Leaf disease identification and classification using optimized deep learning

Algani, Yousef Methkal Abd; Márquez, Orlando Juan; Bravo, Liz Maribel Robladillo; Kaur, Chamandeep; Ansari, Mohammed Saleh Al; Bala, B. Kiran

doi:10.1016/j.measen.2022.100643

Cited by 57 publications

(14 citation statements)

References 9 publications

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“…ACO utilized ants' indirect communication and path-finding behavior to achieve approximate optimization. This approach improved disease identification through color, texture, and leaf arrangement analysis [34]…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification

Baskar,

2024

ECTI-CIT Transactions

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Paddy is a crucial food crop providing essential nutrients and energy and serving more than half the global population. Diagnosing and preventing plant diseases at an early stage is crucial for the health and productivity of crops. Automated disease diagnosis eliminates the need for experts and delivers accurate outcomes. This research will diagnose paddy leaf diseases with Deep Learning technology. The diseases such as bacterial blight, blast, tungro, brown spot, and healthy leaf classes are diagnosed and classified in this study. The dataset contains 160 images from each class with 800 images. Our proposed model is an ensemble of transfer-learned InceptionV3 and VGG16 architectures, which utilizes the strength of individual models to improve overall performance. The use of transfer-learned ensemble deep learning architectures achieved impressive accuracy rates of 97.03%, 94.97%, and 98.87% for training, validation and testing respectively. The results indicating that model is not overfit and generalizes well to unseen data. The model's performance is evaluated with confusion matrix with the parameters like precision, recall, F1-score, and support. We also tested the model's performance against other proposed deep learning techniques with and without transfer learning techniques. Moreover, this research advances reliable automated disease detection systems, fostering sustainable agriculture and enhancing global food security.

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Section: Literature Reviewmentioning

confidence: 99%

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification

Baskar,

2024

ECTI-CIT Transactions

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“…Abd et al [40] introduced a new deep learning technique called ant colony optimization with convolution neural network (ACO-CNN) for disease detection and classification in plant leaves. The technique uses ant colony optimization to enhance the accuracy of disease diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf disease severity classification with explainable artificial intelligence using transformer networks

2023

IJATEE

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The agricultural sector is considered India's most crucial sector. In India, farming is the most common occupation and a significant source of revenue [1]. India is second in the world in terms of population, with 70% of its people living in villages and relying primarily on agriculture for their subsistence. Farmers cultivate a wide variety of crops based on numerous factors such as crop conditions, the environment, soil conditions, local farming practices, new variants of pathogens and various illnesses, etc. *Author for correspondence Presently farmers are incurring losses due to changes in climatic conditions, plant disease is one of the main challenges to food security since it significantly lowers crop quality and output [2].Therefore, it has been difficult to diagnose diseases correctly and discover them early. A variety of methods, including soil management, crop rotation, irrigation, genetic modification, harvesting methods, precision farming, weed control, and pest and disease control, are used to boost the yield. So, it is crucial to identify pesticides and diseases early if you want to boost a crop's production. The traditional method of identifying plant diseases is by visual inspection [3]. This can result in incorrect disease identification, and

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“…For decades, plants have shown an important role in our life and industrial fields. However, several kinds of diseases have significantly reduced to the production of plants (Abd Algani et al, 2023). The detection and diagnosis of the diseases are necessary to improve the production of plants.…”

Section: Introductionmentioning

confidence: 99%

“…compare the classification accuracy of the DNNs on grayscale images of potato and apple. Compared to the classification using the Inception network(Abd Algani et al, 2023), the Densenet allows to improve the classification accuracy for the task.…”

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confidence: 99%

Classification of plant leaf diseases using deep neural networks in color and grayscale images

Phong

2024

J. Decis. Anal. Int. Comp.

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For a long time, plants have shown a crucial role in our life, manufacturing and industry. However, a large number of diseases have significantly affected to the production of plants. The detection and diagnosis of the diseases are necessary to improve the production of plants. In recent years, the automatic classification of plant diseases using artificial intelligence and computer vision have attracted a huge number of researches. The paper presents a classification method of plant leaf diseases using various Deep Neural Networks (e.g., Alexnet, Resnet-50, Densenet-121). The color features have significantly affected the classification accuracy. Therefore, we analyzed and compared the classification accuracy on two public datasets (Plant village leaf datasets) that consist of color and grayscale images. The method obtained the classification accuracy of 98.08% and 92% on color and grayscale images, respectively. The obtained results showed the effectiveness of the method. Based on the obtained results, the impact of color features to the classification accuracy of various Deep Neural Networks is analyzed in the paper. Moreover, the paper compares the performance of various optimization algorithms during the training process of deep neural networks to classify leaf diseases.

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Leaf disease identification and classification using optimized deep learning

Cited by 57 publications

References 9 publications

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification

An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification

Leaf disease severity classification with explainable artificial intelligence using transformer networks

Classification of plant leaf diseases using deep neural networks in color and grayscale images

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