Proceedings ArticleDOI
Potato Blight: Deep Learning Model for Binary and Multi-Classification
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TLDR
In this paper, a CNN-based deep learning (DL) multi-classification model was used to classify the potato crop plants having healthy and potato blight (PB) disease images based on their PB disease severity level, along with this binary classification has also been done to simply classify the healthy and disease crop leaf.Abstract:
Detection of plant crop diseases has become an active field of research day by day due to increasing the demand for such systems and techniques as crop diseases are now become a common part of agriculture. Focusing on this demand and need, we have developed a Convolutional neural network (CNN)-based Deep learning (DL) multi-classification model which classifies the total of 900 real-time collected images of potato crop plants having healthy and potato blight (PB) disease images based on their PB disease severity level, along with this binary classification has also been done to simply classify the healthy and disease crop leaf. A total of four disease severity levels have been taken into account which resulted in a binary classification accuracy of 90.77% and 94.77% of best multi-classification accuracy. This work will be a great contribution in the field of potato disease recognition and detection using DL approaches.read more
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References
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TL;DR: In this article, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies.
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Gouranga Kar,Ashwani Kumar +1 more
TL;DR: In this article, a potato crop was grown with four phenology-based irrigation treatments (75mm water depth with each irrigation) with three replications, and the experimental split-plot design consisted of irrigation treatments in the main plots with mulching and non-mulching as subplots.
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Vegetable quality and productivity as influenced by growing medium: a review
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Proceedings ArticleDOI
A Deep Neural Network based disease detection scheme for Citrus fruits
Vinay Kukreja,Poonam Dhiman +1 more
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.
Proceedings ArticleDOI
A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf
Sunayana Arya,Rajeev Singh +1 more
TL;DR: A comparison of accuracy and efficiency between CNN and AlexNet architecture for detecting the disease in Mango and Potato leaf shows that accuracy achieved from AlexNet is higher than CNN architecture.
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