Machine Learning for Plant Leaf Disease Detection and Classification – A Review

Annabel, L. Sherly Puspha; Annapoorani, T.; Deepalakshmi, P.

doi:10.1109/iccsp.2019.8698004

Cited by 79 publications

(19 citation statements)

References 22 publications

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“…The performance of the proposed leaf disease detection algorithm is compared with state-of-the-art methods such as Deep Neural Network [14], Hyperspectral Imaging [12], Transfer Learning [17] and Machine Learing [18]. The specificity, Sensitivity and Accuracy of the proposed method was higher than the State of the art methods.…”

Section: Fig 6 Performance Comparison For Different Categories Of Lmentioning

confidence: 98%

Centroid Distance Neighbourhood Features and Genetic Algorithm Optimization for Leaf Disease Detection

Swapna¹,

Shaji²

2019

IJITEE

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Leaf disease detection algorithm using Centroid Distance Neighbourhood Features (CDNF) and Genetic Algorithm (GA) optimization is presented in this paper. This method initially segment the disease affected regions from the leaf. The disease affected region is applied for identifying the best feature points using SURF (Speeded Up Robust Feature) algorithm. From a single SURF point four features are extracted by forming a 5×5 neighbourhood across the SURF feature point. The feature extracted using Centroid Distance Neighbour (CDN) is optimized using genetic algorithm to find best features that are able to classify multiple diseases. During testing phase, the disease region is identified and features points are selected using the SURF points. The features are extracted using the CDN and the necessary features that are optimized by genetic algorithm are sorted out as test features. The test features are classified from the trained features using K-Nearest Neighbour (KNN) algorithm. Performance of the proposed leaf disease detection algorithm is evaluated using metrics such as specificity, sensitivity and accuracy. Experimental results shows that the proposed leaf detection algorithm outperforms the state of-the-art methods and it can be used in real time disease detection

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Section: Fig 6 Performance Comparison For Different Categories Of Lmentioning

confidence: 98%

Centroid Distance Neighbourhood Features and Genetic Algorithm Optimization for Leaf Disease Detection

Swapna¹,

Shaji²

2019

IJITEE

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“…In another paper, the authors' overview different plant leaf disease detection based on different machine learning classification techniques. They describe different algorithms and their performance [8].…”

Section: Literature Surveymentioning

confidence: 99%

Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes<strong> </strong>

Bose¹,

Dutta²,

Goyal³

et al. 2021

Preprint

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: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

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“…To identify the effect of weather in the growth of pest and disease the correlation between the weekly average minimum and maximum temperature, relative humidity, sunshine hours and the weekly light traps of leaf folder, yellow stem borer, Brown plant hopper. L. Sherly Puspha Annabel et al [3] reviewed and summarizes various classification techniques for detecting the leaf disease caused by bacteria, fungi and virus. These classification techniques helps in automatic detection of plant leaf disease based on the morphological characteristics.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Outbreak of Tikka and Rust in Grountnut (Arachis Hypogaea)

Mangala*,

Padmapriya

2020

IJRTE

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Groundnut (Arachis hypogaea) is one among the most important oil seed crop cultivated in India. Tikka leaf spot and Rust are the major disease of groundnut that effects on production and productivity. The prediction was made based on factors such as minimum and maximum temperature, morning and evening humidity, wind speed, sunshine hours that quantifies the disease infestation in groundnut. The relationship between the weather, pest and disease infestation are identified which supports the model to predict the occurrence of the disease. The observations were recorded at an interval of one week from the occurrence of tikka and rust. The percent disease intensity is calculated based on the scale explained by Mayee and Data. The favourable climatic conditions for tikka and rust disease development ranges between 26OC – 31OC and 25OC – 30OC respectively, relative humidity greater than 85%, prolonged heavy rainfall, wind speed and rain. The rules are generated based on the recorded observation and the weather parameters. The main objective is to diagnose the existence of tikka and rust disease coupled with weather parameters.

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Machine Learning for Plant Leaf Disease Detection and Classification – A Review

Cited by 79 publications

References 22 publications

Centroid Distance Neighbourhood Features and Genetic Algorithm Optimization for Leaf Disease Detection

Centroid Distance Neighbourhood Features and Genetic Algorithm Optimization for Leaf Disease Detection

Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes<strong> </strong>

Predicting the Outbreak of Tikka and Rust in Grountnut (Arachis Hypogaea)

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