Agriculture plays a vital role in Bangladesh’s economy, contributing 11.5% to the GDP. Fruits comprise 10% of national income. Bangladesh ranks 7th in mango production globally and it is known as the king of fruits. Bangladesh’s annual mango production is around 1.2 million metric tons from over 100,000 acres of land. However, despite its potential, mango production in the country faces challenges, including pest attacks and diseases caused by bacteria, fungi, viruses, and insects. These diseases lead to a substantial annual yield loss of around 30%, impacting farmers’ livelihoods and national production.
Bacterial and fungal diseases are major constraints for mango production, causing around 30% yield loss annually. The absence of real-time, automated systems for early detection and classification of mango leaf diseases hampers efforts to mitigate crop losses. Currently, farmers face delayed diagnoses which reduces productivity and causes financial losses.
This project aims to address this problem by developing a cutting-edge computer vision-based model that provides instant in-field detection and classification of mango leaf diseases, empowering farmers with timely information to reduce losses and enhance their income
- Collect a comprehensive dataset of mango leaf images encompassing multiple bacterial and fungal diseases, ensuring representation across various regions.
- Train and optimize Convolutional Neural Network (CNN) models to accurately detect and classify mango leaf diseases using the collected dataset.
- Develop an intuitive user interface with trained models for real-time mango disease screening by farmers.
Type of data: 240x320 mango leaf images.
Data format: JPG.
Number of images: 4000 images. Of these, around 1800 are of distinct leaves, and the rest
are prepared by zooming and rotating where deemed necessary.
Diseases considered: Seven diseases, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould.
Number of classes: Eight (including the healthy category).
Distribution of instances: Each of the eight categories contains 500 images.
Four mango orchards of Bangladesh, namely Sher-e-Bangla Agricultural University orchard, Jahangir Nagar University orchard, Udaypur village ,mango orchard, and Itakhola village mango orchard.
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Python
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streamlit
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Streamlit cloud