Plants Disease Segmentation using Image Processing

Masood, Rabia; Khan, Saiqa; Khan, M. N. A.

doi:10.5815/ijmecs.2016.01.04

Cited by 17 publications

(6 citation statements)

References 17 publications

(13 reference statements)

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“…The differences in the performance of the traditional image processing algorithms can generally be justified by the type of attribute analyzed, i.e., the descriptor of characteristics used [ 60 ]. Since in this paper, the same descriptors were used for all four algorithms, the variations in overall accuracy found here validate the arguments of Masood and Khan [ 61 ]. As well as those of Singh et al [ 62 ], in that for the identification of symptoms of stress and disease in plants, it is the selection of the method of machine learning that is the determining factor in the success of a classification.…”

Section: Resultssupporting

confidence: 80%

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Caldeira

Santiago

Teruel

2021

Sensors

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The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.

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Section: Resultssupporting

confidence: 80%

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Caldeira

Santiago

Teruel

2021

Sensors

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“…The method of disease identification can use image-based technology or imagery has been done by some previous researchers. The researcher by [2] [5]. The study was investigated by Sanjeev S.Sanaki, R. Arunkumar [6] This study focuses on the existing disease spots on the leaves.…”

Section: Introductionmentioning

confidence: 99%

“…Khan Plants Disease Segmentation using Image Processing I.J. Modern Education and Computer Science [5] researchers only mapped the results of research from other researchers by making literature review of segmentation and algorithm In previous studies, no one has assessed using feature extraction using ACE as an analysis to detect plant disease in chili. In this study will extract features using automated Color Equalization which is then classified using SVM (Support Vector Machine) for disease identification based on its leaves.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Leaf Features in Chili Plants Using Automated Color Equalization (ACE)

Basiroh¹,

Kurniasih²,

Jati³

et al. 2018

IJET

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Chili is a variety of crop groups that have promising business prospects. To obtain optimal agricultural yield, then the process of plant care and how to planting should be maximal. Constraints often experienced by farmers in the process of planting chili in Magelang regency of Indonesia is a disease of yellow leaves. Some diseases in plants can be identified using precision technology, one of them is by using an image or image-based technology. In previous studies, no one has analyzed using feature extraction using ACE as an analysis to detect plant disease in chili. In this study will extract features using Automated Color Equalization (ACE) which is then classified using SVM (Support Vector Machine) for disease identification based on its leaves. With this method, the accuracy of the extraction results in a combination of 80% texture features, color feature extraction, and a combination of 80% color feature texture

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“…Big data analytics helped to healthcare sector upgrade by implementing epitomize medicine and prescriptive analysis, hospital liability interference and predictive analysis, dissipation and responsibility, changeability reduction, automatic extraneous and constitutional exposure of patient record, regulated health conditions and patient registries and disintegrated end solution [1]. Some area of enhancement is more endeavor than really carry out.…”

Section: A Why Big Data Analytics For Medical Applicationsmentioning

confidence: 99%