Evapotranspiration (ET) estimation is important agricultural research in many regions because of the water scarcity, growing population, and climate change. ET can be analyzed as the sum of evaporation from the soil and transpiration from the crops to the atmosphere. The accurate estimation and mapping of ET are necessary for crop water management. One traditional method is to use the crop coefficient (Kc) and reference ET (ETo) to estimate actual ET. With the advent of satellite technology, remote sensing images can provide spatially distributed measurements. Satellite images are used to calculate the Normalized Difference Vegetation Index (NDVI). The relation between NDVI and Kc is used to generate a new Kc. The spatial resolution of multispectral satellite images, however, is in the range of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Moreover, the frequency of satellite overpasses is not high enough to meet the research or water management needs. The Unmanned Aerial Vehicles (UAVs) can help mitigate these spatial and temporal challenges. Compared with satellite imagery, the spatial resolution of UAV images can be as high as centimeter-level. In this study, a regression model was developed using the Deep Stochastic Configuration Networks (DeepSCNs). Actual evapotranspiration was estimated and compared with lysimeter data in an experimental pomegranate orchard. The UAV imagery provided a spatial and tree-by-tree view of ET distribution.
In the last decade, technologies of unmanned aerial vehicles (UAVs) and small imaging sensors have achieved a significant improvement in terms of equipment cost, operation cost and image quality. These low-cost platforms provide flexible access to high resolution visible and multispectral images. As a result, many studies have been conducted regarding the applications in precision agriculture, such as water stress detection, nutrient status detection, yield prediction, etc. Different from traditional satellite low-resolution images, high-resolution UAVbased images allow much more freedom in image post-processing. For example, the very first procedure in post-processing is pixel classification, or image segmentation for extracting region of interest(ROI). With the very high resolution, it becomes possible to classify pixels from a UAV-based image, yet it is still a challenge to conduct pixel classification using traditional remote sensing features such as vegetation indices (VIs), especially considering various changes during the growing season such as light intensity, crop size, crop color etc. Thanks to the development of deep learning technologies, it provides a general framework to solve this problem. In this study, we proposed to use deep learning methods to conduct image segmentation. We created our data set of pomegranate trees by flying an off-shelf commercial camera at 30 meters above the ground around noon, during the whole growing season from the beginning of April to the middle of October 2017. We then trained and tested two convolutional network based methods U-Net and Mask R-CNN using this data set. Finally, we compared their performances with our dataset aerial images of pomegranate trees. [Tiebiao- add a sentence to summarize the findings and their implications to precision agriculture]
Many studies have shown that hyperspectral measurements can help monitor crop health status, such as water stress, nutrition stress, pest stress, etc. However, applications of hyperspectral cameras or scanners are still very limited in precision agriculture. The resolution of satellite hyperspectral images is too low to provide the information in the desired scale. The resolution of either field spectrometer or aerial hyperspectral cameras is fairly high, but their cost is too high to be afforded by growers. In this study, we are interested in if the flow-cost hyperspectral scanner SCIO can serve as a crop monitoring tool to provide crop health information for decision support. In an onion test site, there were three irrigation levels and four types of soil amendment, randomly assigned to 36 plots with three replicates for each treatment combination. Each month, three onion plant samples were collected from the test site and fresh weight, dry weight, root length, shoot length etc. were measured for each plant. Meanwhile, three spectral measurements were made for each leaf of the sample plant using both a field spectrometer and a hyperspectral scanner. We applied dimension reduction methods to extract low-dimension features. Based on the data set of these features and their labels, several classifiers were built to infer the field treatment of onions. Tests on validation dataset (25 percent of the total measurements) showed that this low-cost hyperspectral scanner is a promising tool for crop water stress monitoring, though its performance is worse than the field spectrometer Apogee. The traditional field spectrometer yields the best accuracy as high as above 80%, whereas the best accuracy of SCIO is around 50%.
Irrigated potato production in sandy soils can be impacted by low nitrogen (N) and water retention in the soil. A field study was conducted to use canopy spectral reflectance as a primary means to characterize N fertilizer rates and soil texture variations as growth and yield limiting factors in potato. A hand-held 16-band spectral radiometer was used to obtain reflectance readings of the potato canopies. Reflectance measurements were made in field plots that received four rates of N or in four areas where the soil textures were different. At later stages of plant growth, canopy reflectance in the 760 to 1000 nm spectral range was consistently higher in plots that received higher rates of N or in areas where the soil contained higher clay and silt fractions. Russet Burbank potatoes, with increasing rate of N fertilizer, showed a decreasing trend in total tuber yield and an increasing trend in percent of tubers with weight exceeding 170 g. Canopy reflectance was inversely related to tuber yield or size for Russet Burbank potatoes when soil texture was the only variable.
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