The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

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

Optimizing yield and crop nitrogen response characterization by integrating spectral reflectance and agronomic properties in sugarcane and rice

Nitrogen (N) is one of the most important and limiting nutrients in crop production. The best management practices for N fertilization is always challenging due to its dynamic system in the nature. Remote sensing has emerged as one of the most useful technologies in modern agriculture for non-invasive monitoring of plant N status. The objectives of this research were to 1) determine the effect of water background turbidity and depth on red and red-edge reflectance based prediction models for biomass and grain yield in rice, 2) evaluate agronomic parameters of different sugar cane varieties in response to variable levels of nitrogen supply, and 3) determine the effect of sugarcane varieties on the relationships between spectral reflectance and agronomic parameters. Rice experiments were variety (CL152 and CL261) x N trial established in Crowley, LA in 2011 and 2012. Sugarcane experiments were variety (L 99-226, L 01-283, and HoCP 96-540) x N trial established in St. Gabriel and Jeanerette, LA from 2010 through 2012. Spectral reflectance and agronomic parameters were collected each week for three consecutive weeks beginning two weeks before panicle differentiation in rice and for four consecutive weeks beginning three weeks after N fertilization in sugarcane. There was no significant effect of water background (turbid or clear) on the spectral reflectance at panicle differentiation, one week after panicle differentiation, and at 50 % heading (p \u3c0.05). Water depth slightly influenced the reflectance at red waveband but this effect was not carried over when vegetation indices were computed. Use of red-edge based vegetation indices improved the estimation of biomass and grain yield in rice. The effect of variety on the accuracy of the yield prediction model varied depending on the transformation of reflectance within the red-edge and near infrared bands i.e., into normalized (NDVI) and simple ratio (SR) forms of vegetation indices. This result was associated with the behavior of near infrared wavebands on the geometrical structure of the plant canopy. There were no significant effects of variety on grain yield prediction models using derivative based red-edge indices. Our findings showed that red-edge based NDVI and SR are better predictors of rice grain yield than red-based NDVI and SR. Red-edge based NDVI or SR indices both have potential to predict rice grain yield and rice responsiveness to N fertilization. In sugarcane, the measured agronomic variables at early growth stage, i.e. biomass, tiller number, N content, height and FAI of three sugarcane varieties and their responses to N fertilizer were highly variable across year. The sugar yield response to N determined at harvest had stronger linear relationships with N response of biomass and N content at 4 to 5 weeks after N fertilization compared with N response of height and FAI. There were no differences in leaf spectral reflectance among varieties. In canopy level-spectral reflectance, wavebands at 450-500, 650-700, and 780-830 nm showed high correlation coefficient with agronomic parameters. The vegetation indices which have the potential for predicting biomass N uptake were red and red-edge based simple ratio and normalized difference vegetation index. Varietal effect on the models for estimating biomass and N uptake was significant only when red-based vegetation indices were used (p\u3c0.05). Addition of plant height in the model substantially improved biomass and N uptake estimation while diminishing the effect of variety. Remote sensing technology can be a potential tool to estimate biomass and N uptake in rice and sugarcane. The delivered information from this technology is useful to improve mid-season N management

Assessing the impact of spatial resolution of UAS-based remote sensing and spectral resolution of proximal sensing on crop nitrogen retrieval accuracy

Foliar nitrogen (N) plays a central role in photosynthetic machinery of plants, regulating their growth rates. However, field-based methods for monitoring plant N concentration are costly and limited in their ability to cover large spatial extents. In this study, we had two objectives: (1) assess the capability of unoccupied aerial system (UAS) and non-imaging spectroscopic data in estimating sorghum and corn N concentration and (2) determine the impact of spatial and spectral resolution of reflectance data on estimating sorghum and corn N concentration. We used a UAS and an ASD spectroradiometer to collect canopy- and leaf-level spectral data from sorghum and corn at experimental plots located in Stillwater, Oklahoma, U.S. We also collected foliage samples in the field and measured foliar N concentration in the lab for model validation. To assess the impact of spectral scale on estimating N concentration, we resampled our leaf-level ASD data to generate datasets with coarser spectral resolutions. To determine the impact of spatial scale on estimating N concentration, we resampled our UAS data to simulate five datasets with varying spatial resolutions ranging from 5 cm to 1 m. Finally, we used a suite of vegetation indices (VIs) and machine learning algorithms (MLAs) to estimate N concentration. Results from leaf-level ASD spectral data showed that the resampled data matching the spectral resolution of our UAS-based data at five spectral bands ranging from 360 to 900 nm provided sufficient spectral information to estimate plot-level sorghum and corn N concentration. Regarding spatial resolution, canopy-level UAS data resampled at multiple pixel sizes, ranging from 1 cm to 1 m were consistently capable of estimating N concentration. Overall, our findings indicate the possibility of developing monitoring instruments with optimal spectral and spatial resolution for estimating N concentration in crops

Integration of Optical Remote Sensor-Based Yield Prediction and Impact of Nitrogen Fertilization, Harvest Date, and Planting Scheme on Yield, Quality, and Biomass Chemical Composition in Energy Cane Production in Louisiana

