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.

     

Calibration of a common shortwave multispectral camera system for quantitative agricultural applications

Unmanned aerial systems (UAS) for collecting multispectral imagery of agricultural fields are becoming more affordable and accessible. However, there is need to validate calibration of sensors on these systems when using them for quantitative analyses such as evapotranspiration, and other modeling for agricultural applications. The results of laboratory testing of a MicaSense (Seattle, WA, USA) RedEdge? 3 multispectral camera and MicaSense Downwelling Light Sensor (irradiance sensor) system using a calibrated integrating sphere were presented. Responses of the camera and irradiance sensor were linear over many light levels and became non-linear at light levels below expected real-world, field conditions. Simple linear corrections should suffice for most light conditions encountered during the growing season. Using an irradiance sensor or similar system may not properly account for light variability in cloudy or partly cloudy conditions as also identified by others. A simple stand for aiding in reference panel imaging was also described, which may facilitate repetitive, consistent reference panel imaging.

     

Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables

Precision Agriculture - The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this...     

Mobile low-cost 3D camera maize crop height measurements under field conditions

To tackle global challenges such as food supply and renewable energy provision, the improvement of efficiency and productivity in agriculture is of high importance. Site-specific information about crop height plays an important role in reaching these goals. Crop height can be derived with a variety of approaches including the analysis of three-dimensional (3D) geodata. Crop height values derived from 3D geodata of maize (1.88 and 2.35 m average height) captured with a low-cost 3D camera were examined. Data were collected with a unique measurement setup including the mobile mounting of the 3D camera, and data acquisition under field conditions including wind and sunlight. Furthermore, the data were located in a global co-ordinate system with a straightforward approach, which can strongly reduce computational efforts and which can subsequently support near real-time data processing in the field. Based upon a comparison between crop height values derived from 3D geodata captured with the low-cost approach, and high-end terrestrial laser scanning reference data, minimum RMS and standard deviation values of 0.13 m (6.91% of average crop height), and maximum R² values of 0.79 were achieved. It can be concluded that the crop height measurements derived from data captured with the introduced setup can provide valuable input for tasks such as biomass estimation. Overall, the setup is considered to be a valuable extension for agricultural machines which will provide complementary crop height measurements for various agricultural applications.     

Field performance in agricultural settings of a wireless temperature monitoring system based on a low-cost infrared sensor

Continuous measurement of plant canopy temperature is useful in both research and production agriculture settings. Industrial-quality infrared thermometers which are often used for measurement of canopy temperatures, while reliable, are not always cost effective. For this study a relatively low-cost, consumer-quality infrared thermometer was incorporated into a wireless monitoring system intended for use in plant physiological studies and in agricultural production settings. The field performance of this low-cost wireless system was compared to that of a typical research system based on an industrial-quality infrared thermometer. Performance was evaluated in terms of: reliability of data acquisition, quality of seasonal temperature measurements, seasonal stability of the consumer-quality infrared sensor, and the equivalence of temperatures measured by the consumer-quality and industrial-quality temperature sensors. Results indicate that for many common uses of plant temperature data, the two sensors provide functionally equivalent results. The cost savings and ease of use associated with the low-cost wireless temperature monitoring system present advantages over the higher-cost industrial-quality sensors which may make them a viable alternative in many agricultural settings.     

Mapping crop ground cover using airborne multispectral digital imagery

Empirical relationships between remotely sensed vegetation indices and canopy density information, such as leaf area index or ground cover (GC), are commonly used to derive spatial information in many precision farming operations. In this study, we modified an existing methodology that does not depend on empirical relationships and extended it to derive crop GC from high resolution aerial imagery. Using this procedure, GC is calculated for every pixel in the aerial imagery by dividing the perpendicular vegetation index (PVI) of each pixel by the PVI of full canopy. The study was conducted during the summer growing seasons of 2007 and 2008, and involves airborne and ground truth data from 13 agricultural fields in the Southern High Plains of the USA. The results show that the method described in this study can be used to estimate crop GC from high-resolution aerial images with an overall accuracy within 3% of their true values.     

