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Design of a real-time, embedded weed-detection and spray-control system.

机译：实时嵌入式杂草检测和喷雾控制系统的设计。

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An optical weed sensor and a real-time, embedded weed-detection and spray-control system were developed and evaluated in this study.; Spectral reflectance characteristics of various plant species and soil were analyzed in order to select feature wavelengths that maximized the contrasts between major object categories—weed leaves, weed stems, crop leaves, crop stems, and soil—for weed detection. Relative color indices insensitive to illumination variation were developed at these feature wavelengths. Several calibration models for differentiating weeds against crops and soil were developed using statistical methods of partial least squares and discriminant analysis. The best classification model achieved classification rates of 98.3%, 98.7%, and 64.3% for wheat, bare soil, and weeds, respectively. The classification rates achieved for the validation data set were 83.1%, 79.5%, and 62.5%, respectively.; Based on the classification model, an optical sensor was designed and tested. The effective sensing area of the sensor was determined through a laboratory test. The sensor was tested on different weed densities. When multiple weeds shared the sensor's effective sensing area with soil, a classification rate of 65% was achieved for weeds. The classification rate fell below 50% for single weeds. However, under field conditions, the sensor successfully detected weeds at densities of 0.5 plants/dm2 or above with a classification rate of higher than 96.9%.; Two optical weed sensors and their control modules, a central-control module, a GPS device, and a spray-control module were successfully integrated into a realtime, embedded system. The system components were networked using a Controller Area Network. The system was tested extensively in two wheat fields. With good training, the system generally reached weed-detection accuracies of over 80%. The addition of a light-blocking screen and artificial lights facilitated the use of the system under variable light conditions, including night operations. Classification models trained with multiple weed species improved the classification accuracy. Classification accuracy also was affected by the position of the sensor relative to the training sample during training. At the current design, the total cost for hardware of the system with two weed sensors is about {dollar}2,500.*; *This dissertation includes a CD that is compound (contains both a paper copy and a CD as part of the dissertation). The CD requires the following applications: Windows MediaPlayer or RealPlayer.
机译：本研究开发并评估了光学杂草传感器和实时嵌入式杂草检测与喷雾控制系统。分析了各种植物物种和土壤的光谱反射特性，以便选择特征波长，以使主要对象类别（杂草叶，杂草茎，农作物叶，农作物茎和土壤）之间的对比度最大化，以进行杂草检测。在这些特征波长下开发了对照明变化不敏感的相对颜色指数。使用偏最小二乘和判别分析的统计方法，开发了几种用于区分杂草对作物和土壤的校准模型。最佳分类模型对小​​麦，裸土和杂草的分类率分别达到98.3％，98.7％和64.3％。验证数据集的分类率分别为83.1％，79.5％和62.5％。基于分类模型，设计并测试了光学传感器。传感器的有效感应区域是通过实验室测试确定的。该传感器在不同的杂草密度下进行了测试。当多种杂草与土壤共享传感器的有效传感区域时，杂草的分类率达到65％。单杂草的分类率降至50％以下。但是，在田间条件下，该传感器以0.5株/ dm2或更高的密度成功地检测到杂草，分类率高于96.9％。两个光学杂草传感器及其控制模块，中央控制模块，GPS设备和喷雾控制模块已成功集成到实时嵌入式系统中。使用控制器局域网将系统组件联网。该系统在两个麦田中进行了广泛的测试。通过良好的培训，该系统通常可达到80％以上的杂草检测精度。遮光屏和人造光的加入促进了该系统在各种光照条件下的使用，包括夜间操作。使用多种杂草物种训练的分类模型提高了分类准确性。在训练过程中，分类精度也受到传感器相对于训练样本的位置的影响。在当前设计下，具有两个杂草传感器的系统的硬件总成本约为2500美元*。 *本论文包括一张复合CD（该论文既包含纸质副本，又包含CD）。该CD需要以下应用程序：Windows MediaPlayer或RealPlayer。

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