Optimized Angles of the Swing Hyperspectral Imaging Tower for Single Corn Plant

During recent years, hyperspectral imaging systems have been widely applied in the greenhouses for plant phenotyping purposes. Current imaging systems are mostly designed as either top view or side view imaging mode. Top-view is an ideal imaging angle for top leaves which are often more flat with more uniform reflectance. However, most bottom leaves are either blocked or shaded from top view. From side view, most leaves are viewable, and the entire structure can be imaged. However, at this angle most of the leaves are not facing the camera, which will impact the measurement quality. At the same time, there could be advantages with certain tilted imaging angle between top view and side view. Therefore, it’s important to explore the impact of different imaging angles to the phenotyping quality. For this purpose, we designed a swing hyperspectral imaging tower which enables us to rotate the camera and lighting source to capture images at any angle from side view (0◦) to top view (90◦). 36 corn plants were grown and divided into 3 different treatments: high nitrogen (N) and well-watered (control group), high N and drought-stressed, and low N and well-watered. Each plant was imaged at 7 different angles from 0◦ to 90◦ with an interval of 15◦. According to different treatments applied on experimental samples, two comparative pairs were set up: drought-stressed group vs. control group (Pair 1); N-deficiency group vs. control group (Pair 2). In this study, normalized difference vegetation index (NDVI) and relative water content (RWC) were computed and compared to determine optimized imaging angle(s). For NDVI, the imaging angle near to top view is optimized to separate Pair 1, while, the imaging angle near to side view is optimized to distinguish Pair 2. For RWC, partial least square regression (PLSR) models were applied to predict pixel-level RWC distribution of each plant, and higher imaging angles (close to top view) are better to tell the RWC distribution difference in Pair 1. In conclusion, higher imaging angles (close to top view) are better to separate different water treatments, while, lower imaging angles (close to side view) are better to separate different N treatments

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Quantifying physiological trait variation with automated hyperspectral imaging in rice

Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R2 = 0.797 and RMSEP = 0.264 for N; R2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Remote sensing of crop biophysical parameters for site-specific agriculture

xiv, 194 leaves : ill. (some col.) ; 29 cm.Support for sustainable agriculture by farmers and consumers is increasing as environmental and socio-economic issues rise due to more intensive farm practices. Site-specific crop management is an important component of sutainable agriculture, within which remote sensing can play an integral role. Field and image data were acquired over a farm in Saskatchewan as part of a national research project to demonstrate the advantages of site-specific agriculture for farmers. This research involved the estimation of crop biophysical parameters from airborne hyperspectral imagery using Spectral Mixture Analysis (SMA), a relatively new sub-pixel scale image processing method that derives the fraction of sunlit canopy, soil and shadow that is contributing to a pixel's relectance. SMA of three crop types (peas, wheat and canola) performed slightly better than conventional vegetation indices in predicting leaf area index (LAI) and biomass using Probe-1 imagery acquired early in the growing season. Other potential advantages for SMA were also indentified, and it was conclude that future research is warranted to assess the full potential of SMA in a multi-temporal sense throughout the growing season

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application