An Innovative Deep Learning Technique to Identify Potato Illness

Potato cultivation is important for world food security as it itself is attacked by a great number of diseases like early blight and late blight, which cause a lot of damage to the yield and quality of the crop.But deep learning offers a great opportunity to address these disease detections; however, how effective this will be in the potatogrowing environment in Pakistan is still not known.This research is designed to evaluate the convolutional neural network (CNN) by building custom datasets that denote the local disease description.The ultimate intention is to develop a high-accuracy, reliable disease detection model that will suit the particular needs of Pakistan.The project, therefore, tries to address data imbalance with the use of the synthetic minority oversampling technique (SMOTE) and develop a CNN architecture that is optimized to provide high diagnostic accuracy.Acquired through realworld pictures, the assessment of the model's performance shows significant progress in detecting potato diseases.This research can give innovative and productive locally useful solutions, which might transform the management of diseases for Pakistani farmers while improving food security and economic stability.These deep learning systems also need to be context-sensitive and reliable, which in turn would help preserve long-term agricultural productivity in Pakistan and beyond.