Food security is a major concern of the 21st century, given climate change and population growth. In addition, high salt concentrations in soils affect ~20% of irrigated land and cause a substantial reduction in crop yield. Cultivating salt-tolerant crops could enable the use of salt-affected agricultural land, reduce the use of fresh water and alleviate yield losses. Innovative methods need to be developed to study traditional and novel traits that contribute to salinity tolerance and accurately quantify them. These studies would eventually serve for developing new salt tolerant crops, adapted to the harsh arid and semi-arid climate conditions. A study of 200 accessions of the wild tomatoes (Solanum pimpinellifolium) was conducted in field conditions with phenotyping using an unmanned aerial vehicle (UAV)-mounted hyperspectral camera. Six genotypes with different levels of salt tolerance were sampled for leaf pigment analyses, revealing a clear pattern for the high salt tolerant accession M007, where pigment content in the salt-treated plants significantly increased compared to their control counterparts only in harvesting campaigns 3 and 6, each performed two days after the first and second salt stress application events. Moreover, the light harvesting capacity was found to be better maintained under salt stress in the medium (M255) and highly salt tolerant (M007 and M061) accessions. Pigment quantitation data will contribute towards the groundtruthing of hyperspectral imaging for the development of remote sensing-based predictive pigment mapping methods. This work establishes a reliable quantification protocol for correlating pigment content with vegetation indices. Hence, pigment content captured by imaging techniques and validated using biochemical analysis would serve in developing a high-throughput method for pigment quantitation in the field using UAV-based hyperspectral imaging. This would serve as a tool for measuring pigment content in large number of genotypes in the field which would eventually lead to new salt-tolerant genes.

<p>Nowadays, soybean is one the most consumed crops in the world. As the human population continuously increases, new phenotyping technology is needed to help plant scientists breed soybean that has high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most commonly used technologies for phenotyping. The current HSI techniques include HSI tower and remote sensing on an unmanned aerial vehicle (UAV) or satellite. There are several noise sources the current HSI technologies suffer from such as changes in lighting conditions, leaf angle, and other environmental factors. To reduce the noise on HS images, a new portable, leaf-level, high-resolution HSI device was developed for corn leaves in 2018 called LeafSpec. Due to the previous design requiring a sliding action along the leaf which could damage the leaf if used on a soybean leaf, a new design of the LeafSpec was built to meet the requirements of scanning soybean leaves. The new LeafSpec device protects the leaf between two sheets of glass, and the scanning action is automated by using motors and servos. After the HS images have been collected, the current modeling method for HS images starts by averaging all the plant pixels to one spectrum which causes a loss of information because of the non-uniformity of the leaf. When comparing the two modeling methods, one uses the mean normalized difference vegetation index (NDVI) and the other uses the NDVI heatmap of the entire leaf to predict the nitrogen content of soybean plants. The model that uses NDVI heatmap shows a significant increase in prediction accuracy with an R2 increase from 0.805 to 0.871. Therefore, it can be concluded that the changes occurring within the leaf can be used to train a better prediction model. </p> <p>Although the LeafSpec device can provide high-resolution leaf-level HS images to the researcher for the first time, it suffers from two major drawbacks: intensive labor needed to gather the image data and slow throughput. A new idea is proposed to use a UAV that carries a 6 degree of freedom (DOF) robotic arm with a LeafSpec device as an end-effect to automatically gather soybean leaf HS images. A new UAV is designed and built to carry the large payload weight of the robotic arm and LeafSpec.</p>