An Analysis of Various Techniques for Leaf Disease Prediction

As far as the agricultural domain is concerned, one of the most hot research areas of analysis is accurate prediction of leaf disease from the leaf images of a plant. The prediction of agricultural plant diseases bymeans of the image processing techniques will hence reduce the dependence on the farmers to safeguard their agricultural land and also their products. However, with the presence of noise, the leaf disease prediction is said to be hindered. To address this issue, in this paper, Covariance Kalman Geometric Graph-basedBernoulliClassifier (CKGG-BC) for Plant leaf disease prediction is proposed. The CKGG-BC method is split into three parts. To start with the plant leaf image provided as input, the Covariance Kalman Filtered Preprocessing modelintroduced for the image enhancement. Second, Geometric Graph-based Segmented Co-occurrence Feature Extraction model is applied to the preprocessed image to accurately segment the infected leaf areas and followed by which extracting the accurate infected leaf areas. Finally, Bernoulli Online Multiple Kernel Learning Classifier is applied for accurate plant leaf disease prediction with minimum classification error. The proposed method provides a significant refinement with respect to state-of-the-art methods. Even under complex background conditions, i.e., in the presence of noise, the averageaccuracy of the proposed method is said to be improved and hence paves mechanism for prediction of plant leaf disease in a significant manner. Experimentalresults exhibit the effectiveness of the proposed method in terms of computational overhead, accuracy, true positive rate and classification error respectively.

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Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp2079-2086 ◽

2022 ◽

Vol 12 (2) ◽

pp. 2079

Author(s):

Pallepati Vasavi ◽

Arumugam Punitha ◽

T. Venkat Narayana Rao

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Detection System ◽

Disease Detection ◽

Disease Prediction ◽

Research Papers ◽

Comprehensive Overview ◽

Leaf Disease ◽

Agricultural Yield ◽

Number Of Classes

<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

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The Study of Crop leaf Disease Prediction: An Approach using Genetic Algorithm

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2019.6133 ◽

2019 ◽

Vol 7 (6) ◽

pp. 772-773

Author(s):

Sonal Verma

Keyword(s):

Genetic Algorithm ◽

Disease Prediction ◽

Leaf Disease

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