Weeds growing in the spaces between hedges and under canopies in tea fields are problematic, because these weeds disturb the management of tea fields and contaminate harvested products. Therefore, we developed a weeding machine for tea fields that can cut weeds growing in the spaces between hedges and under canopies in tea fields by attaching weed cutters to both rear sides of a human-driven tea-plucking machine. In this study, we evaluated the weeding efficiency of the weeding machine in the spaces between hedges and under canopies in a tea field in a field test at the Tea Science Research Center, Shizuoka Prefectural Research Institute of Agriculture and Forestry. Weeding with this machine at 0.2 m/s reduced the fresh weight of weeds immediately after weeding by 61.9 ％ in the spaces between hedges and by 94.2 ％ under the canopies, compared to before weeding. In a field test in an organic tea field in Shizuoka City, Japan, the vegetation coverage in the spaces between hedges and under canopies before weeding was 75.0 ％, and immediately after weeding with the weeding machine at 0.3 m/s, it was significantly reduced to 7.0 ％, and was significantly lower at 13.0 ％ even one month after weeding. In addition, we examined the most effective weeding period and weeding frequency with the weeding machine in a tea field where intertillage was performed. By weeding with this machine every two months (March, May, July, September) from March to September, the vegetation coverage in the spaces between hedges and under canopies at the plucking time of each tea season was kept to 50 ％ or less. Furthermore, the maximum plant height of each weed species was suppressed to less than 15 cm. Therefore, we conclude that weeds can be effectively suppressed by weeding with this machine four times at two-month intervals from March to September in tea fields that are subjected to intertillage.

The tradition of brewing and drinking green tea is slowly fading away. However, home economics classes that educate students about tea culture provide an excellent opportunity to rekindle interest in this practice. This study analyzed the written feedback of 115 students who participated in a green tea brewing class. Our analysis revealed that while the teachers primarily focused on imparting knowledge and skills, the students were more captivated by the taste sensation associated with brewing green tea. Consequently, offering tea classes tailored to students' interests can foster a deeper appreciation of culinary culture and inspire a love for tea.

We developed a simple method to estimate the growth stage of tea shoots to reduce the labor involved of tea farmers. We developed a program using Python to estimate the number of opened leaves of tea shoots by capturing pictures of the surface of tea trees using a smartphone and mask processing, with the exception of new shoots. The accuracy of the mask processing was 84%, and the process was successful. The saturation values of the extracted shoots were correlated with the number of opened leaves regardless of the camera used, and a model was developed to estimate the number of open leaves based on the saturation values. Using this model, we compared the estimated number of opened leaves with the actual number of opened leaves measured at eight tea gardens in Shizuoka Prefecture; the root mean square error of prediction (RMSEP) value of the estimation was 0.33 leaves.