Plant disease analysis using histogram matching based on Bhattacharya's distance calculation

Tejonidhi, M. R.; Nanjesh, B. R.; Math, Jagadeesh Gujanuru; D'Sa, Ashwin Geet

doi:10.1109/iceeot.2016.7754943

Cited by 16 publications

(3 citation statements)

References 1 publication

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“…RGB model images are converted into gray image using color conversion. To increase the contrast are used different contrast enhancement method or techniques like, stretch to minimum-maximum [8], stretch and clip to Min-Max, histogram equalization and contrast adjustment. In segmentation group of pixel into regions, thereby defining the boundaries of the region of interest, partitions of the image of the distinct regions that pixel containing the similar attributes are used in the images.it separates image Feature extraction and selection provides the measurement vectors, should be defined as the interest part of an image and will be detected in different images of the same scene.…”

Section: Literature Riviewmentioning

confidence: 99%

Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

fan¹,

Roy²,

Roy³

2020

IJAERS

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Despite sustainable development, increase in use smartphones, drones, satellite and other information communication technologies for data collection and analyzing for decision marking. Crop losses due to insect pests and diseases are a major threat to farming communities globally. In the case of rice, up to 37% of economic losses are caused by insect pests and disease infestation. Timely and accurate disease and insect pest diagnosis and management can not only reduce crop losses. In the last decade, Information and communication technologies (ICTs) have been increasingly used for information sharing. With mobile internet services becoming available in even the furthest locations, ICT-based agricultural solutions are finding a foothold on the farms of poor smallholders. ICT-based tool that supports diagnosis of insect pests and diseases and enables farmers to make timely decisions for better pest management. To identify the rice diseases at any untimely phases is not yet explored. Early classify and detection for estimation of severity effect or incidence of diseases can save the production from quantitative and qualitative losses, reduce the use of pesticide, and increase country's economic growth. The main challenges is to minimize the impacts of attacks. Detection of plant disease through some automatic technique is beneficial as it requires a large amount of work of monitoring in big farm of crops, and at very early stage itself it detects symptoms of diseases means where they appear on plant leaves. In this paper we review different disease classification techniques that can be used for plant leaf disease detection. Also we describe the data collection by information communications technology for rice leaf, different disease classification approaches that can be used for rice diseases detection. Thirdly we suggested the framework for convolutional neural network in Agricultural sector for detection and identification for innovation technology in agriculture.

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Section: Literature Riviewmentioning

confidence: 99%

Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

fan¹,

Roy²,

Roy³

2020

IJAERS

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“…According to paper [4] they have proposed for a technique that can be used for detecting paddy plant disease by comparing it with 100 healthy images and 100 sample of disease1 and another 100 sample of disease2. It's not sufficient enough to detect disease or classify it training data is not linearly separable.…”

Section: Sift Deskmentioning

confidence: 99%

Plant Disease Detection by Image Processing: A Literature Review

Halder¹,

Sarkar²,

Bahar³

2018

SDRP-JFST

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To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the Internet-of-Things (IOT). These processes above are related for solving real life problems. Food is one of the basic needs of human being. World population is increasing day by day. So it has become important to grow sufficient amount of crops to feed such a huge population. But with the time passing by, plants are affected with various kinds of diseases, which cause great harm to the agricultural plant productions. Beside that many countries economy greatly depends on agricultural productivity and it's also a need for a coun try to attain agricultural productivity of basic agricultural product for the people of that particular country. Detection of plant disease through some automatic technique is beneficial as it requires a large amount of work of monitoring in big farm of crops, and at very early stage itself it detects symptoms of diseases means where they appear on plant leaves. In this paper surveys on different disease classification techniques that can be used for plant leaf disease detection.

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“…Identification of scorching and blast sickness through leave image performed by histogram analysis. This method will benefit agriculturalists to identify leaf blasts efficiently (Tejonidhi et al, 2016). Another research of image processing uses mathematical morphology to analyze and depict the texture and shape signature.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection of Rice Leaf Diseases Based on Histogram Color and Closing Morphological

et al. 2022

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Harvest drop in rice because of leaf blast is a vital issue in the country’s food stock and social life where rice is the primary source of food. Epidemics can cause leaf blasts due to weather conditions or environmental transformation. Therefore, early detection of leaf blast is needed to take precautions action to save the harvest. This research presents a new approach for rice leaf blast detection. It seizes colour distribution and shapes to determine the damaging leaf. Two main features: colour and shape, are key points to measure the similarity of an image by comparing the image query and database. The image extraction uses histogram colour throughout the pre-processing phase. The approach will take the dominant colour of leaf. Since this green colour dominated the leaf, the green will be converted from RGB to the HSV domain with 256 range. The shape feature extraction based on morphology closing will calculate the images’ area, diameter, and perimeter. The process is continued by resizing the image and convert into a grayscale mode to apply canny edge detection. The experiment uses 267 images dataset and 74 testing data consisting of 2 categories: blast disease leaf and healthy leaf. The trial results achieve an 85.71% accuracy rate to detect blast disease by colour feature, 71.42% by shape feature, and 85.71% by combined colourshape features

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Plant disease analysis using histogram matching based on Bhattacharya's distance calculation

Cited by 16 publications

References 1 publication

Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

Plant Disease Detection by Image Processing: A Literature Review

Intelligent Detection of Rice Leaf Diseases Based on Histogram Color and Closing Morphological

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