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Detection of Plant Leaf Diseases by Applying Image Processing Schemes

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Timely diagnosis of the disease is the key factor in agricultural productivity. If timely detection of the disease is not taken into account, it may lead to crop yield loss. Hence, agriculturists and agronomists face troubles to detect diseases successfully at an early stage or later stage. To support these personnels to diagnose disease syndromes in infected plants, deep learning plays an important role. The machine based recognition system based on image processing not only saves time but also is more robust and efficient in comparison to manual assessment system. It helps the growers to take timely steps involved in the judicious treatment of the concerned leaf diseases for crop protection. Maximizing the production or minimizing the production loss is the primary goal of automatic plant leaf disease recognition system. Following review presents some leaf disease detection techniques.
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Keywords: Classification; Feature Extraction; Leaf Disease Detection; Segmentation

Document Type: Research Article

Affiliations: Department of Computer Science and Engineering, Lovely Professional University, Jalandhar 144411, Punjab, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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