红外相机技术在鼠类密度估算中的应用

2009年5月至2011年7月,在古田山国家自然保护区24 hm2(600 m×400 m)样地中用分层随机抽样法布置20台红外相机,监测样地内的鼠类密度.利用红外相机技术,引用物理学中气体分子碰撞率原理,在不对鼠类进行个体识别的情况下,估算样地内鼠类密度.结果表明,以此估算的样地内鼠类密度D3与标志重捕法估算的鼠类密度D之间不存在显著差异(P＞0.05),两者契合程度高,说明此模型具有相当高的精确性.而在较高的相机分布密度(0.83台/hm2)下得出的鼠类密度季节消长状况也验证了该模型的可靠程度.     

响应面法优化棒状乳杆菌HS4廉价增殖培养基研究

本研究对棒状乳杆菌的廉价增殖培养基进行筛选及优化,以达到降低棒状乳杆菌菌剂的生产成本。通过比较棒状乳杆菌HS4在9种基础培养基中的生长情况,确定冬瓜基础培养基为增殖基础培养基;经单一营养因子试验和Plackett-Burman设计试验,确定影响棒状乳杆菌HS4增殖的3个重要因素分别为大豆蛋白胨、牛肉膏和酵母浸粉;经响应面分析进行回归优化,获得最佳冬瓜增殖培养基配方为:冬瓜基础培养基(1L),大豆蛋白胨(19.7g/L),酵母浸粉(19.4g/L)和牛肉膏(1.94g/L),活菌数达到39.81×108 CFU/mL。     

使用摄录一体机制作教学片

资金短缺、设备不全的单位制作电视教学片是件很困难的事。利用摄录一体机的简单编辑功能 ,结合多年实践经验 ,介绍使用摄录一体机制作教学录像片的技巧和方法。     

Trimap generation for digital matting using camera exposure

This paper proposed a method to automatically generate the trimap for digital matting.Camera parameters of aperture and shutter speed are used to control its exposure,and accordingly to take pictures of stationary foreground with blurred background.Our method was inspired by color difference matting,both of which require a pre-record background image.In our method,only one image was required.Upon this input image,the process of blurring-deblurring,subtraction,thresholding and dilation were applied to finally generate the trimap.No user's direct interface with the image was needed,and the user only needed to adjust the threshold or width of dilation for some input images.It reduces users' conservative interaction,and results are reliable for most of the pictures.     

Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

Precision Agriculture - Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map...     

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Precision Agriculture - Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops....     

Agricultural applications of a low-cost infrared thermometer

Plant canopy temperature is used in many studies of plant/environment interactions and non-contact measurement is often made with radiometric surface thermometers commonly referred to as infrared thermometers. Industrial-quality infrared thermocouples are widely available and often used in agricultural research. While research on canopy temperature has provided management tools for production agriculture, the high cost of the industrial-quality infrared thermocouples has limited their adoption and use in production agriculture settings. Our objective was to evaluate a low-cost consumer-quality infrared thermocouple as a component of a wireless thermal monitoring system designed for use in a production agriculture setting. The performances of industrial-quality and low-cost consumer-quality sensors were compared under controlled constant temperature and under field conditions using both grass and cotton canopies. Results demonstrate that under controlled constant-temperature the two types of infrared thermocouples were “significantly the same” at 10 °C, 20 °C and 30 °C and “significantly not the same” at 40 °C and 50 °C. Across the temperature range tested, the consumer-quality infrared thermocouples temperature reading was closer to the thermocouple reading than the industrial-quality infrared thermocouples. A field comparison of industrial-quality and consumer-quality infrared thermocouple sensors monitoring a grass canopy and a cotton canopy indicated that the two types of sensors were similar over a 13–35 °C range. The measurement of temperature made with two types of sensors would not differ significantly. Based on these results we conclude that the lower-cost consumer-quality infrared thermometers are suitable for use in production agricultural applications.     

