Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images.

Abstract

Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.

Plant phenotyping relevance

RGB画像から果実をセグメンテーションし、画素数で果実サイズと成長を時系列推定する手法開発が中心である。

Code and data availability

The authors explicitly state that their trained CROP neural network dictionaries and related programs are publicly available on GitHub. The paper's image datasets (Data_Fruit from Pixabay, farm pear images) are described but the annotations/datasets themselves are not deposited at a public URL; the USDA ARS image and C