Computer Visionimage Enhancement for Plant Leaves Disease Detection

Thangadurai, K.; Kora, Padmavathi

doi:10.1109/wccct.2014.39

Cited by 41 publications

(10 citation statements)

References 7 publications

(1 reference statement)

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“…Thangadurai and Padmavathi [48] have proposed an algorithm to detect disease in plant leaves. The RGB image was converted into grayscale for easy processing.…”

Section: Other Methodsmentioning

confidence: 99%

A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

Jeyalakshmi¹,

Radha²

2017

IJIVP

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Plants, for their growth and survival, need 13 mineral nutrients. Toxicity or deficiency in any one or more of these nutrients affects the growth of plant and may even cause the destruction of the plant. Hence, a constant monitoring system for tracking the nutrient status in plants becomes essential for increase in production as well as quality of yield. A diagnostic system using digital image processing would diagnose the deficiency symptoms much earlier than human eyes could recognize. This will enable the farmers to adopt appropriate remedial action in time. This paper focuses on the review of work using image processing techniques for diagnosing nutrient deficiency in plants.

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“…Thangadurai and Padmavathi [48] have proposed an algorithm to detect disease in plant leaves. The RGB image was converted into grayscale for easy processing.…”

Section: Other Methodsmentioning

confidence: 99%

A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

Jeyalakshmi¹,

Radha²

2017

IJIVP

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“…The histogram equalization that distributes the image intensities is then applied to improve images of plant disease. Intensity levels are distributed by a cumulative distribution function [20].…”

Section: H Image Pre-processingmentioning

confidence: 99%

Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things

Balram¹,

Kumar²

2022

IJACSA

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The Internet of Things can be defined as the network of physical objects that have sensors, software, and other technologies built into them in order to communicate and exchange data with other systems and devices over the internet. In intelligent agricultural advancements to increase the quality of agriculture, the Internet of Things (IoT) can be used. The manual monitoring of plant diseases is quite challenging. It demands enormous effort, expertise in the diseases of plants and the considerable time required for processing. The idea of automation in Smart Agriculture is implemented using the Internet of Things (IoT). They help monitor the plant leaf conditions, control water irrigation, gather images using installed IoT system which includes NodeMCU, cameras, soil moisture, temperature sensors and detect diseases in plants on the datasets collected from leaves. To detect plant diseases, image processing is applied. The detection of diseases comprises the acquisition of images, image pre-processing, segmenting an image, extracting and classifying features. In addition, the performance of two machine-learning techniques, such as a linear and polynomial kernel multi hidden extreme machine (MELM) and a support vector machine (SVM), has been studied. This paper discussed how plant diseases could be detected via images of their leaves. This analysis seeks to validate a proposed system for an appropriate solution to the IoT-based environmental surveillance, water irrigation system management and an efficient approach for leaf disease detection on plants. The proposed multi hidden layers extreme machine classification delivers good performance of 99.12% in the classification of leaf diseases in comparison to the Support Vector Machine classification, which gives 98%.

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“…It helps to improve the image quality and the accuracy of consecutive modules. For the purpose, we used histogram equalization [10] and Median filtering. The histogram equalization method used to improves the contrast and filtering remove the unnecessary noise.…”

Section: B Preprocessingmentioning

confidence: 99%

Weed Detection and Classification using ICA Based SVM Classifier

Medlin*,

Bennet

2020

IJRTE

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Support vector machine (SVM) is a commonly known efficient supervised learning algorithm for classification problems. However, the classification accuracy of the SVM classifier depends on its training parameters and the training data set as well. The main objective of this paper is to optimize its parameters and feature weighting in order to improve the strength of the SVM simultaneously. In this paper, the Imperialist Competitive Algorithm based Support Vector Machine (ICA-SVM) classifier is proposed to classify the efficient weed detection. This enhanced ICA-SVM classifier is able to select the appropriate input features and to optimize the parameters of SVM and is improving the classification accuracy. Experimental results show that the ICA-SVM classification algorithm reduces the computational complexity tremendously and improves classification Accuracy.

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Computer Visionimage Enhancement for Plant Leaves Disease Detection

Cited by 41 publications

References 7 publications

A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing

Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things

Weed Detection and Classification using ICA Based SVM Classifier

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