Advanced Machine Learning Approach for Medicinal Plant Leaf Disease Detection: Combining Modified Sigmoid-Hyperbolic Functions with Logistic Regression

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : A. Rajeshkanna, Thalari Chandrasekhar, N. Subramanyan, S. Kiran

10.14445/23488549/IJECE-V11I11P109

How to Cite?

A. Rajeshkanna, Thalari Chandrasekhar, N. Subramanyan, S. Kiran, "Advanced Machine Learning Approach for Medicinal Plant Leaf Disease Detection: Combining Modified Sigmoid-Hyperbolic Functions with Logistic Regression," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 95-109, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P109

Abstract:

Detecting diseases in the medicinal plant leaves, especially on pepper and citrus, is very important in maintaining plant health and supporting food production. This research work, therefore, suggests using a more sophisticated machine learning algorithm that can diagnose and categorize the diseases affecting the leaves of the aforementioned plants with a high-efficiency level, especially for pepper and citrus crops. The method involves the addition of sigmoid, and hyperbolic sine functions with the efficiency of logistic regression to improve the accuracy of diagnosing disease. When combined with the hyperbolic sine function, the sigmoid function promotes increased activation flexibility in capturing disease patterns for interpretation by the model. The logistic regression model is adopted as the principal classification algorithm; it outperforms other classifiers in binary classification problems. Hyperparameter tuning optimizes the model's performance in terms of disease prediction, adding further layers of complexity and shouldering the added responsibility of good accuracy. The proposed approach is further validated through experiments conducted on a set of pepper and citrus leaf images, which show that the current approach yields better accuracy, sensitivity, and specificity than typical ML algorithms. The results evince the effectiveness of this hybrid approach in enhancing early disease diagnosis of medicinal plants that, in turn, can improve disease control of plants.

Keywords:

Agriculture productivity, Disease detection, Hyperbolic sine sigmoid function, Hyperparameter tuning, Logistic regression.

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