Rui Zhang^1,2, Tonghai Liu^1,2,*, Wenzheng Liu^1,2, Chaungchuang Yuan^1,2, Xiaoyue Seng^1,2, Tiantian Guo^1,2, Xue Wang^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397 - 27 June 2024

Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >

HuangJing Yu¹, Jinming Qiu¹, Ning Cao^2,*, Russell Higgs²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3181-3193, 2023, DOI:10.32604/csse.2023.030985 - 21 December 2022

Abstract Aiming at the dynamics and uncertainties of natural colors affected by the natural environment, a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoretical basis for plant dynamic color simulation and color sensor data transmission. Based on the HSL (Hue, Saturation, Lightness) color solid, the proposed method uses the function P-set to provide a color P-law generation model and an algorithm of the Dynamic Colors System (DCS), establishing the DCS modeling theory of the natural environment and the color P-reasoning simulation based on the HSL color solid.… More >

Lei Zhu¹, Guojiang Xin^1,2,*, Xin Wang¹, Changsong Ding^1,2, Hao Liang^1,2, Qilei Chen³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4727-4742, 2022, DOI:10.32604/cmc.2022.028187 - 28 July 2022

Abstract The collection and extraction of tongue images has always been an important part of intelligent tongue diagnosis. At present, the collection of tongue images generally needs to be completed in a sealed, stable light environment, which is not conducive to the promotion of extensive tongue image and intelligent tongue diagnosis. In response to the problem, a new algorithm named GCYTD (GELU-CA-YOLO Tongue Detection) is proposed to quickly detect and locate the tongue in a natural environment, which can greatly reduce the restriction of the tongue image collection environment. The algorithm is based on the YOLO… More >