油菜直播机组自动对厢作业控制器设计与试验

针对基于视觉的油菜直播机组自动对厢作业控制,本研究提出了结合模糊控制和带死区的PD控制的组合控制器,其中模糊控制器作为对厢作业路径跟踪控制器,带死区的PD控制器作为直播机组转向控制器。根据直播机组运动模型和相机成像模型,分析了图像路径参数跟随直播机组运动的变化规律,并设计模糊控制器的控制规则。同时,在图像中将直播机组与目标厢沟相对位置没有偏差时的图像路径标定出来作为图像目标路径,据此以图像实时检测路径与图像目标路径的角度偏差和截距偏差设计为模糊控制器输入,前轮目标转角为模糊控制器输出。对直播机组前轮转向控制则设计了带死区的PD控制器,通过两者的有机结合实现了直播机组的自动对厢作业。田间试验结果表明:油菜直播机以0.5或0.8m/s的速度行驶时,直线导航跟踪的横向偏差小于6cm,以1.0m/s的速度行驶时,横向偏差小于10cm。     

Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system

Classification of oil palm fresh fruit bunch (FFB) maturity is a critical factor that dictates the quality of produced palm oil. This study evaluates a multi-band portable, active optical sensor system; comprising of four spectral bands, 570, 670, 750, and 870 nm, to detect oil palm FFB maturity. The in-field spectral reflectance data were collected using the sensor system from a total of 120 fresh fruit bunches. These fruit bunches were categories into unripe, ripe, and overripe classes. Different classifiers were applied to assess the applicability of using the sensor system. Based on the classification accuracies, data analysis on the spectral features (reflectance data and other features extracted from vegetation indices) indicated that the spectral reflectance data could be valuable in predicting the maturity of the fruit bunches. The quadratic discriminant analysis and discriminant analysis with Mahalanobis distance classifiers yielded highest average overall accuracies of greater than 85% in classifying oil palm FFB maturity. Additionally, the average individual class (unripe, ripe, and overripe) classification accuracies were also higher than 80%. Thus, optical sensing using four-band sensor system could be useful for oil palm FFB maturity classification under field condition.     

Real-time,volatile-detection-assisted control for microwave drying

A real-time, volatile-detection-assisted control system was designed for microwave drying. Detected volatile signals were integrated to a fuzzy logic algorithm to determine the drying temperature. A phase controller was used to automatically and continuously adjust the microwave power. A data acquisition unit with developed program was employed to integrate the entire control. Carrot samples were used in system tests. The results showed that the designed system could successfully achieve the desired temperature, power, and volatiles control and lead to acceptable product's quality.     

Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors

The objective of this study was to evaluate the crop monitoring performance of a consumer-grade camera with non-modified and modified spectral ranges which are commonly used in low-altitude unmanned aerial vehicle (UAV) platforms. The camera was fixed sequentially with seven types of filters for collecting visible images and near-infrared (NIR) images with different center band locations and bandwidths. Meanwhile, field-based hyperspectral data and normalized difference vegetation index (NDVI) measured by a GreenSeeker handheld crop sensor (GS-NDVI) were collected to examine the accuracy of rapeseed growth monitoring in terms of vegetation indices (VIs) derived from UAV images. Results showed that the UAV-based RGB-VIs and optimal NIR-VIs had similar accuracy for predicting GS-NDVI. Moreover, similar results were achieved based on the hyperspectral data, indicating the importance of spectral characteristics for GS-NDVI estimation. However, the UAV-based results also indicated that the performance of VIs derived from the band combinations containing longer NIR center wavelengths and narrower bandwidths was obviously poorer than that of the RGB-VIs. The image quality of the NIR band was also found to be inferior to the visible band based on quantitative analysis, which also revealed that image quality had great impact on UAV-based results. Image quality was then related to the effects of camera exposure, spectral sensitivity, soil background and dark areas. The results from this study provide useful information for camera modifications by selecting appropriate filters that not only are sensitive to crop growth, but also ensure image quality.

     

Real-time, volatile-detection-assisted control for microwave drying

A real-time, volatile-detection-assisted control system was designed for microwave drying. Detected volatile signals were integrated to a fuzzy logic algorithm to determine the drying temperature. A phase controller was used to automatically and continuously adjust the microwave power. A data acquisition unit with developed program was employed to integrate the entire control. Carrot samples were used in system tests. The results showed that the designed system could successfully achieve the desired temperature, power, and volatiles control and lead to acceptable product's quality.     

Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert.     

Hyperspectral image analysis for water stress detection of apple trees

Plant stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress would allow for a timely intervention and mitigation of the problem before critical thresholds were exceeded, thereby maximizing productivity. The spectral signature of plant leaves was analyzed by a hyperspectral camera to identify the onset and intensity of plant water stress. Five different levels of water treatment were created in young apple trees (cv. ‘Buckeye Gala’) in a greenhouse. The trees were periodically monitored with a hyperspectral camera along with an active-illuminated spectral vegetation sensor and a digital color camera. Individual spectral images over a 385-1000 nm wavelength range were extracted at a specific wavelength to estimate reflectance and generate spectral profiles for the five different water treatment levels. Various spectral indices were calculated and correlated to stress levels. The highest correlation was found with Red Edge NDVI at 705 and 750 nm in narrowband indices and NDVI at 680 and 800 nm in broadband indices. The experimental results indicated that intelligent optical sensors could deliver decision support for plant stress detection and management.     

Development of an optical sensor for crop leaf chlorophyll content detection

Nitrogen content in crop leaf is an important indication for evaluating crop health and predicting crop yield. A normalized difference vegetation index (NDVI) is widely used as an indicator in estimating leaf nitrogen content in practice. How to effectively and accurately measure the NDVI value of crop leaves in the field is a challenge on in-field use instrument development. This paper reports the development of a hand-held spectroscopy-based optical sensing device for measuring crop leaf NDVI values under in-field natural light conditions. This optical sensing device could simultaneously measure the spectral reflectance of canopies and the solar intensity at two bands of 610 and 1220 nm, and calculate NDVI value in real-time based on measured spectral reflectance. This device was tested in tomato plants chlorophyll content measurement. A series of field tests were conducted to evaluate the performance of the sensor, and tomato leaf samples were collected for measuring chlorophyll contents as the reference for validation. Obtained results indicated that NDVI values measured with this sensing device had a close correlation with chlorophyll contents of the collected leaf samples measured in laboratory with a UV–vis spectrophotometer .     

A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration

This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.     

Verification of color vegetation indices for automated crop imaging applications

An accurate vegetation index is required to identify plant biomass versus soil and residue backgrounds for automated remote sensing and machine vision applications, plant ecological assessments, precision crop management, and weed control. An improved vegetation index, Excess Green minus Excess Red (ExG − ExR) was compared to the commonly used Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used an Otsu threshold value to convert the index near-binary to a full-binary image. The indices were tested with digital color image sets of single plants grown and taken in a greenhouse and field images of young soybean plants. Vegetative index accuracies using a separation quality factor algorithm were compared to hand-extracted plant regions of interest. A quality factor of one represented a near perfect binary match of the computer extracted plant target compared to the hand-extracted plant region. The ExG − ExR index had the highest quality factor of 0.88 ± 0.12 for all three weeks and soil-residue backgrounds for the greenhouse set. The ExG + Otsu and NDI − Otsu indices had similar but lower quality factors of 0.53 ± 0.39 and 0.54 ± 0.33 for the same sets, respectively. Field images of young soybeans against bare soil gave quality factors for both ExG − ExR and ExG + Otsu around 0.88 ± 0.07. The quality factor of NDI + Otsu using the same field images was 0.25 ± 0.08. The ExG − ExR index has a fixed, built-in zero threshold, so it does not need Otsu or any user selected threshold value. The ExG − ExR index worked especially well for fresh wheat straw backgrounds, where it was generally 55% more accurate than the ExG + Otsu and NDI + Otsu indices. Once a binary plant region of interest is identified with a vegetation index, other advanced image processing operations may be applied, such as identification of plant species for strategic weed control.     

