JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
A comparative analysis of fruit disease detection using deep learning.
Suman S, Adarsh M J.
Agriculture occupies a central role in the Indian economy and acts as the main source of livelihood for a substantial segment of the population worldwide. Therefore, enhancing fruit production is crucial. The health and quality of fruits are often undermined by diseases, primarily caused by bacterial and fungal pathogens. Early detection of fruit diseases is beneficial for agricultural practitioners as it reduces expenses by enabling the forecasting and averting of disease outbreaks. Identifying the most fruitful techniques for the detection of fruit diseases is a proactive measure to mitigate the impact of these diseases in their initial stages. To protect farmers' investments experts are devising a method to detect infections in fruits the main goal of their study is to assess various deep-learning techniques for identifying fruit diseases the paper presents an innovative approach that combines deep learning with machine learning for detecting and classifying fruit diseases it employs multiple feature extraction and selection techniques utilizing a comprehensive fruit dataset to test both machine learning and deep learning classifiers the proposed hybrid CNN model demonstrated a remarkable 97.10 accuracy in extensive experimental tests across all fruit image datasets.
Fruit Disease Detection, Feature extraction, Deep-Learning Techniques, Classification