Hemp Disease Detection and Classification using Machine Learning

Abstract

The project aims to create an efficient and effective method for the identification and isolation of diseases affecting cannabis plants. Marijuana is a versatile plant with commercial and medicinal uses. The plant is easy to grow and care for and is suitable for all climates. But like other plants, cannabis disease can affect the plant's growth and have a significant financial impact on cannabis production. The project aims to use the power of machine learning to increase the accuracy and speed of disease diagnosis by identifying characteristics such as leaf changes, growth patterns, texture or softness or squishiness. This research aims to improve early detection and management of these diseases in cannabis cultivation by creating a robust model learned from different datasets of cannabis plant images, good models learned from many different cannabis plants.