Color-Ratio Maps Enhanced Optical Filter Design and Its Application in Green Pepper Segmentation.

Abstract

There is a growing demand for developing image sensor systems to aid fruit and vegetable harvesting, and crop growth prediction in precision agriculture. In this paper, we present an end-to-end optimization approach for the simultaneous design of optical filters and green pepper segmentation neural networks. Our optimization method modeled the optical filter as one learnable neural network layer and attached it to the subsequent camera spectral response (CSR) layer and segmentation neural network for green pepper segmentation. We used not only the standard red-green-blue output from the CSR layer but also the color-ratio maps as additional cues in the visible wavelength and to augment the feature maps as the input for segmentation. We evaluated how well our proposed color-ratio maps enhanced optical filter design methods in our collected dataset. We find that our proposed method can yield a better performance than both an optical filter RGB system without color-ratio maps and a raw RGB camera (without an optical filter) system. The proposed learning-based framework can potentially build better image sensor systems for green pepper segmentation.

Plant phenotyping relevance

緑ピーマンの画像分割を対象に、光学フィルタとカメラ応答・ニューラルネットワークを同時最適化する画像センシング手法を開発し、RGB方式などと性能比較しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's green pepper hyperspectral dataset (133 images) is described as self-collected and not stated to be publicly available; no author code, models, or data deposit URL is given. The only URL (labelme) is a generic third-party annotation tool, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.