Deep Learning-Based Knowledge Extraction From Diseased and Healthy Edible Plant Leaves

Deep Learning-Based Knowledge Extraction From Diseased and Healthy Edible Plant Leaves

Udit Jindal, Sheifali Gupta
Copyright: © 2021 |Pages: 15
DOI: 10.4018/IJISMD.2021040105
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Abstract

Agriculture contributes majorly to all nations' economies, but crop diseases are now becoming a very big issue that has to be resolving immediately. Because of this, crop/plant disease detection becomes a very significant area to work. However, a huge number of studies have been done for automatic disease detection using machine learning, but less work has been done using deep learning with efficient results. The research article presents a convolution neural network for plant disease detection by using open access ‘PlantVillage' dataset for three versions that are colored, grayscale, and segmented images. The dataset consists of 54,305 images and is being used to train a model that will be able to detect disease present in edible plants. The proposed neural network achieved the testing accuracy of 99.27%, 98.04%, and 99.14% for colored, grayscale, and segmented images, respectively. The work also presents better precision and recall rates on colored image datasets.
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2. Background

The literature studied for this study was majorly focusing on the use of the machine as well as deep learning techniques for early detection of various plant diseases and therefore reduces crop failure. A huge amount of literature is available which can help to understand the nature of the problem and various solutions proposed for the respective problems. The following table compares various authors’ studies which highly contribute to the prediction of plant diseases.

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