Early detection of major diseases in turmeric plant using improved deep learning algorithm

• Autorzy: Devisurya V. , Devi Priya R. , Anitha N.

Identyfikatory

DOI

10.24425/bpasts.2022.140689

• Języki publikacji: EN

Abstrakty

EN

Turmeric is affected by various diseases during its growth process. Not finding its diseases at early stages may lead to a loss in production and even crop failure. The most important thing is to accurately identify diseases of the turmeric plant. Instead of using multiple steps such as image pre-processing, feature extraction, and feature classification in the conventional method, the single-phase detection model is adopted to simplify recognizing turmeric plant leaf diseases. To enhance the detection accuracy of turmeric diseases, a deep learning-based technique called the Improved YOLOV3-Tiny model is proposed. To improve detection accuracy than YOLOV3-tiny, this method uses residual network structure based on the convolutional neural network in particular layers. The results show that the detection accuracy is improved in the proposed model compared to the YOLOV3-Tiny model. It enables anyone to perform fast and accurate turmeric leaf diseases detection. In this paper, major turmeric diseases like leaf spot, leaf blotch, and rhizome rot are identified using the Improved YOLOV3-Tiny algorithm. Training and testing images are captured during both day and night and compared with various YOLO methods and Faster R-CNN with the VGG16 model. Moreover, the experimental results show that the Cycle-GAN augmentation process on turmeric leaf dataset supports much for improving detection accuracy for smaller datasets and the proposed model has an advantage of high detection accuracy and fast recognition speed compared with existing traditional models.

Słowa kluczowe

EN

artificial intelligence; computer vision; turmeric leaf diseases detection

PL

sztuczna inteligencja; wizja komputerowa; wykrywanie chorób liści kurkumy

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 2
• Strony: art. no. e140689
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Devisurya V.

• Department of Information Technology, Kongu Engineering College, Perundurai, India

autor

Devi Priya R.

• Department of Information Technology, Kongu Engineering College, Perundurai, India

autor

Anitha N.

• Department of Information Technology, Kongu Engineering College, Perundurai, India

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