Publication Date: 2024/02/02
Abstract: The integration of smart farming systems and requisite infrastructural developments represents a paradigm shift in agricultural technology, significantly augmenting both the quality and yield within the sector. Tomatoes, as one of the world's most vital crops, are frequently afflicted by leaf diseases, which critically impact harvest outcomes. Prompt detection and identification of these diseases are imperative to mitigate crop devastation and implement efficacious control measures, particularly in understanding the pathogen species composition. Delays in disease diagnosis and inadequate control responses can precipitate substantial crop losses and marked degradation in product quality. This study introduces an IT-based solution leveraging image processing and deep learning methodologies for the expedited detection of diseases in tomato plants. Utilizing a dataset of 22,930 images, encompassing nine distinct diseased-leaf categories and a healthy-leaf category, the research employs a Convolutional Neural Network (CNN) for disease classification and prediction. The model demonstrates notable efficacy, achieving an overall accuracy rate of 98.2% and maintaining a loss rate of 0.0532. This advancement in precision agriculture exemplifies the potential of integrating cutting-edge technology with traditional farming practices to enhance productivity and disease management.
Keywords: Component, formatting, style, styling, insert.
DOI: https://doi.org/10.5281/zenodo.10609342
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN673.pdf
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