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亚洲精细农业与计算机农业应用联合大会

作者： sakae shibusawa Faculty of Agriculture Tokyo University of Agriculture and technology 3-8-5 Saiwai-cho Fuchu Tokyo 183-8509 Japan

The paper describes current three topics in Japan: new responsibility of land policy, technology package in precision agriculture, and thinking process for farm management. The land uses strategy should be changed by serious impacts of the last food crisis, where a keen behind story was that production increase could not catch up with the demand increase during the last decade. All farm land even house garden is required for the contribution to food production. City governments get started to combat against the food crisis by promoting their city agriculture, which makes a new trend of food business and agriculture. Precision agriculture reaches a new stage of technology package which is inducing innovations in farm management. A combination of the real-time soil spectrophotometer and a combine harvester with a yield monitor, for example, creates environmentally impact map of missing nitrogen. A model of decision making process redefines by four learning phases and also creates eleven process units of thinking, and then decision process support will be discussed by weed control practice as a case study.

关键词： Evidence-based Thinking process Agro-Informatics Technology package

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A COMPARISION OF MACHINE VISION SYSTEM AND NEAR-INFRARED REFLECTANCE SPECTROSOPY IN TOMATO INNER-QULITY ANALYSIS

A COMPARISION OF MACHINE VISION SYSTEM AND NEAR-INFRARED REF...

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亚洲精细农业与计算机农业应用联合大会

作者： sakae shibusawa Kojima Yoichiro Faculty of Agriculture Tokyo University of Agriculture and Technology 3-5-8 Saiwai-Cho Fuchu Tokyo 183-8509 Japan

In order to produce the more information-added production, the grading system and automatically fruits selection system is often used. Fruits grading is carried out using non-destructive inner quality evaluation method such as relative density, imaging sensor, near-infrared spectroscopy （NIR）, X-ray, terra-hertz light. However, in many cases, these methods have some problems that they can only measure some specific factor such as sugar content, etc. The inner-quality evaluation of agricultural production could not be decided on only one factor. In this paper, the sugar content, amino acid content, acid content and water content of tomatoes will be measured at the same time by two different methods. The present work compares the machine vision system technology and near-infrared reflectance spectroscopy （NIR） in tomato inner-quality analysis. The machine vision system is effective on measuring the fruit outer-quality, but the NIR is often used for the inner-quality. This paper applied machine vision technology to get the inner-quality of tomato, and make a comparison with the result from NIR. Due to the different fruit color, tomatoes of different periods were considered in the experiment, the growing period and the matured period. 4 kinds of important inner-quality of tomato, sugar content and amino-acid value were measured in this experiment. In NIR, the spectra were taken by inner quality sensor used 710-949nm spectra with 1nm resolution. The calibration model with 710-949nm spectra converted to the second derivative. As a result, sugar content, amino acid content, acid content and water content of the tomatoes were predicted through NIR spectroscopy. On the other side, a stable light box was built due to the color image is sensitive to the light condition in machine vision system. LAB color model and the gray level co-occurrence matrix （GLCM） were used to catch the color feature and texture feature from the color image. The relationship between the feature and th

关键词： NIR machine vision tomato

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基于机器视觉的番茄内部品质预测

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农业工程学报 2010年第S2期26卷 366-370页

作者：张亚静 sakae shibusawa 李民赞中国农业大学现代精细农业系统集成研究教育部重点实验室北京100083 东京农工大学农学部

为了实现番茄内部品质的实施快速检测,利用机器视觉技术,从"定性"和"定量"两方面进行了番茄内部品质预测方法的研究。首先设计开发了番茄图像采集机器视觉系统,可分别从3个不同高度:0.5、1、1.5m和6个不同方向:上、下、左、右、前、后... 详细信息

为了实现番茄内部品质的实施快速检测,利用机器视觉技术,从"定性"和"定量"两方面进行了番茄内部品质预测方法的研究。首先设计开发了番茄图像采集机器视觉系统,可分别从3个不同高度:0.5、1、1.5m和6个不同方向:上、下、左、右、前、后采集番茄图像。视觉系统利用4个卤素灯作为光源,内部亮度恒定为600lx。然后收集了68个不同生长阶段的番茄样本,样本根据是颜色从未成熟阶段(绿色)到成熟阶段(红色)被分为了5个等级。在利用开发的机器视觉系统采集了番茄样本的图像之后,通过RGB色彩模型、L*a*b*色彩模型和灰度共生矩阵(GLCM)计算番茄图像特征值,并将其输入BP神经网络,对糖度、酸度、氨基酸含量和水分含量共4种番茄内部品质进行预测。在"定量"预测中,分别建立了每种内部品质的预测模型。结果表明,酸度与图像特征之间的相关系数最高为0.536,定量预测精度还有待进一步提高。在"定性"预测中,利用BP神经网络,通过番茄内部属性含量的不同组合值预测番茄生长阶段,对隐层节点数和训练函数这两个重要的网络参数进行优化。试验中使用40个样本作为训练集建立模型,使用28个样本作为测试集,其中22个样本预测正确,结果表明利用机器视觉方法预测番茄内部品质具有较好应用前景。

关键词：农产品神经网络图像处理

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