Hybrid CNN & Random Forest Model for Effective Marigold Leaf Disease Diagnosis

Abstract

This study paper digs into the classification of marigold leaf disease, using cutting-edge machine-learning approaches to ensure accurate and prompt detection. Convolutional neural network (CNN) along with Random Forest methods were employed in our study to determine the precision, recall, and F1-Score of diseases that included Theodore Powdery Mildew, Downy's Mould, Botrytis Blight, and the Fungal Leaf Spot, Aster Oranges or Yellows Greens Rust, Bacterial Plant Spot, or Leaf Curl Virus. The precision values show the percentage of favorable predictions for each disease class, ranging from 95.02% to 95.68%. Recall data show that the model regularly captures true positives, with scores of more than 95.19% in all classes. The F1-Score, a proportional combination of recall and accuracy, demonstrates a balanced performing range of 95.28 to 95.70 percent. The support values demonstrate the dataset's layout for each disease class, as well as the average number of incidents. The research reported a total modeling accuracy of 98%, demonstrating the classification method's durability and reliability. The macro stages, weighed averages, or micro averages all contribute to the model's consistent and equitable performance across courses. These numerical results offer a complete understanding of the model's ability to precisely recognize and categorize marigold leaf diseases. The study offers crucial insights into agriculture pathology or precision agriculture, setting the framework for informed decisions about disease prevention and farming strategies. The findings provide novel techniques for maintaining the health and brightness of marigold petals within agricultural contexts.