IoT-Based Plant Disease Detection using Machine Learning: A Systematic Literature Review

Agriculture has a huge benefit on the global landscape due to rapid population growth and the subsequent increase in demand for food. Hence, there is an urgent need to enhance crop productivity. One of the primary factors contributing to low crop productivity is the spread of diseases caused by bacteria, spores, fungi, and viruses. However, this problem can be mitigated by applying plant disease detection methods, Recently, the field of machine learning and deep learning has achieved remarkable results when combined with IoT -based technology, especially in the field of plant disease recognition. This scientific article aims to clarify the prevailing methodologies used in IoT -based smart agriculture, with a clear focus on the use of IoT and machine learning. In this context, the manuscript presents an innovative model for plant disease detection and recognition, drawing on the fields of machine learning and deep learning, ultimately leading to increased accuracy and efficiency. In essence, this article provides a comprehensive review and overview of various machine learning and deep learning techniques used in plant disease detection, using an IoT approach.