Learning an optical filter for green pepper automatic picking in agriculture
Learning an optical filter for green pepper automatic picking in agriculture
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DOI：
10.1016/j.compag.2021.106521
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Xinzhi Liu;Jun Yu;T. Kurihara;Ke Li;Zhao Niu;Shu Zhan
中科院分区：
文献类型：
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作者：
Xinzhi Liu;Jun Yu;T. Kurihara;Ke Li;Zhao Niu;Shu Zhan
由于青椒和绿叶的颜色相似，青椒的自动采摘一直是农业中的一个长期挑战。为了解决这个棘手的问题，我们试图利用高光谱信息作为先验知识来区分它们。作为我们的核心洞察力，我们设计了一种新型的光学滤光片，作为一种预处理工具，以找到辣椒与叶子有很大差异的有价值的波长。为此，首先利用神经网络对像素级高光谱输入进行端到端训练，学习滤光片的参数。其次，将所学习的滤光片应用于高光谱数据，得到滤波后的RGB图像，并将其送入进一步的分割框架。在此基础上，提出了一种两阶段分割青椒的方法，由于引入了滤光片，取得了良好的分割效果。
Green pepper automatic picking has been a long-standing challenge in agriculture due to the similar color between green peppers and green leaves. To tackle this intractable problem, we tried to distinguish between them by using hyperspectral information as prior knowledge. As our core insight, a novel optical filter was designed as a pre-processing tool to find valuable wavelengths where peppers differ a lot from leaves. To this end, firstly, the parameters of the optical filter were learned by end-to-end training with a neural network for pixel-wise hyperspectral input. Secondly, the learned optical filter was applied to hyperspectral data to obtain filtering RGB images, which will be sent to further segmentation framework. Thereby, a two-stage method for green pepper segmentation was proposed, and promising results were achieved owing to the incorporation of the optical filter.