• HARIHARAN R
• AVULA NAGA SANJAY
• SAI KRISHNA S
• D. PRABHU

This paper describes the use of deep learning models pre-trained on object classes in image classification problems like plant disease detection tasks. In this context, we propose a feature extraction method based on deep learning for plant species identification and plant leaf disease classification. The agricultural industry has undergone a great revolution due to the digital identification of plant diseases. The main goal of our project is to use neural networks to identify and treat diseases that have attacked crops. Plant diseases are a significant source of financial loss for the agricultural sector. Disease management is a difficult chore. Typically, illnesses or their signs, like coloured spots or steaks, appear on a plant's leaves or stem. We can find diseases more quickly and precisely with image processing technology than we can with manual labour. As it produces the best results and requires less human work, image processing is essential in the diagnosis of plant diseases. Deep learning assists in identifying ailments and offering a targeted treatment.

Deep Learning Models, leaf diseases detection, convolutional Neural networks

"TEA LEAVES DISEASES DETECTION USING DEEP LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e451-e455, April-2024, Available :http://www.jetir.org/papers/JETIR2404450.pdf

"TEA LEAVES DISEASES DETECTION USING DEEP LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe451-e455, April-2024, Available at : http://www.jetir.org/papers/JETIR2404450.pdf