Spray Prediction Model for Aonla Rust Disease Using Machine Learning Techniques

Abstract

: Disease prediction in plants has acquired much attention in recent years. Meteorological factors such as: temperature, relative humidity, rainfall, sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant. On the other hand, if the factors are inadequate, they may also support the growth of a disease in the plants. The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters. Fifteen different models are tested for spray prediction on conducive days. Two resampling techniques, random over sampling (ROS) and synthetic minority oversampling technique (SMOTE) have been used to balance the dataset and five different classifiers: support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), decision tree (DT) and random forest (RF) have been used to classify a particular day based on weather conditions as conducive or non-conducive. The classifiers are then evaluated based on four performance metrics: accuracy, precision, recall and F1-score. The results indicate that for imbalanced dataset, kNN is appropriate with high precision and recall values. Considering both balanced and imbalanced dataset models, the proposed model SMOTE-RF performs best among all models with 94.6% accuracy and can be used in a real time application for spray prediction. Hence, timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.