Tomato Growth State Map for the Automation of Monitoring and Harvesting
Tomato Growth State Map for the Automation of Monitoring and Harvesting
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DOI：
10.20965/jrm.2020.p1279
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发表时间：
2020-12-01
影响因子：
1.1
通讯作者：
Ishii, Kazuo
中科院分区：
文献类型：
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作者：
Fujinaga, Takuya;Yasukawa, Shinsuke;Ishii, Kazuo
为了实现智慧农业，我们致力于其系统化，从监测到使用机器人收获番茄果实。在本文中，我们解释了一种方法，生成一个地图的番茄生长状态，以监测番茄果实的各个阶段，并决定收获策略的机器人。番茄生长状态图将成熟期、收获时间和产量之间的关系可视化。提出了番茄生长状态图的生成方法、番茄果实的识别方法和番茄生长状态（成熟期和采收期）的估计方法。对于番茄果实识别，我们证明了使用有限学习数据集和红外图像上番茄果实的光学特性的简单机器学习方法超过了更复杂的卷积神经网络，尽管结果取决于如何创建训练数据集。为了估计生长状态，我们对有经验的农民进行了调查，将成熟阶段量化为六个分类，将收获时间量化为三个术语。根据调查结果估计了生长状态。为了验证番茄生长状态图，我们在实际番茄温室中进行了实验，并在此报告结果。
To realize smart agriculture, we engaged in its systematization, from monitoring to harvesting tomato fruits using robots. In this paper, we explain a method of generating a map of the tomato growth states to monitor the various stages of tomato fruits and decide a harvesting strategy for the robots. The tomato growth state map visualizes the relationship between the maturity stage, harvest time, and yield. We propose a generation method of the tomato growth state map, a recognition method of tomato fruits, and an estimation method of the growth states (maturity stages and harvest times). For tomato fruit recognition, we demonstrate that a simple machine learning method using a limited learning dataset and the optical properties of tomato fruits on infrared images exceeds more complex convolutional neural network, although the results depend on how the training dataset is created. For the estimation of the growth states, we conducted a survey of experienced farmers to quantify the maturity stages into six classifications and harvest times into three terms. The growth states were estimated based on the survey results. To verify the tomato growth state map, we conducted experiments in an actual tomato greenhouse and herein report the results.