WITHDRAWN: Novel plant leaf disease detection based on optimize segmentation and law mask feature extraction with SVM classifier

Kaur, Navneet; Devendran, V.

doi:10.1016/j.matpr.2020.10.901

Cited by 18 publications

(7 citation statements)

References 2 publications

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“…The GLCM technique is a way to obtain statistical values of the 2nd order by calculating the odd value by connecting the difference between two-pixel values at a distance with a certain angle [14]. The distance in the GLCM method will create a co-occurrence in the data of an image [6].…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Evandari

Soeleman

Pramunendar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.

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Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Evandari

Soeleman

Pramunendar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Precision agriculture is developing mostly in line with two themes, Big Data & advanced analytics and robotics, claims McKinsey. Aerial photography, sensor technology, and accurate local weather forecasts with GPS capabilities are all included [4].…”

Section: Significant Work Donementioning

confidence: 99%

GoAgro: Design and development of a precision farming robot

Bhuvan¹,

Pavithra²,

T.C.Manjunath³

et al. 2023

IJETMS

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Since a long time ago, agriculture has been a significant part of the Indian economy. 18% of India's gross domestic product is produced by the agriculture sector (GDP). A better answer for precision agriculture is provided by robotics and the internet of things. Selective harvesting is necessary when picking fruits or vegetables like tomatoes and apples. In traditional farming, each fruit is hand-picked by a worker, requiring a huge amount of labour to carry out selective farming. We suggest a robot that helps farmers with a variety of labor-intensive chores, such as selective crop harvesting and qualitative segregation, while also simultaneously providing data on crop health, soil nutritional status, and crop shelf-life detection. An android application is used to analyse, process, and send the obtained data to the farmer. Later, the weight, colour, and health of the particular crop are checked to grade its quality. The fruit is subsequently moved into the designated container after being graded. Spoiled or overripe fruits and vegetables would be plucked and dropped so as not to interfere with the plant's growth. A harvesting arm on the proposed robot would reach the fruit or vegetable and pick it from the plant or tree. The work given here is a mini-project that is taken up as a part of the curriculum completed by electronics and communication engineering students in the second year of the electronics & communication engineering department at Dayananda Sagar College of Engineering in Bangalore.

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“…Numerous image‐processing methodologies are implemented to overcome the existing problems. Through the comparison between artificial and manual detection, it is observed that the detection speed of the network is highly faster than manual identification 8 . Further, the increasing accuracy in the detection of plant disease is achieved through continuous improvements 9 .…”

Section: Introductionmentioning

confidence: 99%

“…Through the comparison between artificial and manual detection, it is observed that the detection speed of the network is highly faster than manual identification. 8 Further, the increasing accuracy in the detection of plant disease is achieved through continuous improvements. 9 By developing agricultural networks and combining the agricultural industry with the internet, the possibility of resolving the issues affecting crop yield by insect pests has been increased along with providing the benefits of developing agricultural informatization.…”

mentioning

confidence: 99%

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Y.¹,

Rao

Veeresh

et al. 2023

Concurrency and Computation

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The conventional models suffer in providing significant accuracy and detection speed, especially in detecting apple diseases, to assure the healthy development of the apple industry. The main aspect of the proposed scheme is to present a new plant disease classification using intelligent segmentation and classification models. The adaptive leaf abnormality segmentation is enhanced by the solution index-based Jaya-Krill Herd optimization (SI-JKHO). Further, the feature extraction is accomplished using "gray level co-occurrence matrix, hybrid local binary pattern with local gradient patterns," color features, and shape features. These collected features are subjected to feature selection using principle component analysis, in which the optimal features are obtained that are further considered to "tuned long short-term memory with recurrent neural network (T-LSRNN)." From the validating results, the performance of the enhanced-JKHO-LSRNN method is correspondingly secured at 6.66%, 6.66%, 7.86%, and 4.34% higher enriched performance RNN, long short-term memory, convolutional neural networks, and LSRNN at 35th learning percentage in dataset 3. The results confirm that the developed model can accurately determine plant diseases compared to conventional models based on diverse performance metrics like "accuracy, precision, specificity, sensitivity, false positive rate, and false negative rate," and so forth. K E Y W O R D Sadaptive fuzzy C-means clustering with thresholding, plant disease classification, solution index-based Jaya-Krill Herd optimization, tuned long short-term memory with recurrent neural network INTRODUCTIONPlant disease can be diagnosed through the plant leaves, which involve more complexities while considering optical observation. 1 Due to the increasing cultivation of plants, the complexities also increase along with the conventional phytopathological problems. Although experienced agronomists and pathologists diagnose plant diseases, sometimes it causes failures in diagnosing particular plant diseases and leads to wrong conclusions and further mistaken treatments. 2 The existing automatic computational system has offered significant support to the agronomist in detecting and diagnosing plant diseases while subjecting the optical observation of infected plants through their leaves. 3 Here, the developed system has to be simple

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WITHDRAWN: Novel plant leaf disease detection based on optimize segmentation and law mask feature extraction with SVM classifier

Cited by 18 publications

References 2 publications

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

GoAgro: Design and development of a precision farming robot

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

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