• Binu P Chacko

One of the most active research fields in agriculture is the early diagnosis of plant diseases. The health of crops depends on the prompt diagnosis of plant diseases. The manual method of identifying and preventing the spread of plant diseases is unreliable. Utilizing AI techniques in agriculture can reduce labour costs, decrease time inefficiencies, and enhance crop quality and overall yield. After revolutionizing the domain of computer vision, machine learning and deep learning can now solve many tasks such as automated plant disease diagnosis, soil fertility management, rainfall prediction, crop yield prediction, etc. To ensure the quality and quantity of crops, it is important to protect plants from diseases. Early identification and prevention of plant diseases are the important aspects of crop harvesting since they can effectively reduce any growth disorders, and thus minimize pesticide application for pollution-free crop production. To this end, various image processing activities such as preprocessing, segmentation, and feature extraction are discussed. For classification, ML and DL based approaches used in the different vegetable plants are elaborated.

Preprocessing, Segmentation, Feature extraction, Classification

"Deep Learning Based Plant Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.c236-c242, July-2025, Available :http://www.jetir.org/papers/JETIR2507227.pdf

"Deep Learning Based Plant Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppc236-c242, July-2025, Available at : http://www.jetir.org/papers/JETIR2507227.pdf