基于PIC单片机的农田灌溉智能控制系统研究

以PIC16F877新型单片机为核心设计了全自动智能灌溉控制系统,该系统可根据土壤湿度传感器检测水分信息和天气环境温度来模糊决策灌水量,克服了以往以土壤湿度信息进行灌溉决策的不精确性,可以满足现代农业精量灌溉的需求。     

静止无功补偿器的智能自适应PID控制器设计

将神经网络和模糊控制与有着广泛应用ＰＩＤ控制相结合,设计了一种静止无功补偿器的智能自适应ＰＩＤ控制器。利用神经网络实现系统模型辨识,采用模糊逻辑和神经网络相结合对ＰＩＤ控制器参数动态寻优。使ＳＶＣ的控制既具有模糊控制的简单,有效的非线性控制作用,又具有神经网络的自学习,自适应能力。     

一种基于多模态图像技术的葡萄水分胁迫识别

提出了一种利用多模态图像技术,以实现被葡萄水分胁迫水平的测定方法,通过检测获取葡萄植株表面图像的反射率和纹理信息与水分胁迫水平之间的关系,从而实现植物缺水报警。试验将盆栽葡萄人为建立不同的水分胁迫水平,利用3CCD照相机(三通道的R,G和IR)、多光谱相机(在900,970 nm的光谱波段覆盖)和一个数字彩色摄像机(RGB)对叶片定期进行监测。试验采用偏最小二乘(PLS)方法预测水含量的纹理特征和光谱特性,在葡萄生长的前期,RGB相机获得的纹理参量对含水量预测结果的rp,RMSEP和偏差值分别为0.77,1.15和-0.14,而利用3CCD相机获取的反射参量对含水量预测结果的rp,RMSEP和偏差值分别为0.77,1.22和-0.26;在葡萄生长后期,RGB相机获取的纹理参量对含水量预测结果的rp,RMSEP和偏差值分别为0.81,1.34和0.26,而利用3CCD相机获取的反射参量对含水量预测结果的rp,RMSEP和偏差值分别为0.74,1.46和0.15。通过监测植株覆盖率与不同水分灌溉植株的生长周期发现,植株覆盖率能对葡萄植株的水分胁迫检测做辅助参考变量。试验结果表明,所设计的多传感器系统可用于支持葡萄水分胁迫检测的决策,有利于葡萄的田间管理。     

Space plasma physics: atmospheric emissions photometric imaging experiment

The atmospheric emissions photometric imaging experiment was flown on Spacelab 1 to study faint natural and artificial atmospheric emission phenomena. The instrument imaged optical emission in the region 2000 to 7500 angstroms with a television system consisting of two optical channels, one wide-angle and one telephoto. A third optical channel imaged onto the photochathode of a microchannel plate photomultiplier tube that has 100 discrete anodes. A hand-held image intensifier camera with an objective grating permitted spectral analysis of the earth's airglow and the shuttle glow. Preliminary data show magnesium ion emission features in the lower ionosphere as well as the spececraft glow spectrum.     

Design of a hyperspectral nitrogen sensing system for orange leaves

The orange (Citrus sinensis) is one of the most important agricultural crops in Florida. Heavy reliance on agricultural chemicals and low fertilizer use efficiencies in citrus production have raised environmental and economic concerns. In this study, a nitrogen sensor was developed to predict nitrogen concentrations in orange leaves. Four design criteria were chosen to maximize the sensing efficiency and reliability. They were: (1) coverage of the spectral N sensing range, (2) no moving parts, (3) single leaf detection, and (4) diffuse reflectance measurement. Based on chlorophyll and protein spectral absorption bands, the sensor's wavelength ranges were chosen to be 620–950 nm and 1400–2500 nm. A reflectance housing was designed to block environmental noise and to ensure single leaf measurement. A halogen light source, two detector arrays, two linear variable filters, and data acquisition cards with 16-bit analog-to-digital converters were used to collect data. The designed N sensor had a spectral resolution less than 30 nm. Test results showed that the nitrogen sensor had good linearity (r > 0.99) and stability. With averaged signal-to-noise ratio (SNR) of 299, the system was able to predict N content with a root mean square difference (RMSD) of l.69 g kg⁻¹ for the validation data set. Using the N sensor, unknown leaf samples could be classified into low, medium and high N levels with 70% accuracy.     

