application developed for farmers not only reduces the physical hardship and time spent on different agricultural activities, but also increases the overall land yield, reduces possibility of losses due to failure of crops in a particular soil and lessens the chances of damage caused to crops by weeds. Keywords Internet of Things (IOT) · Crop recommender systems · Deep learning · Weed detection · Wireless sensor network (WSN) · Precision agriculture
1 Introduction Farmers all around the world are required to follow an intricate practice of planting crops in rotation. This practice, which is extremely important to the well-being of the soil, is referred to as crop rotation and precision agriculture [1, 3]. In this method, different crops are planted in alternating seasons so that the soil is not constantly deprived of a particular mineral or nutrient. If the same crop is planted, season after
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Pattabiraman Venkatasubbu [email protected] Ishita Dasgupta [email protected] Jayit Saha [email protected] Parvathi Ramasubramanian [email protected]
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Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
season, then it does not give the soil a chance to regenerate the resources, hence rendering the soil barren. With multiple crops, the nutrients are regenerated at a uniform pace. However, with the concept of multiple crops comes the concern to recommend the best crops to be pla
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