Betel Plant Tech: Betel Disease Forecasting System and Finding Marketplace

Abstract

Betel in Sri Lanka extends back to 340 B.C. and it has a significant cultural value in Sri Lanka. Betel is currently planted throughout the country, and it is the primary source of income for numerous farmers. Betel leaves are easily exposed to diseases. Taking that into consideration, it is clear that anticipating the spread of betel diseases, which have been identified as having a significant influence on the country’s economy, and creating a better marketplace is critical. The "Betel Plant Tech" smartphone application was created in this project with a focus on image processing, sequential models, regression and classification, in machine learning techniques. The findings indicate that predictions of betel yield identification produces high accuracy of 98% in random forest regression (RFR), identification of betel diseases with 92% accuracy with Restnet 34, and disease propagation level consists of a 92% accuracy level in the sequential model while predicting the spread of viral/fungal diseases has a 97% accuracy rate in Decision Tree Regression. The planned study would determine and predict the yield of betel cultivation, identify the diseases of betel leaf, predict the prevalence of diseases, and identify the level of disease prevalence. Besides, it will be easy to find a good marketplace for betel growers.