Plant Disease Detection: A Comprehensive Survey

Chapaneri, Radhika; Desai, Maithili; Goyal, Anmolika; Ghose, Shreya; Das, Sheona

doi:10.1109/cscita47329.2020.9137779

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…The review serves as a valuable resource for researchers and practitioners by highlighting the strengths and limitations of each approach, offering insights into the most effective strategies for advancing plant disease detection using deep learning techniques." [4] 'Md. Ali-Al-Alvy, Golam Kibria Khan, Mohammad Jahangir Alam, Saiful Islam, Mokhlesur Rahman, and Mirza Shahriyar Rahman' are the authors of the paper titled "Rose Plant Disease Detection using Deep Learning" They say that "The paper "Rose Plant Disease Detection using Deep Learning" presents an innovative approach to identifying diseases in rose plants through the application of deep learning techniques.…”

Section: IImentioning

confidence: 99%

Plant Doctor: The Messiah for New-Age Farmers

Gagare,

Sabale,

Shelake

et al. 2024

IJRASET

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bstract: Agriculture plays a major role in developing countries; however, food security remains a vital issue. Most crops get wasted due to a lack of storage facilities, transportation, and diseases. More than 15% of the crops get wasted in India due to diseases, hence it has become one of the major concerns to be resolved. There is a need for an automaticsystem that can identify these diseases and help farmers take appropriate steps to get rid of crop loss. Farmers have followedthe conventional method of identifying plant diseases with theirnaked eyes, and all farmers can't identify these diseases the sameway. With the advances in AI, there is a need to incorporate the facilities of computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer-friendly environment to work with, all these qualities make Deep Learning the favourable method to get started with this problem.

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Section: IImentioning

confidence: 99%

Plant Doctor: The Messiah for New-Age Farmers

Gagare,

Sabale,

Shelake

et al. 2024

IJRASET

View full text Add to dashboard Cite

show abstract

“…The authors in [ 31 , 32 ] stated that DL techniques are effective in the early detection of crop diseases. They recommended the use of these techniques to overcome the limitations of traditional approaches.…”

Section: Related Workmentioning

confidence: 99%

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Kundu

Rani

Dhaka

et al. 2021

Sensors

142

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Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

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“…[7] provided data for vascular plants. [9] provided data for flowering plants and [8] provided the data for moss plants. These four categories of plants provided 21 species of plants used to develop the data model figure 1.…”

Section: The Data Model Of the Fungi Datasetmentioning

confidence: 99%

Plant Disease Detection and Monitoring Using Artificial Neural Network

Orakwue

Nwazor

2022

int.jour.sci.res.mana.

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Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.

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Plant Disease Detection: A Comprehensive Survey

Cited by 14 publications

References 9 publications

Plant Doctor: The Messiah for New-Age Farmers

Plant Doctor: The Messiah for New-Age Farmers

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Plant Disease Detection and Monitoring Using Artificial Neural Network

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