Prediction of Fruits and Vegetable Diseases Using Machine Learning and IoT

Sensors and the Internet of Things (IoT) will be integral to making agriculture more sustainable and productive in the future. Most of the pressing environmental, economic, and technological problems can be solved by taking advantage of IoT, WSNs, and ICT (Information and Communications Technology). Adding more and more connected devices generates a large volume of data with various modalities. Additionally, the increase in the number of interconnected devices occurs due to geographical and temporal factors. This vast amount of data, once intelligently processed and analyzed, will provide a higher level of insights that will improve forecasting, decision making, and sensor dependency management in the future. In this article, we will cover a comprehensive overview of how machine learning algorithms can assist in the analysis of agricultural sensor data. We also discuss a prototype for an integrated food, energy, and water (FEW) system utilizing IoT data. The majority of previous papers in the literature on fruits and vegetables disease detection focused on just one type of disease. Nevertheless, this paper reviews several types of fruits and vegetables diseases.