Smart and Sustainable Agriculture

Abstract

Last decade has seen the emerging concept of Smart and Sustainable Agriculture that makes farming more efficient by minimizing environmental impacts. Behind this evolution, we find the scientific concept of Machine Learning. Nowadays, machine learning is everywhere throughout the whole growing and harvesting cycle.Many algorithms are used for predicting when seeds must be planted. Then, data analyses are conducted to prepare soils and determine seeds breeding and how much water is required. Finally, fully automated harvest is planned and performed by robots or unmanned vehicles with the help of computer vision. To reach these amazing results, many algorithms have been developed and implemented.This paper presents how machine learning helps farmers to increase performances, reduce costs and limit environmental impacts of human activities. Then, we describe basic concepts and the algorithms that compose the underlying engine of machine learning techniques. In the last parts we explore datasets and tools used in researches to provide cutting-edge solutions.Index terms -Smart and Sustainable Agriculture, Machine Learning, Datasets, Supervised and Unsupervised Algorithms, Practical applications I -INTRODUCTIONSmart farming is a concept of agriculture management based on information and communication technologies implemented to increase products quantity and quality [1]. Different kind of technologies can be found behind this concept: (i) Sensors to scan soils or scale water, light, humidity or temperature (ii) Software for specific farm applications (iii) Communication technologies like cellular network (iv) Positioning by GPS (v) Hardware and software systems that enable IoT-based solutions and automation like drones [2] (vi) Data analytics to make decision and predict outcomes.All these technologies integrated on farms allow farm-holders making farming processes data-driven and data-enabled. This emerging concept makes agriculture more efficient. Next step is now to reach a sustainable agriculture with the help of high-precision algorithms. The mechanism that drives it is machine learning which is the scientific approach that provides machines the ability to learn by themselves [3]. It has emerged with the help of data sciences, computing progresses and the ability to collect then store more and more data.The rest of this article is organized as follows. Section II presents an overview of ML applications in employed by smart agriculture. In section III, we expose main concepts behind machine learning. Sections IV and V describe with details some supervised and unsupervised algorithms respectively. In section VII, we present some widely employed tools and detail Weka. Then, some common metrics for evaluating approach performances are defined. Section VIII concludes this paper. II -APPLICATIONSFrom smart farming to sustainable agriculture, Machine Learning Algorithms or MLAs are mechanisms behind this evolution. The most popular models deployed in agriculture are ANN which stands for Artificial Neural Networks and Sup...