Abstract
This study proposes an evaluation method for fruit tree growth status using automatic annotation that does not require human intervention. The proposed method calculates the normalized difference vegetation index (NDVI) from hyperspectral images taken during the grape growing season, extracts the image values for each small area from the NDVI image, learns the correspondence between the shooting date and time data and the small area by deep learning, and estimates the period for the unlearned small area. The proposed method was applied to hyperspectral images taken in 2021 and 2022, and the growth status was estimated using the model trained on the small areas extracted by equal division. The growth rate was defined as the ratio of the number of small areas that were judged to be the same as the shooting period among all small areas, and the growth comparison showed that the growth progress is likely to be similar until around mid-August. The impact of downy mildew, which occurred frequently from around August 4, 2022, on the growth rate was not suggested, and the possibility that pinching, one of the cultivation management operations, affects the NDVI image was suggested.
