Groundnut leaf disease detection and classification by using back probagation algorithm

Ramakrishnan, M.; Nisha, A. Sahaya Anselin

doi:10.1109/iccsp.2015.7322641

Cited by 64 publications

(27 citation statements)

References 5 publications

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“…They suggested that ANNs based classifier detected numerous plant diseases with combination of colour and texture features to recognize Alternaria, Bacterial Blight Disease and Anthracnose diseases. Ramakrishnan [49] found much higher accuracies of disease detection, around 97.41% for Ground nut. Their experiments were done with CCM textural analysis and back propagation ANNs algorithm for detection of leaf disease.…”

Section: Discussionmentioning

confidence: 97%

Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection

et al. 2019

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Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.

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Section: Discussionmentioning

confidence: 97%

Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection

et al. 2019

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“…Misra [6] using the proposed image recognition and segmentation algorithm which afford high detection accuracy rate and a user friendly environment. Ramakrishnan.M and SahayaAnselin Nisha [7] proposed a methodology for detection and classification of leaf diseases of groundnut. With neural network and back propagation algorithm author concluded that detection and classification is done with 97% efficiently.…”

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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“…Ramakrishnan M et al [4] proposed work on the identification of groundnut plant disease Cercospora (leaf spot) using Backpropagation method. The experimental results and observation shows out of 100 sample diseased leaf images they classified four types of diseases and secured 97.41% of accuracy.…”

Section: Relatedworkmentioning

confidence: 99%

Tomato Leaf Disease Prediction using Convolutional Neural Network

Sangeetha¹,

Rani²

2019

IJITEE

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Plant diseases are the common cause of the reduction in yield eventually resulting in low income to farmers. Researchers are at their best efforts to find a solution for the detection of plant diseases to increase farm productivity. In this paper, a novel approach of disease detection and prediction for tomato plant leaves has been proposed using deep learning techniques. Training of the models was performed with the use of an open database of 13,848 images, which included 7 distinct classes of [plant, disease] combinations, including healthy tomato crops. Convolution Neural Network which is well-suited for detection and prediction problems has been used for predicting healthy and unhealthy leaves affected by two types of diseases septoria spot and bacteria spot. Experiments are conducted using plant village dataset comprising of 4930 images including healthy and unhealthy leaves. The performance of the model is evaluated using precision, recall and F1-score and the model has achieved the highest accuracy 94.66 %. The significantly high success rate makes the model a very useful advisory or early warning tool.

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Groundnut leaf disease detection and classification by using back probagation algorithm

Cited by 64 publications

References 5 publications

Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection

Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection

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

Tomato Leaf Disease Prediction using Convolutional Neural Network

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