An Artificial Intelligence Framework for Disease Detection in Potato Plants

Abbas, Ahmed; Maqsood, Umair; Ur Rehman, Saif; Mahmood, Khalid; AlSaedi, Tahani; Kundi, Mahwish

doi:10.48084/etasr.6456

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…These modifications contributed to the model's improved accuracy and efficiency, rendering it a more reliable tool for ophthalmological diagnosis and care. The future scope of this research will be to implement various other features that affect eye disease and incorporate more datasets and a variety of advanced models [39][40][41][42].…”

Section: Discussionmentioning

confidence: 99%

Advancing Eye Disease Assessment through Deep Learning: A Comparative Study with Pre-Trained Models

Alzamil

2024

Eng. Technol. Appl. Sci. Res.

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The significant global challenges in eye care are treatment, preventive quality, rehabilitation services for eye patients, and the shortage of qualified eye care professionals. Early detection and diagnosis of eye diseases could allow vision impairment to be avoided. One barrier to ophthalmologists when adopting computer-aided diagnosis tools is the prevalence of sight-threatening uncommon diseases that are often overlooked. Earlier studies have classified eye diseases into two or a small number of classes, focusing on glaucoma, and diabetes-related and age-related vision issues. This study employed three well-established and publicly available datasets to address these limitations and enable automatic classification of a wide range of eye disorders. A Deep Neural Network for Retinal Fundus Disease Classification (DNNRFDC) model was developed, evaluated based on various performance metrics, and compared with four established pre-trained models (EfficientNetB7, EfficientNetB0, UNet, and ResNet152) utilizing transfer learning techniques. The results showed that the proposed DNNRFDC model outperformed these pre-trained models in terms of overall accuracy across all three datasets, achieving an impressive accuracy of 94.10%. Furthermore, the DNNRFDC model has fewer parameters and lower computational requirements, making it more efficient for real-time applications. This innovative model represents a promising avenue for further advancements in the field of ophthalmological diagnosis and care. Despite these promising results, it is essential to acknowledge the limitations of this study, namely the evaluation conducted by using publicly available datasets that may not fully represent the diversity and complexity of real-world clinical scenarios. Future research could incorporate more diverse datasets and explore the integration of additional diagnostic modalities to further enhance the model's robustness and clinical applicability.

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Section: Discussionmentioning

confidence: 99%

Advancing Eye Disease Assessment through Deep Learning: A Comparative Study with Pre-Trained Models

Alzamil

2024

Eng. Technol. Appl. Sci. Res.

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A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Gülmez

2024

Potato Res.

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This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.

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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops

Ali,

Rehman,

Ali

et al. 2024

Sci Rep

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Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

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An Artificial Intelligence Framework for Disease Detection in Potato Plants

Cited by 11 publications

References 33 publications

Advancing Eye Disease Assessment through Deep Learning: A Comparative Study with Pre-Trained Models

Advancing Eye Disease Assessment through Deep Learning: A Comparative Study with Pre-Trained Models

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops

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