Olive Leaves Disease Detection and Classification using Deep Learning

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ID: 313131
2023
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Abstract
Plants play a vital role in our environment and food substantially. Many plants are part of our food chain. The olive plant is one of the most significant plants, and we are achieving many benefits from it. The disease that affects these plants harms the quality of the olive and reduces its productivity, which also negatively impacts the economy. The Early detection and classification of these diseases is a big Challenge for the farmers. The early identification and targeted interventions can reduce crop management costs, minimize pesticide use, and prevent losses. A standard dataset called Zeytin_224x224_Augmented repository is utilized in this research. The dataset is openly available on Kaggle. Timely and accurate disease identification is essential for growers' income and olive production quality. The methodology utilized in this study for evaluating and classifying the effectiveness of InceptionV3 in detecting olive leaf disease and classification. Inception V3, deep learning enables early disease detection, allowing for timely treatment and ensuring the long-term sustainability of olive farming. We proposed a deep-learning model, Inception V3 that achieves the highest accuracy of 98.8%.
Reference Key
imported_1777059310_69ebc5ee94928 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ammad Noor
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/257
Keywords Keywords not found

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