An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases

Patil, Rutuja Rajendra; Kumar, Sumit; Chiwhane, Shwetambari; Rani, Ruchi; Pippal, Sanjeev Kumar

doi:10.3390/agriculture13010047

Cited by 19 publications

(9 citation statements)

References 46 publications

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“…The evaluation metrics include the mean intersection over union (IoU) and the accuracy of the pneumothorax segmentation. The mean IoU measures the overlap between the predicted and ground-truth pneumothorax masks, while the accuracy measures the percentage of correctly predicted pixels in the pneumothorax region [12] , [13] , [14] , [15] .…”

Section: Methodsmentioning

confidence: 99%

“…To detect moderate or large pneumothorax that could be potentially life-threatening, the authors of [4] , employed deep convolutional networks trained on a large dataset of chest X- ray images that were manually annotated by radiologists [13] . Specifically, the radiologists annotated approximately 13,292 frontal view X-rays of the chest, categorizing images with large or moderate pneumothorax as positive and those with only a little trace as negative.…”

Section: Literature Reviewmentioning

confidence: 99%

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Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

Chiwhane,

Shrotriya,

Dhumane

et al. 2024

MethodsX

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

Chiwhane,

Shrotriya,

Dhumane

et al. 2024

MethodsX

Self Cite

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“…The proposed method achieved an accuracy of 99.97% and a mAP of 0.981 using the public dataset PlantVillage. Patil et al [24] introduced a novel method to estimate the severity of rice diseases using a private dataset of 1200 images. The proposed model is based on an optimized faster RCNN model using the EfficientNet-B0 architecture as the backbone, for the calculation of leaf area and infected region.…”

Section: Related Workmentioning

confidence: 99%

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Tej,

Bouaafia,

Hajjaji

et al. 2024

Preprint

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Agriculture serves as a vital sector in Tunisia, supporting the nation's economy and ensuring food production. However, the detrimental impact of plant diseases on crop yield and quality presents a significant challenge for farmers. In this context, computer vision techniques have emerged as promising tools for automating disease detection processes. This paper focuses on the application of the YOLOv5 algorithm for the simultaneous detection and localization of multiple plant diseases on leaves. By using a self-generated dataset and employing techniques such as augmentation, anchor clustering, and segmentation, the study aims to enhance detection accuracy. An ablation study comparing YOLOv5s and YOLOv5x models demonstrates the superior performance of YOLOv5x, achieving a mean average precision (mAP) of 96.5%.

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“…Patil et al [13] introduced an innovative AI model for grading rice, which employs an enhanced strategy employing the faster-region-based convolutional neural network (FR-CNN). This method is utilized to accurately determine the sizes of individual leaf instances and identify regions affected by infections.…”

Section: Related Studymentioning

confidence: 99%

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Aggarwal,

Khullar,

Goyal

et al. 2023

Agronomy

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Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.

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An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases

Cited by 19 publications

References 46 publications

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

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