Analysis of spectral response patterns of Kiwifruit orchards using satellite imagery to predict orchard characteristics of commercial value before harvest : : a thesis presented in fulfilment of the requirements for the degree of PhD Prod Tech in the School of Engineering and Advanced Technology at Massey University, Palmerston North, New Zealand

Several characteristics of kiwifruit determine its value to the kiwifruit marketing company, Zespri Ltd, and to the grower. The foremost of these is the dry matter content. Much effort is expended in predicting the final dry matter content of the fruit as early in the season as possible so that the optimal dry matter content can be achieved. Dry matter content is currently measured through a destructive 90-fruit protocol that may be repeated several times in a season on each maturity block. Remote sensing data available from modern satellites can provide four-colour (red, green, blue and near-infrared) data with resolution down to 1-2m, less than the size of one kiwifruit vine. Many indices can be created from these and correlated to the characteristics of plants with indifferent results. This thesis presents the development of an index wherein the four colours are used to create a three-dimensional unit colour vector that is largely independent of light level. This transform was used to allow the direct visualisation of data from a number of satellite images of the Te Puke kiwifruit growing area in New Zealand over five years, for which dry matter content values were available from the 90-fruit protocol. An attenuation model was chosen to correct the top-of-atmosphere light intensities recorded by the satellite cameras to those at ground level. The method of Hall et al., (1991) was found to reduce the variation of fiduciary pixels by the largest amount and was used. The visualisation revealed that there was an axis along which dry matter was ordered by magnitude. A regression line of best fit was applied to this data producing an R2 value of 0.51 with a standard mean-square error of 0.76. This is significantly lower than the average mean-square error of 1.05 for the 90-fruit protocol. Comparison of the predictive power of other indices, based on one image, showed a range of R2 values of 0.008 to 0.49. The method developed in this thesis produced an R2 of 0.70 for the same data

Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor.

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields

Uumanned Aerial Vehicle Data Analysis For High-throughput Plant Phenotyping

The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short time and by less labor. There are currently many options for customized UAV system on market; however, data analysis efficiency is still one limitation for the fully implementation of HTPP. To this end, the focus of this program was data analysis of UAV acquired data. The specific objectives were two-fold, one was to investigate statistical correlations between UAV derived phenotypic traits and manually measured sorghum biomass, nitrogen and chlorophyll content. Another was to conduct variable selection on the phenotypic parameters calculated from UAV derived vegetation index (VI) and plant height maps, aiming to find out the principal parameters that contribute most in explaining winter wheat grain yield. Corresponding, two studies were carried out. Good correlations between UAV-derived VI/plant height and sorghum biomass/nitrogen/chlorophyll in the first study suggested that UAV-based HTPP has great potential in facilitating genetic improvement. For the second study, variable selection results from the single-year data showed that plant height related parameters, especially from later season, contributed more in explaining grain yield. Advisor: Yeyin Sh

Spectral analysis of hybrid bermudagrass placed under various combinations of nitrogen and water availability

Remote sensing technology that uses a movable ground-based system has promise for rapid, accurate and objective evaluation of turfgrass quality for instantaneous nitrogen and water application correction. Such a test has been made on hybrid bermudagrass \u27Tifway\u27 [ Cynodon dactylon (L.) Pers. x C. transvaalensis Burtt-Davy] through a 2-year field study at the Center for Urban Water Conservation in the city of North Las Vegas. Ten combinations of water and nitrogen treatments including cyclic and steady conditions were imposed on twenty experimental plots, with two replications per treatment. Treatments consisted of five N treatments: High Steady Nitrogen (HSN), Low Steady Nitrogen (LSN), High Pulse Nitrogen (HPN), Low Pulse Nitrogen (LPN), High Incremental Nitrogen (HIN), Low Incremental Nitrogen (LIN); and three water treatments based on leaching fractions: Low Leaching Fraction (LLF = -0.15), High Leaching Fraction (HLF = +0.15), Low to High Leaching Fraction (LHLF ranging from -0.25 to +0.25) all combined with N treatments in ten different combinations. Canopy spectral reflectance measurements were acquired on a biweekly basis. (Abstract shortened by UMI.)

Evaluation of the Use of UAV-Derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring Based on Machine Learning Algorithms and SHAP Analysis

(c) The Author/sfals

Remote sensing of moisture and nutrient stress in turfgrass systems

Management of irrigation and fertility on a golf course or other large turfgrass area requires a significant amount of time scouting for and identifying problem areas to maintain optimum turfgrass quality. The objectives of these studies were to evaluate the relationship between remotely sensed reflectance data collected from a turfgrass canopy and the associated phosphorus and nitrogen content of turfgrass tissue, and to determine the relationship between reflectance data and soil moisture content as determined by time domain reflectometry (TDR). Phosphorus deficiency symptoms decreased and biomass production increased at P rates above 1.0 g m-2 with a single application while no increase in soil-P was observed. Reflectance measurements were taken in increments from 400 to 1050 nm and correlated with plant tissue P concentration, chlorophyll content, plant biomass and visual quality. Stepwise regression identified a model utilizing reflectance in the blue, yellow, orange, and red regions of the spectrum that explained 73% of the variability in plant tissue P concentration for all sampling dates in 2002 and 2003. Few correlations were found between vegetative indices such as the normalized difference vegetation index (NDVI) and plant response. Results indicate that P deficiencies of creeping bentgrass can be detected through the use of remote sensing. P deficiencies were corrected with a single foliar application of P at rates above 1.5 g m-2. Using partial least-squares regression, our results indicate a weak relationship between the actual and predicted values for turfgrass quality, biomass production, and chlorophyll content under varying rates of N fertilization. However, a strong relationship was observed between actual and predicted values for N concentration of the plant tissue during 2002 and 2003 (r2 = 0.90 and 0.74 respectively). Similarly, no correlation was observed between visual drought stress ratings and the associated soil moisture content for samples collected one day before the onset of visible drought stress. However, PLS regression indicates a strong relationship between actual and predicted soil moisture content based on reflectance data one day prior to onset of drought stress symptoms (r = 0.79)