Eggplant Disease Detection Using Yolo Algorithm Telegram Notified

Nasution, Sandika Wahyuni; Kartika, Kartika

doi:10.52088/ijesty.v2i4.383

Cited by 2 publications

(1 citation statement)

References 12 publications

(12 reference statements)

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“…A piezoelectric or piezo sensor is an electronic component that can convert mechanical energy into electrical energy (Yun et al, 2023) (Muradi & Kartika, 2023), called the piezoelectric effect (Su et al, 2021). Piezoelectric will produce an electric voltage if pressure or mechanical strength is generated in both fields (S. W. Nasution & Kartika, 2022). The piezoelectric effect was first discovered in 1880 in France by two physicists Pierre Curie and Jacques Curie.…”

Section: Piezoelectricmentioning

confidence: 99%

Design of Midi Drum Controller Using Piezoelectric and Arduino-Based TCRT5000 Sensor to Facilitate Sound Engineer Performance in the Music Industry

Maulana,

Kartika,

Asran

et al. 2023

J. Renew Energ. Electric. Comp Eng

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This study discusses the Midi Drum Controller using the Arduino Mega which has many analog pins making it suitable for producing complete drum pads as well as piezoelectricity which is used to detect vibrations when the drum pad is beaten, as well as the Tcrt5000 sensor which is used to control the hi-hat so that it can move. from the sound of an open hi-hat to a closed hi-hat. The results of this study indicate that piezoelectricity can detect vibrations and dynamics of strong and weak drum beats and send the game to midi-based virtual drum applications. The Tcrt5000 sensor used can also work well, judging by the sound it produces and the data sent when the Tcrt5000 sensor is on and off.

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Section: Piezoelectricmentioning

confidence: 99%

Design of Midi Drum Controller Using Piezoelectric and Arduino-Based TCRT5000 Sensor to Facilitate Sound Engineer Performance in the Music Industry

Maulana,

Kartika,

Asran

et al. 2023

J. Renew Energ. Electric. Comp Eng

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Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI

Gomez,

Selvaraj,

Casas

et al. 2024

Sci Rep

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Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers’ ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.

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Eggplant Disease Detection Using Yolo Algorithm Telegram Notified

Cited by 2 publications

References 12 publications

Design of Midi Drum Controller Using Piezoelectric and Arduino-Based TCRT5000 Sensor to Facilitate Sound Engineer Performance in the Music Industry

Design of Midi Drum Controller Using Piezoelectric and Arduino-Based TCRT5000 Sensor to Facilitate Sound Engineer Performance in the Music Industry

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI

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