Prediction of Guava Plant Diseases Using Deep Learning

Srinivas, B.; Satheesh, P.; Naidu, P. Rama Santosh; Neelima, U

doi:10.1007/978-981-15-7961-5_135

Cited by 12 publications

(6 citation statements)

References 28 publications

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“…In this paper, the researchers have used Flask in Python, in which they have created their own CNN model. The confusion matrix is used for obtaining the accuracy, and the accuracy archived was between 65 and 85% [20].…”

Section: Related Workmentioning

confidence: 99%

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Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

Islam,

Zaib,

Ferraioli

et al. 2024

AgriEngineering

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Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.

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Section: Related Workmentioning

confidence: 99%

“…Seeking to enhance plant disease classification accuracy, researchers explored improvements and modifications to existing deep learning architectures, demonstrating superior performance in identifying plant species ailments. Among them, we have considered VGG 16 [19], AlexNet [20], ResNet [21], and DenseNet 121 [21].…”

Section: Deep Learning Architecturementioning

confidence: 99%

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

Islam,

Zaib,

Ferraioli

et al. 2024

AgriEngineering

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“…References [19], [20], [21], [22], [23] focused on classifying potato leaf disease. References [24] and [25] performed the proposed work on a dataset of Guava leaves. Singh and Misra [26] focused on some of the most common leaves of banana, jackfruit, lemon, mango, potatoes, tomato, and sapota.…”

Section: Related Workmentioning

confidence: 99%

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

Tabbakh

Barpanda

2023

IEEE Access

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This paper proposes a novel approach for extracting deep features and classifying diseased plant leaves. The agriculture industry is negatively impacted by plant diseases causing crop and economic loss. Accurate and timely diagnosis is crucial for managing and controlling plant diseases, as traditional methods can be costly and time-consuming. Deep learning-based tools effectively detect plant diseases depending on the qualitative of extracted features. In this regard, a hybrid model for plant disease classification based on a Transfer Learning-based model followed by a vision transformer (TLMViT) is proposed. TLMViT has four stages: 1) data acquisition, where the PlantVillage and wheat datasets are used to train and evaluate the proposed model, 2) image augmentation to increase the number of training samples and overcome the overfitting issue, 3) leaf feature extraction by two consecutive phases: initial features extraction by using pre-trained based model and deep features extraction by using ViT model, and 4) classification by using MLP classifier. TLMViT is experimented with using five pre-trained-based models followed by ViT individually. TLMViT performs accurately in plant disease classification, obtaining 98.81% and 99.86% validation accuracy for VGG19 followed by the ViT model on PlantVillage and wheat datasets respectively. Moreover, TLMViT is compared with pre-trained-based architecture. The comparison result illustrates that TLMViT achieved an enhancement of 1.11% and 1.099% in validation accuracy, 2.576% and 2.92% in validation loss compared with the transfer learning-based model for PlantVillage and wheat datasets respectively. Thereby proposed model proves the efficiency of using ViT for extracting deep features from the leaf.INDEX TERMS Plant disease, image processing, deep learning, transfer learning, vision transformer.

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“…These methods have also been applied to visualize lesions on products like guava [22], tea [23], and apple [24]. Additionally, Goluguri et al [25] developed a neural network to predict rice blast disease using meteorological parameters like wind speed, temperature, rainfall, and relative humidity.…”

Section: Introductionmentioning

confidence: 99%

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

Paçal,

Kunduracıoğlu

2024

J. Soft. Comput. Decis. Anal.

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Sugar cane is an important agricultural product that provides 75% of the world's sugar production. As with all plant species, any disease affecting sugarcane can significantly impact yields and planning. Diagnosing diseases in sugarcane leaves using traditional methods is slow, inefficient, and often lacking in accuracy. This study presents a deep learning-based approach for accurate diagnosis of diseases in sugarcane leaves. Specifically, training and evaluation were conducted on the publicly available Sugarcane Leaf Dataset using leading ViT (Vision Transformer) architectures such as DeiT3-Small and DeiT-Tiny. This dataset includes 11 different disease classes and a total of 6748 images. Additionally, these models were compared with popular CNN models. The findings of the study show that there is no direct relationship between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 12 models tested, the DeiT3-Small model showed the highest performance with 93.79% accuracy, 91.27% precision, and 90.96% F1-score. These results highlight that rapid, accurate, and automatic disease diagnosis systems developed using deep learning techniques can significantly improve sugarcane disease management and contribute to increased yields.

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Prediction of Guava Plant Diseases Using Deep Learning

Cited by 12 publications

References 28 publications

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

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