Plant Disease Prediction and classification using Deep Learning ConvNets

Lakshmanarao, A.; Babu, M. Raja; Kiran, T. Srinivasa Ravi

doi:10.1109/aimv53313.2021.9670918

Cited by 51 publications

(12 citation statements)

References 4 publications

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“…(2020) explored the identification of plant diseases using a recurrent neural network incorporating an attention mechanism, which has better generalization over public datasets compared to the classical CNN approach. L Lakshmanarao et al. (2021) applied the Convnets network architecture to identify nine tomato diseases.…”

Section: Related Workmentioning

confidence: 99%

LightMixer: A novel lightweight convolutional neural network for tomato disease detection

2023

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Tomatoes are among the very important crops grown worldwide. However, tomato diseases can harm the health of tomato plants during growth and reduce tomato yields over large areas. The development of computer vision technology offers the prospect of solving this problem. However, traditional deep learning algorithms require a high computational cost and several parameters. Therefore, a lightweight tomato leaf disease identification model called LightMixer was designed in this study. The LightMixer model comprises a depth convolution with a Phish module and a light residual module. Depth convolution with the Phish module represents a lightweight convolution module designed to splice nonlinear activation functions with depth convolution as the backbone; it also focuses on lightweight convolutional feature extraction to facilitate deep feature fusion. The light residual module was built based on lightweight residual blocks to accelerate the computational efficiency of the entire network architecture and reduce the information loss of disease features. Experimental results show that the proposed LightMixer model achieved 99.3% accuracy on public datasets while requiring only 1.5 M parameters, an improvement over other classical convolutional neural network and lightweight models, and can be used for automatic tomato leaf disease identification on mobile devices.

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Section: Related Workmentioning

confidence: 99%

LightMixer: A novel lightweight convolutional neural network for tomato disease detection

2023

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“…DL models have made significant advances in a variety of fields including, but not limited to, deep fakes [ 22 , 23 ], satellite image analysis [ 24 ], image classification [ 25 , 26 ], the optimization of artificial neural networks [ 27 , 28 ], the processing of natural language [ 29 , 30 ], fin-tech [ 31 ], intrusion detection [ 32 ], steganography [ 33 ], and biomedical image analysis [ 14 , 34 ]. CNNs have recently surfaced as one of the most commonly used techniques for plant disease identification [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

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“…They made three partition of the dataset and applied Convnets on them. They achieved 98.3%, 98.5%, 95% accuracy for disease detection on potato, pepper and tomato respectively [7] Laura Falaschetti et al, (2021) proposed a low cost, low power mdoel for detecting the Esca disease on grapes leaf using compressed CNN which was based on CANDE-COMP/PARAFAC(CP) tensor decomposition. This model was trained on the dataset with low power, low-cost machine for real time classification which was more efficient with respect to the state-of-the-art network.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Classification using Alex Net

Kewat¹

2023

Preprint

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—In modern agriculture field, pest and disease identification is a major role of crop cultivation. Image classification by the use of deep convolutional neural networks of training and methodology used the facilitate a quick and easy system implementation. Pests and diseases are a threat to plant production, especially in India, but identification remains to be a challenge in massive scale and automatically. Collecting images from Image Net dataset. The results show that we can effectively detect and recognize the rice diseases and pests including healthy plant class using a deep convolutional neural network, with the best accuracy of 96.50%. The significantly high success rate makes the model a really useful advisory or early warning tool, and an approach that would be further expanded to support an integrated plant disease identification system to work in real cultivation conditions.

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Plant Disease Prediction and classification using Deep Learning ConvNets

Cited by 51 publications

References 4 publications

LightMixer: A novel lightweight convolutional neural network for tomato disease detection

LightMixer: A novel lightweight convolutional neural network for tomato disease detection

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Plant Disease Classification using Alex Net

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