Abstract:
Bangladesh's economy is based primarily on agriculture, with the production of tea and
rice being essential to maintaining livelihoods and food security. The persistent risk of
illnesses in rice and tea plants, however, makes agricultural productivity and financial
stability extremely difficult. The goal of this research project is to create reliable and
effective disease detection models for Bangladeshi rice and tea leaves by utilizing
machine learning, more especially deep learning and image analysis approaches as these
two items are consumed in a huge amount on a daily basis from poor to rich. Given
Bangladesh's strong agricultural economy, early disease identification in these crops is
essential to maintaining both food security and economic stability. The research seeks to
offer user-friendly software tools for rapid and precise disease diagnosis, encouraging
precision agriculture, lowering pesticide usage, and improving food security. This will be
accomplished by training machine learning models on a big datasets of photos of healthy
and diseased plants. The project is projected to play a key role in the modernization and
resilience of Bangladesh's agriculture by empowering smallholder farmers through
technology transfer, promoting sustainable agricultural practices, and supporting
government activities. Alongside it will help them to reduce the amount of their loss in
farming.
