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

Abstract

The present research extensively analyses the use of methods based on machine learning to classify symptoms of fenugreek leaves. this investigation focuses on the complicated topic of fenugreek sickness, using artificial neural networks like CNN's network and random forests to improve disease identification both in precision and efficacy. The Powdery mildew and downy mildew, anthracnose (which affected plants where it occurred), rust, bacterial plant spots (Fusarium Wilt), and botrytis blight were among the most frequent fenugreek leaf diseases studied. In addition, support metrics provide a breakdown of occurrence for each disease class, which is useful for measuring the prevalence or impact of various illnesses in fenugreek farming. The macro, balancing, and micro-average data give a summary of the algorithm's effectiveness. The integrated approach to classifying illnesses demonstrates security, with average outcomes closely matching specific class requirements. The simulation offers a remarkable mean precision of 93.84%, suggesting its ability to provide accurate estimates across the entire fenugreek disease of leaves range. Finally, this study improves the automated disease classification of fenugreek leaves, demonstrating the potential of AI approaches for precision agriculture. The findings given herein not only provide critical information for fenugreek cultivation but also lay the groundwork for future research into the interface of technology and sustainability in agriculture.