Apperception of Plant Disease with avail of algorithm

Gutte, Vitthal; Mundhe, Pramod; Bhandari, Mahesh

doi:10.1109/icpc2t53885.2022.9776831

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Strength Weakness [20] Genetic Algorithm -Superior performance to Otsu segmentation method -Obtained high dice coefficient score -Ineffective control over luminosity inhomogeneity -Small dataset [21] Support Vector Machines -Post-processing not required -Better results on instance segmentation -High computational cost -Excessive pre-processing required [23] Extreme Learning Machine -Automatic detection using the mobile application -Great results from an extreme learning machine -The image results from the camera's automatic adjustment are different -Need more segmentation adjustment for background color [24] Deep Convolutional Networks -Able to detect 13 different types of diseases -Big impact augmentation process -Small dataset -Fine-tuning does not have a big impact [25] Convolutional Neural Network Simple morphology erosion improves the detection Has a long runtime [26] YOLOv3-MobileNetv2 -Lightweight depth-wise convolutions are used -Good lighting conditions -Has a long runtime -High computational cost [27] Single The remainder of this paper is organized as follows: In Section 2, the materials and methods are described. The experimental results are described and compared with those of other recent iterative methods in Section 3.…”

Section: Reference Modelmentioning

confidence: 99%

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Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

2022

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Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios.

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Section: Reference Modelmentioning

confidence: 99%

“…This study used a genetic algorithm to compute an optimal convolution kernel mask that emphasizes fungal infections' texture and color features. Gutte et al [21] used three phases for monocot and dicot diseases. First, they segmented the leaf using the k-mean clustering technique.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

2022

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“…Crop diseases brought on by bacterial, fungal, viral infections, and insect infestations have been a severe problem for farmers since they can significantly affect crop fruit yield, both in terms of quantity and quality, which has a direct influence on the nation economy [4]. The physiological processes of the crop are altered when a disease is present, and the emergence of disease signs can be seen on the crop leaves, fruits, stems, flowers, and roots.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the manual procedures of identifying and evaluating crop diseases are challenging, even for workers with the necessary qualifications [4]. These manual procedures are carried out using either laboratory detection methods, biomarker-based detection methods, or imaging methods.…”

Section: Introductionmentioning

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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“…These plants' illnesses can result in decreased production and losses in the variety and quality of the fruits. An important area of study is how to identify and categorise plant diseases [5,6]. The production of guava has, however, decreased recently because of fungi diseases.…”

Section: Introductionmentioning

confidence: 99%

Guava Pest App to Assist Farmers

Rani¹

2022

IJECC

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In this paper, Guava Pest App is designed to identify the several diseases in Guava at early stage. Guava Pest App is an innovative application that is useful for farmers. The farmers can capture the images from Guava farm and upload the images. Guava API is created and placed in cloud services. Images taken from farmers field matches with Guava API and gets the TNAU recommendation for the Guava fruit diseases. Guava Pest App for pest management in Guava will analyze and provide an accurate recommendation to farmers about the type of pesticide for the symptoms given through the images This paper expresses the idea about the creation of Guava Pest App, an android application that helps to make management decision for cotton leaf symptoms. The study would provide a better understanding of the management practice required for the Guava fruit disease.

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Apperception of Plant Disease with avail of algorithm

Cited by 4 publications

References 4 publications

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

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

Guava Pest App to Assist Farmers

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