Classification of powdery mildew disease symptoms on sandalwood using machine learning techniques
Abstract
Powdery mildew (Oidium sp.) is a fungal disease that infects plants by creating white powdery spots on plants and trees, reducing in yield. Powdery mildew is often influenced by changes in climatic conditions with cloud factors, humidity, and temperature playing major roles. This study focuses on building a Machine learning model to classify powdery mildew disease symptoms on sandalwood trees based on abiotic features like soil moisture, temperature, humidity, and cloud factors. Various machine learning algorithms such as Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, and K-Nearest Neighbors were used on the dataset, and the model with the highest accuracy was chosen for building a powdery mildew prediction web application on the cloud platform. This web application helps in the prediction of the disease incidence/intensity and thereby enlightens the farming community to adopt appropriate management strategies.
Keywords
References
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