WonderGaze - Plant Disease Detection System

An accessible plant disease detection system that uses leaf images to diagnose issues and provide solutions, empowering farmers and gardeners to protect their crops efficiently.

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What inspired us: United Nations estimated that food production must increase by at least 60% to ensure food security for the predicted population by 2050. To achieve this, food losses need to be reduced significantly. Here we observed a space for societal impact and developed WonderGaze.

What it does: It allows users to upload leaf images and diagnose issues with their plants and provide solutions, empowering farmers and gardeners to protect their crops efficiently.

How we built it: Front End : HTM , CSS, JavaScript, Bootstrap Back End: Flask, PIL Machine Learning: TensorFlow(Keras), Numpy, Pandas, Pytorch, CNN

Challenges we ran into: 1) Training the Machine Learning Model and achieving a high accuracy. 2) Had a tough time intergating the front end and the back end.

Accomplishments that we're proud of: 1)WonderGaze is capable of making a positive impact on society. 2)Able to integrate multiple technologies in one project, all while incorporating the theme of the Hackathon(Alice in WonderLand).

What we learned: 1) Learned the basics of CNN and how to train a Machine Learning Model 2)Obtained a significant experience with Web Devlopment. 3)Learnt how to deligate tasks and manage a project effectively within a group.

What's next for WonderGaze - Plant Disease Detection System: 1) Increasing the data set to not only have application for farmers but also for florists and botanists. 2)Aim at increasing the accuracy the Machine Learning Model. 3)Incoporate a data base system to store upload histories for the users.

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