不同成熟度草莓鲜榨果汁的电子鼻和电子舌检测

采用电子鼻和电子舌,通过主成分分析(principal component analysis,PCA)、线性判别分析(linear discriminant analysis,LDA)对不同成熟度草莓鲜榨果汁的风味品质进行检测区分,并通过偏最小二乘回归分析(partial least squares,PLS)建立电子鼻和电子舌传感器响应信号与草莓鲜榨汁理化指标之间的关系,定量预测草莓的品质指标.结果表明:PCA和LDA法均对不同成熟度草莓鲜榨汁的区分效果较好,且电子舌的区分效果好于电子鼻,电子鼻和电子舌传感器响应信号融合后的区分效果与电子舌相当;通过PLS法,电子舌传感器响应信号能够较好地预测不同成熟度草莓鲜榨汁的可溶性固形物含量、Vc含量和pH值,但对总酸含量的预测能力稍差,其校正决定系数R2为0.876,预测决定系数R2为0.793;电子鼻和电子舌传感器响应信号融合后能够很好地预测不同成熟度草莓鲜榨汁的可溶性固形物、Vc和总酸含量及pH值,其预测能力好于单一的电子鼻或电子舌,其中对于电子舌不能很好预测的总酸含量其校正决定系数和预测决定系数分别上升为0.965和0.908.表明采用上述方法能对样品进行较好地区分且能对样品的品质指标进行较好地预测,其中电子鼻和电子舌信号融合对样品的区分能力和预测能力增强,验证了电子鼻和电子舌结合是对样品气味和味道的综合信息进行评价.     

Mineralogic information from a new airborne thermal infrared multispectral scanner

A new six-channel aircraft multispectral scanner has been developed to exploit mineral signature information at wavelengths between 8 and 12 micrometers. Preliminary results show that igneous rock units can be identified from their free silica content, and that carbonate as well as clay-bearing units are readily separable on the digitally processed images.     

Moisture Stress as a Requirement for Flowering of Coffee

When coffee plants are watered at relatively short intervals, so as to maintain the water content of the soil at close to field capacity, the flower buds remain dormant and no fruits are formed. Irrigation or rain induces flowering only when preceded by a period of water shortage. Water stress is apparently essential to break the dormancy of coffee flower buds.     

Improved vegetation segmentation with ground shadow removal using an HDR camera

A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application (<1 s required). The proposed ground shadow detection and removal method enhances the performance of vegetation segmentation under natural illumination conditions in the field and is feasible for real-time field applications.     

Development of a low-cost attitude sensor for agricultural vehicles

The objective of this research was to develop a low-cost attitude sensor for agricultural vehicles. The attitude sensor was composed of three vibratory gyroscopes and two inclinometers. A sensor fusion algorithm was developed to estimate tilt angles (roll and pitch) by least-squares method. In the algorithm, the drift error of the gyroscopes was estimated using the inclinometers. In addition to tilt angles, the attitude sensor also estimated the absolute heading angle and position with inclination error correction by integrating a GPS. Tests were conducted on a flat field, a sloping ground and a bumpy road. Results showed that the attitude sensor was able to estimate the roll angle with the maximum root mean square error of 0.43°, the pitch angle with 0.61° and the heading angle with 0.64°. Moreover, the attitude sensor dramatically improved the positioning accuracy from 25.9 cm to 3.0 cm in the sloping ground test and from 8.4 cm to 3.7 cm in the bumpy road test. The proposed technology used in the attitude sensor will help to develop advanced agricultural applications.     

River discharge monitoring using a vertically moving side-looking acoustic Doppler profiler

This paper presents an innovative river discharge monitoring system which uses a horizontal acoustic Doppler current profiler (H-ADCP) attached to a mechatronic system that provides vertical motion to different depths. Also, an extended index velocity method is developed which utilises velocity measurements from different depths to achieve more accurate discharge calculation under complex flow conditions. The prototype system was developed and installed near the estuaries of a river where backwater and tidal motion was known to be present due to small distance and altitude difference from the sea. Five experiments were performed, and in two experiments the proposed method performed roughly the same as the standard index velocity method. In the other three experiments the proposed method performed much better and the difference between the discharges was as large as 10%. The results indicate that a vertically moving H-ADCP can be used to calculate river discharge more accurately in complex flow environments.     

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R²=0.89, RMSE=0.68 g m^–2, RE=14.62% for calibration and R²=0.83, RMSE=1.08 g m^–2, RE=19.71% for validation) was much better than that at the all-leaf level (R²=0.73, RMSE=2.20 g m^–2, RE=26.70% for calibration and R²=0.70, RMSE=2.48 g m^–2, RE=31.49% for validation) and at the plant level (R²=0.66, RMSE=4.46 g m^–2, RE=30.96% for calibration and R²=0.63, RMSE=3.69 g m^–2, RE=24.81% for validation). Similarly, the XGB model (R²=0.65, RMSE=0.09, RE=8.59% for calibration and R²=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R²=0.62, RMSE=0.10, RE=7.92% for calibration and R²=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R²=0.64, RMSE=0.09, RE=9.24% for calibration and R²=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.