Sugarcane Diseases Detection Using Optimized Convolutional Neural Network with Enhanced Environmental Adaptation Method
DOI:
https://doi.org/10.52756/ijerr.2024.v41spl.005Keywords:
Sugarcane Diseases, CNN, EEAM, Precision Agriculture, Deep Learning, Disease RecognitionAbstract
This research aims to address the need for accurate and prompt identification of sugarcane diseases, which substantially impact the worldwide sugar industry and the livelihoods of numerous farmers. Conventional visual inspection methods are hindered by subjective interpretations and restricted availability, prompting the investigation of more sophisticated techniques. By harnessing deep learning capabilities, specifically Convolutional Neural Networks (CNNs), and further enhancing their performance using the Environmental Adaptation Method (EAM) optimization, this research demonstrates significant enhancements in disease detection accuracy, precision, recall, and F1-Score. Based on the macro values obtained from the different approaches, it has been observed that an accuracy of 89% was obtained for the CNN designed from EEAM in comparison to the other counter parts. Similarly, the precision of the proposed architecture of CNN is better in comparison to GA, PSO and DE. On the same lines the Recall and F1 score of the proposed approach is better in comparison to that of the three counterparts. Similarly, the ROC analysis for the analysis of AUC is done and it was identified that the AUC curve for the different CNN designed by various optimizer were good in identifying the different classes of the sugarcane diseases. The major limitation of this approach is that model has marginal accuracy with its counterpart algorithm, however, the algorithm suggested the use of simple CNN models that are easy to use. The rigorous methodology, encompassing data collection and model optimization, guarantees the reliability and applicability of the sugarcane disease detection system based on Convolutional Neural Networks (CNN). Future research directions focus on integrating hyperspectral imaging, unmanned aerial vehicles (UAVs), and user-friendly mobile applications. This integration aims to empower farmers, facilitate proactive disease management, and ensure the sustainability of the sugarcane industry. This advancement represents notable progress in precision agriculture and disease mitigation.
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