The Success Of Tomato Genius

Updated: Aug 30, 2021

The COVID-19 Pandemic has infected people of many races and ages and I fear to imagine if a disease like that emerged in crops. Crops are the number one source of food for humans and diseases can easily wipe them out. With that inspiration, I created an AI service that can predict what disease a tomato plant has such as Early Blight, Late Blight, Septoria, Curl Virus



Aim I wanted to learn the basics of Image Classification using a neural network.

What I did The COVID-19 Pandemic has infected people of many races and ages and I fear to imagine if a disease like that emerged in crops. Crops are the number one source of food for humans and diseases can easily wipe them out. With that inspiration, I created an AI service that can predict what disease a tomato plant has such as Early Blight, Late Blight, Septoria, Curl Virus. To achieve this, I used images of tomato plant leaves as a dataset to the Image Classification AI training program provided by aiclub.world website. The dataset was made up of 5 categories (1 for each disease and 1 healthy group) with 20 images per category equaling 120 images.

Problem

The leaf on the left is early blight and is predicted by the AI before I moved it to the Late Blight Category.

When I tested my AI service, I got a disappointing accuracy of 23.3%. That is almost the same as randomly picking 1 out of the 5 categories. This happened because my AI service was confusing a disease called Early Blight with the healthy leaves as they look the same.

Solution

I first attempted to change my dataset 7 times but that didn’t improve the accuracy much. After some tests, I figured the Early Blight disease was being confused with the healthy leaves. I proceeded to use the elimination by trial method by eliminating one category from the whole dataset and testing it. I repeated this for