• Dr. Shaik Mahaboob Basha
• K.Muneesh
• CH.Vasundhara
• KS.Charan Raj
• G.Ujjeev Kumar

Agriculture is the primary occupational sector with the highest level of engagement worldwide. Due to many illnesses in crops and plants, this industry consistently experiences significant losses in output and profit. Conventional approaches provide high accuracy for identifying illnesses in plants and crops. The process takes time, though, which may be sneaky in most situations. As most illnesses are extremely infectious among crops and plants, agricultural diseases must be found and treated as soon as possible. This project focuses on using the ResNet152V2, VGG16, and VGG19 neural network models to analyze and identify leaf diseases in agricultural plants. Crop disease is a significant element that now puts agricultural output activities in jeopardy. It is now possible to automatically identify crop illness using the ResNet152V2 deep learning model based on photos of plant leaf disease thanks to deep learning technology's remarkable success in the fields of image classification and image identification. By putting neural networks through a variety of parameter training cycles, numerous model accuracy tests are conducted. In this project, the neural network uses about 90,000 photos of plant leaf illnesses from 38 different groups of plant diseases. The final trained model's overall accuracy is approximately 95%, when compared to manual recognition, is more accurate. This demonstrates unequivocally the capability of the ResNet152V2 neural network-based deep learning model to distinguish crop disease.

ResNet152V2, VGG16, VGG19, Deep Learning

"Plant leaf disease detection using Image processing based on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i71-i74, March-2023, Available :http://www.jetir.org/papers/JETIR2303812.pdf

"Plant leaf disease detection using Image processing based on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi71-i74, March-2023, Available at : http://www.jetir.org/papers/JETIR2303812.pdf