Image Processing Based Computational Intelligent Methodology for Plant Disease Detection and Classification
Keywords:
Computational intelligence, Disease classification, Image processing, Machine learning (ML), deep learning (DL), Plant disease detectionAbstract
This research introduces a novel approach to identifying and categorizing plant diseases by integrating image processing methods with computational intelligence. With the increasing importance of agriculture in ensuring food security, timely identification and precise categorization of plant diseases play a pivotal role in efficient disease control and maximizing crop productivity. Conventional disease detection approaches typically involve manual intervention, leading to subjective interpretation, time inefficiency, and susceptibility to errors. In this research, we harness the power of digital image processing to automate the detection process, providing a rapid and objective means for identifying plant diseases. We utilize cutting-edge image processing algorithms to extract pertinent features from plant images, capturing intricate details associated with various symptoms of diseases. Following this, we apply computational intelligence methods like machine learning and deep learning to classify diseases using the extracted features. Our proposed methodology offers several advantages over conventional approaches. By leveraging computational intelligence, the system can adapt and learn from a vast amount of image data, enhancing its accuracy and robustness in disease classification. Furthermore, the automation of disease detection reduces the dependency on human expertise, enabling scalable and cost-effective solutions for agricultural stakeholders. To validate the efficacy of our methodology, extensive experiments are conducted on diverse datasets encompassing various plant species and disease types. The outcomes exhibit encouraging performance concerning accuracy, sensitivity, and specificity, highlighting the viability of the suggested approach for practical applications in agriculture. In conclusion, this research presents a novel and efficient framework for plant disease detection and classification, merging the capabilities of image processing and computational intelligence. The proposed methodology holds significant promise for revolutionizing disease management practices in agriculture, facilitating timely interventions and ultimately contributing to global food security.
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