Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach

Acar, Emrullah; Ertuğrul, Ömer Faruk; Aldemir, Erdoğan; Öztekin, Abdulkerim

doi:10.1007/s41348-022-00583-x

Cited by 6 publications

(2 citation statements)

References 86 publications

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“…used various machine learning techniques in the detection of plant diseases caused by pathogens. As a result, they achieved high performance[7].…”

mentioning

confidence: 96%

“…Sevli's work (2023), "Detection of Apple Plant Diseases with Deep Learning", includes agricultural sustainability and the importance of deep learning in combating diseases. By applying an ESA-based classification to the data set consisting of 1821 images, 98.76% accuracy was achieved[6] Acar et al (2022). used various machine learning techniques in the detection of plant diseases caused by pathogens.…”

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confidence: 99%

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Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Acar,

Özcan

2024

Balkan Journal of Electrical and Computer Engineering

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Fruit and vegetable diseases have an important place in the food sector in terms of sustainable agricultural policies. Thus, ıt affects tissues, targeting and negatively impacting the food supply. In this study, Two separate Deep Learning (CNN, AlexNet) models were employed to detect this difference, visual damage and surface marker seen in fruits and vegetables. 22 strawberries and 18 tomato images were used for this analysis, and than data augmentation was implemented 600 images out of 40 images using the image reproduction broadcast. As a result, 83.3% success was achieved.

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“…used various machine learning techniques in the detection of plant diseases caused by pathogens. As a result, they achieved high performance[7].…”

mentioning

confidence: 96%

mentioning

confidence: 99%

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Acar,

Özcan

2024

Balkan Journal of Electrical and Computer Engineering

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A systematic review of deep learning techniques for plant diseases

Pacal,

Kunduracioglu,

Alma

et al. 2024

Artif Intell Rev

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Early detection and control of anthracnose disease in cashew leaves to improve crop yield using image processing and machine learning techniques

Sudha¹,

Kumaran²

2023

SIViP

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Agriculture is one of the primary pillars powering India's economy. It is alarming to note that India's agriculture rate is declining steeply. Climate change, environmental pollution, and soil erosion are well-known factors affecting crop productivity. The increasing prevalence of plant diseases is also a signi cant contributing factor affecting agriculture.Early disease detection and mitigation actions based on identi ed diseases in the plants are critical in increasing crop productivity. This study considers a machine-learning model for detecting disease in cashew leaves. This work concentrates on Anthracnose disease, which leads to severe yield loss when it affects the cashew plant. In this regard, cashew leaves are collected and used to train various machine learning classi ers to identify and classify the disease. This work focuses on the segmentation and classi cation of leaves using various Machine Learning models. For this,

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Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach

Cited by 6 publications

References 86 publications

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

A systematic review of deep learning techniques for plant diseases

Early detection and control of anthracnose disease in cashew leaves to improve crop yield using image processing and machine learning techniques

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