土地利用覆被遥感影像信息提取方法研究综述

阐述了土地利用覆被、遥感影像信息等相关概念,概述了目前在影像信息计算机提取过程中所使用的方法:监督分类与非监督分类,人工神经网络,小波分析,模糊逻辑分类,基于知识库的专家系统,基于"3S"集成系统的分类。     

Registration of multispectral 3D points for plant inspection

Machine vision technologies have shown advantages for efficient and accurate plant inspection in precision agriculture. Regarding the balance between accuracy of inspection and compactness for infield applications, multispectral imaging systems would be more suitable than RGB colour cameras or hyperspectral imaging systems. Multispectral image registration (MIR) is a key issue for multispectral imaging systems, however, this task is challenging. First of all, in many cases, two images needing registration do not have a one-to-one linear mapping in 2D space and therefore they cannot be aligned in 2D images. Furthermore, the general MIR algorithms are limited to images with uniform intensity and are incapable of registering images with rich features. This study developed a machine vision system (MVS) and a MIR method which replaces 2D-2D image registration by 3D-3D point cloud registration. The system can register 3D point clouds of ultraviolet (UV), blue, green, red and near-infrared (NIR) spectra in 3D space. It was found that the point clouds of general plants created by images of different spectral bands have a complementary property, and therefore a combined point cloud, called multispectral 3D point cloud, is denser than any cloud created by a single spectral band. Intensity information of each spectral band is available in a multispectral 3D point cloud and therefore image fusion and 3D morphological analysis can be conducted in the cloud. The MVS could be used as a sensor of a robotic system to fulfil on-the-go infield plant inspection tasks.     

Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor

Operational airborne and satellite remote sensing in agriculture remains constrained by matching platform availability to suitable daytime weather and illumination conditions, crop development, and availability of ground staff. An ultra low-level aircraft carrying an active NIR/Red CropCircle™ sensor was successfully deployed to record and subsequently map crop vigour via the simple ratio (SR) index over a field of sorghum. Given the logging frequency of ≈20 Hz and the presence of alternate rows of bare soil, the Moiré effect reduced the contrast between crop and bare soil skip-rows. Such effects would not be expected to occur in non-skip-row crops. The ultra low-level airborne (ULLA)-SR map derived from the 20 m transect records compared favorably with the SR map derived from a meter-resolution airborne digital multispectral image that was re-sampled to a similar spatial resolution. This case study, involving a CropCircle™ sensor mounted in a low-level aircraft demonstrates another deployment option for users of this class of sensor. Moreover, an ULLA configuration offers the potential for greater flexibility in scheduling compared to airborne imaging, given it can be flown at any sun-angle, under cloud, at night, and may easily be incorporated into aircraft already conducting low-level operations, for example crop dusting and reconnaissance, over agricultural fields.     

Enhanced broadband greenness in assessing Chlorophyll a and b,Carotenoid, and Nitrogen in Robusta coffee plantations using a digital camera

Direct-leaves measurement of spectral indices using a digital camera with a portable small chamber and custom illumination is used to take images of 600 leaves from 40 coffee plants. In this research, several vegetation indices (VIs) are developed and evaluated. Through a series of experiments, Chlorophyll a and b, Carotenoids, and Nitrogen critical level of Robusta coffee plants are analyzed and evaluated using selected VIs obtained from spectra of different tools like Spectrometer, digital camera, and SPAD-502 Chlorophyll meter. The actual Nitrogen critical level was determined using Kjeldahl laboratory test. Beside Hue, the newly proposed VIs could significantly improve the correlation in estimating photosynthetic pigments (Chlorophyll a and b, Carotenoids) and Nitrogen critical level of Robusta coffee plant. Finally, consumer-grade digital camera with custom chamber is shown to be used for rapid and accurate in situ estimation of Chlorophyll a and b, Carotenoids, and Nitrogen critical level of Robusta coffee plant from direct-leaves measurement.     

遥感影像正立体化研究

山脊与沟谷视觉效果相反的反立体现象,是遥感影像目视判读中的常见问题。消除反立体现象,使影像地物显示正立体化,对加强遥感影像的使用,特别是对非专业人员具有重要意义。本文提出了阴影模型(SRM)条件参与的IHS融合法、SRM乘法融合法和纯地形纠正法3种遥感影像正立体化方法,并与5种已有的方法进行QuickBird影像实验。通过比对证明:在实现影像正立体化的同时,这3种方法的整体视觉效果和地物光谱保留方面要优于其他方法。其中,相比已有的SRM参与的IHS融合法,SRM条件参与的IHS融合法是一种改进,能获得色调与原始影像相似的正立体影像;SRM乘法融合法算法简单、正立体影像视觉效果较好,适用于影像效果制图;纯地形纠正方法通过改变光源位置实现正立体化,从原理上保留了地物光谱信息,可用于定量研究。