A Systematic Mapping of Smart Farming and Image Recognition in Agriculture

Abstract

Automation, Internet 4.0, the use of production models and Computational Intelligence techniques have been strongly related with agriculture in recent years. Considering agriculture as a fundamental human activity and also a key industry that is being strongly affected by the climate crisis, knowing how to propose solutions to its various problems through information technologies is of utmost importance. Consequently, this article presents a systematic mapping in order to identify the different applications of machine learning in agriculture, paying special attention to the tools used to acquire relevant information, such as drones and sensors, and to the models used to create solutions, such as Support Vector Machines and Artificial Neural Networks. The paper focuses on the use of image classifying models, evaluating the applications of artificial vision in agriculture, especially in detecting diseases. A comparative study carried out with different deep learning tools in order to identify plant diseases will be presented. The study shows the power of deep learning tools using transfer learning, evidencing that, in networks, these tools learn within few iterations, maintaining excellent levels of generalization, as shown by the validation results.