Improving Rice Leaf Disease Identification with Object Detection and Image Enhancement

Chowdhury, Rafi Hassan; Ahmed, Faria; Annesha, Tasfia Tasneem

dc.contributor.author	Chowdhury, Rafi Hassan
dc.contributor.author	Ahmed, Faria
dc.contributor.author	Annesha, Tasfia Tasneem
dc.date.accessioned	2025-03-10T07:58:45Z
dc.date.available	2025-03-10T07:58:45Z
dc.date.issued	2024-06-11
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dc.identifier.uri	http://hdl.handle.net/123456789/2373
dc.description	Supervised by Mr. Tareque Mohmud Chowdhury, Assistant Professor, Mr. Njayou Youssouf, Lecturer, Ms. Sabrina Islam, Lecturer, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Computer Science and Engineering, 2024	en_US
dc.description.abstract	Highly accurate rice leaf disease identification using lighter models can ensure food security globally and also less crop loss by maximizing the rate of disease identifica tion in proper time. Our thesis focus is on the process where the quality of the image is enhanced and the diseased part is detected for predicting the disease in the output. Image enhancement refers to the process where noisy, low-contrast, and low-quality image is taken care of. The object detection mechanism detects which part of the im age contains disease. The identification process specifies the disease from the given input. The currently available disease detection model seems to have limitations, such as - high-quality training and testing images, requiring high computational power, images with biased backgrounds, etc. Our proposed technique works on these limita tions by including an image enhancement technique, Lite SR-GAN, that gives a better result even if we train our models on low-quality images. To make the computation less complex we have used lightweight architectures in the different phases (image enhancement, detection, disease identification) of the model. Next, to address the is sue of biased images, we have used an object detection technique, YOLO-s, to detect the diseased part of the image. In the final stage, we introduce a lightweight model, EfficientViT that, with the help of previous layers, works equally fine with pictures of all qualities and identifies the disease using less computation power with an accuracy of 97.615%, inference time 33.99ms per image, parameter count 4.56M, model size 6.95MB and flop count 33.56B.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Disease_identification, lightweight_model, Lite_SRGAN, Yolov8n, EfficientViT	en_US
dc.title	Improving Rice Leaf Disease Identification with Object Detection and Image Enhancement	en_US
dc.type	Thesis	en_US