Doctor of Philosophy / Department of Biological & Agricultural Engineering / Naiqian Zhang / One of precision agriculture researches currently focuses on the relationship between plant phenotype, genotype, and ambient environment, including critical investigations of a multi-sensor-integrated phenotyping platform and data mining technology for big data. This study examined the designs of two phenotyping platforms and developed machine vision (MV) technology to estimate wheat growth status and count wheat head. The GreenSeeker, an infrared thermometer (IRT), a web camera, and a global positioning system (GPS) receiver were integrated into one handheld phenotyping platform, named as Phenocorn. The Phenocorn allowed simultaneous collection of the normalized difference vegetative index (NDVI) and canopy temperature (CT) with precise assignment of all measurements to plot location by GPS data points. The Phenocorn was tested using a field trial of 10 historical and current elite wheat (Triticum aestivium) breeding lines at the International Maize and Wheat Improvement Center (CIMMYT) in Ciudad Obregon, Mexico, during the 2013 and 2014 growing seasons. Results showed that the NDVI data, PVC (percent vegetation coverage) data, and temperature data obtained by the handheld phenocorn could availably reflect the wheat growing status in the field, and the handheld phenocorn could be used as an instrument to do plant phenotyping information collection. This study also used the modular design method to design the mechanical structures of a robot-based phenotyping platform, named as Phenorobot. Its control system was based on a Controller Area Network (CAN bus). The basic function performances such as steering function, lifter load, and movement features were tested in the laboratory. Proposed design indicators were achieved, demonstrating its potential utilization for field experiments. Image acquisition is one of the main data collection methods for plant phenotyping research. The method for extracting plant phenotyping traits based on MV was explored in this research. Experiments for detecting the wheat development based on the images taken in the field were designed and carried out from March to June 2015, and a method based on color analysis to estimate percent vegetation coverage (PVC) of wheat was developed. A wheat growth model based on the PVC was used for the wheat growth status analysis. In addition, a wheat head counting method was developed and divided into three steps: wheat head image segmentation, leaf debris elimination, and wheat head counting. This paper proposes the first wheat head counting model (WCM) based on the pixels group measurement of wheat heads. Compared to the Joint Points Counting (JPC) method (Liu et al., 2014) and the Wheatear Shape Index (WSI) method (Frédéric et al., 2012), the WCM more accurately counted wheat heads from images taken in the experiments.

Abstract Fishpond area of Sihcao, Tainan City, approximate 488 hectors, is a part of coastal wetlands of Taiwan. Fishponds can be found almost everywhere in the wetlands, readily forming a fragmented ecosystem. Previous studies on populations of small mammals in coastal wetlands of Taiwan are scarce. Disturbance types found in the study area include straying dogs, working farmers, tillaging of the fishponds, and typhoons, etc, which would probably reduce the abundance of small mammals. The resources such as shelter, vegetation coverage, insects and seeds would increase the abundance of small mammals. The seasonal changes of these environmental factors may in turn affect the population dynamics of small mammals. I monitored the monthly population fluctuation of small mammals and invertebrates in the fishpond habitats, 2008. Six species of small mammals and 106 species of invertebrates were found. The small mammal populations in the Hairy Beggar Ticks region were compared with those within the non-Hairy Beggar Ticks region. More small mammals were found in the Hairy Beggar Ticks region in the sampling period. The vegetation coverage of the former was higher than that of the latter. There was positive correlation between evenness of small mammals and temperature. No differences of the biodiversity of invertebrates were found between the regions in the year. Significant correlation existed between the sunshine duration and the richness of invertebrates. The correlation may be due to the change of photoperiod. There was no significant correlation between the richness of small mammals and that of invertebrates.

Water quality of a stream or river is influenced by the surrounding landscape and vegetation. The Normalized Difference Vegetation Index (NDVI) is commonly used to characterize landcover and vegetation density. Benthic macroinvertebrates are ubiquitous in freshwater streams and are excellent indicators of the quality of freshwater habitats. Data from one NDVI remote sensing flight and one macroinvertebrate sampling event for eight wadeable stream study sites in the National Ecological Observatory Network (NEON) were acquired. Proportions of high, moderate, and sparse vegetation were calculated for each stream watershed using ArcGIS. Functional feeding groups and tolerance values were assigned to macroinvertebrate taxa. The Fourth-corner and RLQ methods of analysis, available in the ade4 package for R software, were used to evaluate the relationships of macroinvertebrate traits with environmental variables. Hypothesis testing using Model 6 in the ade4 package resulted in p-values of 0.066 and 0.057 for global (overall) significance. Mean NDVI values of moderately vegetated areas and proportion of sparse vegetation were found to be significant to percent shredders at alpha ≤ 0.05. Results of these methods of analysis, when combined with traditional macroinvertebrate sampling metrics, show that NDVI can be a useful, additional tool to characterize a watershed and its effects on macroinvertebrate community composition and structure.