Detection of Corn Gray Leaf Spot Severity Levels using Deep Learning Approach
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In this paper, a simple Convolutional Neural Network (CNN) based deep learning (DL) model has been proposed for multi-classification of corn gray leaf spot (CGLS) disease based on five different severity levels of CGLS disease on the corn plant.Abstract:
A simple Convolutional neural network (CNN) based deep learning (DL) model has been proposed for multi-classification of corn gray leaf spot (CGLS) disease based on five different severity levels of CGLS disease on the corn plant. Certain corn leaf diseases like CGLS, common rust, and leaf blight are quite common and dangerous in corn harvest. Hence, the current work presents a solution for CGLS disease detection on corn plants using a multi-classification DL model which gives the best detection accuracy of 95.33% in high-risk severity level image. Along with this comparison of five different severity levels has also been conducted based on resulted performance measures (PM).read more
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References
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Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing
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Proceedings ArticleDOI
A Deep Neural Network based disease detection scheme for Citrus fruits
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TL;DR: This study aims to use the dense CNN algorithm to detect and provide an effective method for detecting the apparent defects of citrus fruit and shows that techniques of data augmentation and preprocessing have delivered promising insights to estimate citrus fruit's damages.
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A CNN Approach for Corn Leaves Disease Detection to support Digital Agricultural System
TL;DR: The improved CNN model is used for training and testing four kinds of corn leaf images and achieves 98.78% of average detection accuracy, which is the highest accuracy achieved only for corn disease detection from leaves with shorter training convergence times.
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Crop leaf disease grade identification based on an improved convolutional neural network
TL;DR: The experimental results showed that the proposed leaf disease grade identification method based on a convolutional neural network was feasible and effective for the classification of leaf disease grades.
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A retrospective study on handwritten mathematical symbols and expressions: Classification and recognition
Sakshi,Vinay Kukreja +1 more
TL;DR: In this paper, the authors performed an extensive state-of-the-art on the techniques and methods used for recognizing and classifying HMSE, and brought out all significant findings in sub-processes, representation models, algorithms, tools, datasets, and comparative analysis of the accuracy of the recognition models.