EZM-AI: A Yolov5 Machine Vision Inference Approach of the Philippine Corn Leaf Diseases Detection System

Austria, Yolanda C.; Mirabueno, Maria Concepcion A.; Lopez, Dylan Josh D.; Cuaresma, Dexter James L.; Macalisang, Jonel R.; Casuat, Cherry D.

doi:10.1109/iicaiet55139.2022.9936848

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…The results of the comparative analysis of deep learning-based disease detection algorithms of our study with the other studies are expounded in Table 4 . The authors ( Austria et al., 2022 ; Li et al., 2022b ) have used the YOLOv5 algorithm for the detection of apple and corn respectively, and they have achieved 90% and 97% results. Li et al.…”

Section: Resultsmentioning

confidence: 99%

A mobile-based system for maize plant leaf disease detection and classification using deep learning

et al. 2023

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Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

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Section: Resultsmentioning

confidence: 99%

A mobile-based system for maize plant leaf disease detection and classification using deep learning

et al. 2023

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“…The results of the comparative analysis of deep learning-based disease detection algorithms of our study with the other studies are expounded in Table 4. The authors (Austria et al, 2022;Li et al, 2022b) have used the YOLOv5 algorithm for the detection of apple and corn respectively, and they have achieved 90% and 97% results. A precision curve of the YOLOv8 model.…”

Section: Comparative Analysis Between Applied Modelsmentioning

confidence: 99%

Spotlight on Artificial Intelligence (AI) for Sustainable Plant Production

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…However, when dealing with limited resources, such as the Raspberry Pi, the primary considerations revolve around the inference speed and the model accuracy. YOLOv5 has demonstrated a trade-off between speed and accuracy in various detection applications compared to other approaches [54,55]. Moreover, it is well-suited for resource-constrained environments due to its low parameters within the model weights.…”

Section: Object Detectionmentioning

confidence: 99%

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

Abu Awwad,

Rana,

Perera

2024

Sensors

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The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

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EZM-AI: A Yolov5 Machine Vision Inference Approach of the Philippine Corn Leaf Diseases Detection System

Cited by 10 publications

References 9 publications

A mobile-based system for maize plant leaf disease detection and classification using deep learning

A mobile-based system for maize plant leaf disease detection and classification using deep learning

Spotlight on Artificial Intelligence (AI) for Sustainable Plant Production

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

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