Zhilin Li^1,2, Yuxin Li¹, Chunyu Yan¹, Peng Yan¹, Xiutong Li¹, Mei Yu¹, Tingchi Wen^4,5, Benliang Xie^1,2,3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526 - 18 July 2024

Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease More >

Qingmin Pan¹, Yongzong Lu¹, Liang Xue², Yongguang Hu^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.2, pp. 411-421, 2023, DOI:10.32604/phyton.2022.023437 - 12 October 2022

Abstract The wettability of leaf surface, commonly represented by contact angle (CA), affects various physiological and physical processes. The present study aims to better understand the wettability of tea leaves and elucidate its influence on the energy barrier of the droplet condensation process. The CA values of different leaf ages (young, mature and old) of five famous tea cultivars (Maolu, longjing 43, Huangjinya, Zhongcha 108 and Anji Baicha) were measured via the sessile drop method, and the micro-morphology of two cultivars leaves (Maolu, Zhongcha 108) was investigated by a 3D super depth-of-field digital microscope. Specifically, two radically distinctive types of CA trends… More >

Santhana Krishnan Jayapal¹, Sivakumar Poruran^2,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1261-1275, 2023, DOI:10.32604/iasc.2023.026564 - 06 June 2022

Abstract Tea plant cultivation plays a significant role in the Indian economy. The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant. Various climatic factors and other parameters cause these diseases. In this paper, the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy. Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval. Deep Hashing with Integrated Autoencoders… More >