Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

Araujo, Sufola Das Chagas Silva; Malemath, V. S.; Sundaram, K. Meenakshi

doi:10.3389/frobt.2021.650134

Cited by 9 publications

(3 citation statements)

References 22 publications

(46 reference statements)

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“…One more cost-effective and intuitive method of diagnosing various plant leaf diseases is to closely examine the symptoms on the leaves of the plants to get a sense of the infection [23]. This describes a method for identifying unhealthy plant symptoms by applying the idea of feature learning [24].…”

Section: Symptom-based Identification Of G-4 Chili Leaf Diseases Base...mentioning

confidence: 99%

Chili Disease Detection Using HOG with Euclidean Distance

Chauhan Pareshbhai Mansangbhai

2024

jes

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In order to detect plant diseases in the leaves of chili plants, automatic learning is used in this study. Farmers are planting chilies with the intention of exporting them worldwide. Chili is a need for regular meals. There aren't many illnesses that need to be found in the leaves of chili plants. There are three types of chili plants: weak, diseased, and healthy. Weak and sick chili plants can be affected by diseases such as a harsh leaf, spot leaf, whitefly, yellowish, etc. It has been reported that research is underway to determine whether chile plants are safe to grow or polluted. But when it comes to agriculture, it's critical to recognize the damaged plant by its unique type. Various category diseases are studied using the HOG (Histogram of Oriented Gradients) of the leaf of the chili plant. The representative feature vectors in the feature vector are created using the mean value of every feature point. A typical feature vector and the Euclidean distance are used to calculate the outliers. For the Euclidean distance larger than 0.0025, 0.0016, and 0.00125, the average accuracy rate was 61.6%, 73.2%, and 81.00%, respectively, with the modified border point in the feature vector being 0.0016, 0.00125, and 0.0009. The results presented above suggest that machine-learning techniques for image processing can be used to determine the type of plant disease.

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Section: Symptom-based Identification Of G-4 Chili Leaf Diseases Base...mentioning

confidence: 99%

Chili Disease Detection Using HOG with Euclidean Distance

Chauhan Pareshbhai Mansangbhai

2024

jes

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“…As a result, noise removal is an important and fundamental stage of image processing [38]. The research of [39] centred on applying a Gaussian filter to remove noise from resized images of chili leaves. Firstly, the colour channel of images is converted from Red, Green, Blue (RGB) to Hue, Saturation, Value (HSV) to make disease identification easier.…”

Section: Image Pre-processingmentioning

confidence: 99%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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Chili, an important crop whosefruit is used as a spice, is significantly hampered by the existence of chili diseases.While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 researchpaperpublications on theidentificationof chilidiseases, with publication dates spanning from 2013 to 2021, arereviewed in this paper.Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.

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“…In general, a disease on chili peppers can be identified by its symptoms. Most research is focused on image processing [16,17,20] and deep learning [18,19] disease analysis from chili pepper leaves that require large image datasets to train the model. However, some symptoms that appear on chili pepper leaves may be similar, resulting in discrepancies in the efficiency and accuracy of classification.…”

Section: E Deploymentmentioning

confidence: 99%

Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

Nuanmeesri

Sriurai

2021

Eng. Technol. Appl. Sci. Res.

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The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.

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Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

Cited by 9 publications

References 22 publications

Chili Disease Detection Using HOG with Euclidean Distance

Chili Disease Detection Using HOG with Euclidean Distance

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

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