Crop disease has a significant impact on agricultural productivity that to is reduction in yields and financial loss for farmers. Therefore, the detection of crop diseases should be done early and accurately in order to manage crops and practice sustainable agriculture. The purpose of this project is to present a system that uses Deep Learning (Convolutional Neural Networks) to automatically detect crop diseases using images of leaves. This system uses a pre-trained AlexNet model which is extremely accurate at identifying many crops by utilizing leaf images that display visible clues/signs. The system is delivered to end users as an easy to use web application using the Flask framework; this will allow farmers and agronomists to upload images of leaves and instantly receive a prediction about the condition of those crops. The real-time prediction given to the end user through this system will be used to make data-driven decisions about precision farming and ultimately reduce crop losses and increase the health of crops. This project is an example of how AI-based technology will transform the agricultural industry through timely monitoring and intervention for crop diseases.
Crop diseases, Deep Learning, Convolutional Neural Networks, Web application
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