• Srujanendhra Talla
• B.Deepika
• K.Sai Sumanth
• Dr.V.Saidulu

Plant health is crucial for achieving higher agricultural production. Some plant species are more prone to diseases due to environmental changes, so it is important to take good care of agricultural plants and adopt effective farming practices. Most farmers are not aware of the plant diseases or the appropriate pesticides to use when a plant gets infected. This lack of knowledge often leads to reduced crop yield and financial loss. To address this problem, we have designed a system using deep learning CNN model that identifies and detects plant diseases from images. We have integrated CNN model into web application that facilitates disease detection from plant images and also provides recommendations for purchasing suitable plants and pesticides. Training of the models was performed with the use of an open database of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes,[plant, disease] combinations, including healthy plants. Several model architectures were trained, with the best performance reaching a 96% success rate in identifying the corresponding [plant, disease] combination(or healthy plant). The model's notable success rate underscores its potential as an effective advisory and early warning tool. Moreover, this approach can be further extended to develop a comprehensive plant disease identification system capable of functioning under real-world cultivation conditions.

CNN, Deep learning, leaf disease identification, Plant disease, Web application, Purchase assistance.

"Plant and Plant Disease Identification System Using Deep learning and Purchase Assistance System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.f402-f408, May-2025, Available :http://www.jetir.org/papers/JETIR2505644.pdf

"Plant and Plant Disease Identification System Using Deep learning and Purchase Assistance System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppf402-f408, May-2025, Available at : http://www.jetir.org/papers/JETIR2505644.pdf