Disease detection of tomato crop in a hydroponic farming system using a convolutional neural network
Date
2022-05
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Abstract
There is a need to explore hydroponic farming as a new way of farming produce in urban
areas in Ghana. Hydroponic farms usually take up less space than traditional farms and
employ a level of automation, making them easier to manage and a more appealing option
for the youth. Crop disease identification is usually made best with expert eyes; however,
work has been done to improve disease detection using computer vision on single leaves.
To aid in the automation of disease identification, there is the need to detect and classify
crop diseases on leaf clusters to implement preventions early and increase crop yield. This
project focuses on detecting five tomato crop diseases, namely, Bacterial spot, Black leaf
mould, Grey leaf spot, Late Blight and Powdery mildew diseases. The resulting model has
an accuracy of 99.01% on the test dataset, which included images of single tomato leaves,
clustered tomato leaves and images with varying brightness. The model is then uploaded
onto Android and iOS mobile devices for convenient, real-time disease classification. The
accuracy obtained from the mobile devices is about 90% ± 6.8% when using images from the internet.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2022
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Capstone Project
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Keywords
automated agriculture