A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment

The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Vinay Gautam [verfasserIn] Naresh K. Trivedi [verfasserIn] Aman Singh [verfasserIn] Heba G. Mohamed [verfasserIn] Irene Delgado Noya [verfasserIn] Preet Kaur [verfasserIn] Nitin Goyal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	artificial intelligence transfer learning paddy leaf disease detection crop disease classification

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 14(2022), 20, p 13610
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:20, p 13610

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su142013610

Katalog-ID:	DOAJ083907297

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520			\|a The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.
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callnumber-first	T - Technology
author	Vinay Gautam
spellingShingle	Vinay Gautam misc TD194-195 misc TJ807-830 misc GE1-350 misc artificial intelligence misc transfer learning misc paddy leaf disease detection misc crop disease classification misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment
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topic_title	TD194-195 TJ807-830 GE1-350 A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment artificial intelligence transfer learning paddy leaf disease detection crop disease classification
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc artificial intelligence misc transfer learning misc paddy leaf disease detection misc crop disease classification misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc artificial intelligence misc transfer learning misc paddy leaf disease detection misc crop disease classification misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment
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author_browse	Vinay Gautam Naresh K. Trivedi Aman Singh Heba G. Mohamed Irene Delgado Noya Preet Kaur Nitin Goyal
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title_sort	transfer learning-based artificial intelligence model for leaf disease assessment
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title_auth	A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment
abstract	The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.
abstractGer	The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.
abstract_unstemmed	The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.
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title_short	A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment
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author2	Naresh K. Trivedi Aman Singh Heba G. Mohamed Irene Delgado Noya Preet Kaur Nitin Goyal
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