Revolutionizing Potato Farming: A CNN-Powered Approach for Early Blight and Late Blight Detection to Ensure Global Food Security

Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers wit...

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Bibliographic Details
Published in2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) pp. 1 - 5
Main Authors Sai Ponnuru, Mahesh Datta, Amasala, Likhitha
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.02.2024
Subjects
Agriculture
Convolutional Neural Networks (CNNs)
Crop protection Food security
Crops
Early blight
Economic stability
Economics
Food security
Late blight
Mobile applications
Potato disease detection
Predictive models
Stability analysis
Technological innovation
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Summary:Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers with rapid and accurate disease identification, facilitating timely treatments to prevent economic losses and reduce agricultural waste. Our CNN model achieved a remarkable validation accuracy of 99.59%, outperforming conventional methods. With 183,747 trainable parameters, our efficient and accurate model is set to revolutionize agricultural practices. Furthermore, we are deploying our model to Google Cloud and developing a React Native mobile app for potato prediction. This integration aims to provide farmers with real-time disease insights, preventing economic losses and ensuring global food security, thereby enhancing economic stability in the potato farming sector.
ISSN:2688-0288
DOI:10.1109/SCEECS61402.2024.10482138