Plant Disease Detection and Classification Using Machine Learning and Deep Learning Techniques: Current Trends and Challenges

Every year, all over the world, the major crops are affected by various diseases, which in turn affects agriculture and the economy. The traditional method for plant disease inspection is a time-consuming, complex problem that mainly depends on expert experience. The explosive growth in the field of artificial intelligence (AI) provides effective and smart agriculture solutions for the automatic detection of these diseases with the help of computer vision techniques. This paper presents a survey on recent AI-based techniques proposed for plant disease detection and classification. The studied techniques are categorized into two classes: machine learning and deep learning. For each class, its main strengths and limitations are discussed. Although a significant amount of research has been introduced, several open challenges need to address in this field. This paper provides an in-depth study of the different steps presented in plant disease detection along with performance evaluation metrics, the datasets used, and the existing challenges for plant disease detection. Moreover, future research directions are presented.