Diagnosing plant leaf diseases is essential for agricultural development. Leaves are an important part of the plant and are often where signs of disease appear. With the support of image processing algorithms, researchers have widely used them to support disease detection tasks on plant leaves. Transfer learning approaches have revealed encouraging results in many domains but require fine-tuning hyperparameter values. Additionally, a combination with noise reduction can lead to positive potential effects in improving performance. This study proposes an approach leveraging a noise reduction technique based on Soft-Thresholding with Lasso regression and then performing the disease classification with a fine-tuned ShuffleNetV2. The experimental results on 14,400 images of 24 plant leaf disease classes of 10 various plant species show that the Threshold-based noise reduction combined with a fine-tuned ShuffleNetV2 can obtain better performance in disease classification on plant leaves than the original model and several considered transfer learning methods.
Tạp chí: International scientific conference proceedings “Enhancing cooperation to promote sustainable tourism in response to climate change, the fourth industrial revolution and artificial intelligence" 2024, Trường Đại học Nam Cần Thơ
Tạp chí: 8th International ICONTECH CONGRESS on Innovative Surveys in Positive Sciences, March 16-18, 2024, Azerbaijan Cooperation University, Baku, Azerbaijan
Tạp chí khoa học Trường Đại học Cần Thơ
Lầu 4, Nhà Điều Hành, Khu II, đường 3/2, P. Xuân Khánh, Q. Ninh Kiều, TP. Cần Thơ
Điện thoại: (0292) 3 872 157; Email: tapchidhct@ctu.edu.vn
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