The established sugarcane industry in Louisiana is perceived as an advantage for biofuel industry because of the similarities of energy cane and sugarcane by way they are cultivated, harvested, and processed. This study was conducted at the LSU AgCenter Sugar Research Station in St. Gabriel, LA from 2013-2015 to evaluate the influence of planting scheme, N rate, and harvest date on energy cane yield, quality parameters, nutrient uptake, and biomass chemical composition. The relationship of vegetation indices (VI) with stalk, fiber yield, and N uptake of energy cane harvested at different dates was also evaluated. The experiments consisted of variety (Ho 02-113, US 72-114), N rate (0, 56, 112, and 224 kg N ha-1) and harvest date (one- and two- months earlier harvest and scheduled harvest) as treatments arranged in split-split plot in a randomized complete block design with four replications. Another experiment was conducted with planting scheme (whole stalks vs. billets) and variety (Ho 02-113, US 72-114, Ho 06-9001, Ho 06-9002, L 01-299, and L 03-371) as factors arranged in split plot in randomized block design with four replications. Energy cane yield, quality parameters, chemical composition, and nutrient concentration and uptake were significantly affected by harvest date only. Both N rate and planting scheme did not affect biomass yield and quality. The nutrient removal rates between planting scheme were similar but not among harvest dates and varieties suggesting that the fertilizer recommendation will remain virtually the same for whole stalk- and billet-planted energy cane. The Pearson correlation analysis showed a strong dependence between VIs (i.e., simple ratio, normalized difference vegetation index) computed from reflectance readings at 670 (red) and 705 (red-edge) nm and stalk yield, N uptake, and fiber yield across cane age. The outcomes of this study show the: a) applicability of sugarcane cultural management practices for energy cane production, b) potential use of optical remote sensing in energy cane stalk and fiber yield prediction, and c) several areas of research emphasis to pursue for future studies on energy cane

Detection of multi-tomato leaf diseases (\u3ci\u3elate blight, target and bacterial spots\u3c/i\u3e) 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 neighbor 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

COVER CROPPING: SENSOR-BASED ESTIMATIONS OF BIOMASS YIELD AND NUTRIENT UPTAKE AND ITS IMPACT ON SUGARCANE PRODUCTIVITY

Sugarcane in Louisiana can be harvested for up to three years from one planting. Soil cultivation along sides of established beds is done for weed control and improve fertilizer use efficiency which increases the risk of soil degradation and yield decline. Planting cover crops (CC) is a soil conservation practice and an effective strategy to improve soil health and nutrient recycling. Limited work has been done on remote sensor-based evaluation of the potential nutrient benefits from cover crops and its effect on nutrient cycling on sugarcane systems. This study was conducted to evaluate the effect of two planting methods (broadcast and drilling) and three seeding rates (100%, 50%, and 25% of NRCS recommendation) of a mix of three legumes and two brassicas CC species and a control without CC, on sugarcane yield and quality parameters, and on soil nutrients levels. This study was also used for the acquisition of normalized difference vegetation index (NDVI), collected using GreenSeeker® and multispectral camera (MicaSense® - RedEdge-M) mounted on an unmanned aerial vehicle, to correlate with CC biomass and nutrient uptake. The NDVI readings and CC biomass clippings, using the quadrat frame method, were collected a week before CC termination. Tissue analysis was carried out by C:N dry combustion analyzer and nitric acid digestion-hydrogen peroxide for multi-element analysis. Cane yield was acquired with a chopper harvester and a dump billet wagon. Quality components were obtained by a SpectraCane® automated near infrared (NIR) analyzer for quality parameters. Soil inorganic nitrogen (N) content (NH4+ + NO3-) was quantified using KCl extraction procedure and flow injection analysis. Other soil nutrients content was determined based on Mehlich-3 extraction procedure followed by ICP. A strong positive correlation between the GreenSeeker NDVI (NDVI-GS) and aerial images derived NDVI (NDVI-AI) was obtained with a coefficient of determination (R2) value of 0.63. Adjustment of NDVI with, number of days, cumulative growing degree days, and number of days with positive growing degree days, from planting to sensing increased the R2 values up to 0.76, 0.76 and 0.73, respectively. The NDVI-GS obtain a stronger linear relationship with CC dry biomass and N content than NDVI-AI. Good positive correlations (0.48 \u3e R2 \u3e 0.12) were found between NDVI and some macronutrients (P and K) and micronutrients (Mn and Cu). Overall, there was no significant effect of planting method and seeding rate observed on cane yield and quality parameters. Moreover, there was no statistical difference on CC nutrient removal rate among the treatments (p\u3e0.05). For plant cane, the average cane and sugar yield across sites was 96 Mg ha-1 and 10794 kg ha-1, respectively. Lower yield was attained by the ratoon crops averaging only at 71 Mg ha-1 cane yield and 7197 kg ha-1 sugar yield. Remote sensing is a promising and viable technique to estimate CC biomass and nutrient uptake. Finally, this study corroborates the long-term effect of CC on nutrient management and their effect on cane yield and quality parameters

An investigation into the detection of sugarcane African stalk borer (Eldana saccharina Walker (Lepidoptera : Pyralidae)) using hyperspectral data (spectroradiometry).

Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2009.The South African Sugarcane production is one of the world’s leading sugarcane (Saccharum spp. Hybrid) producers. However, in recent years Eldana saccharina Walker has been the most destructive pest in South African sugarcane production, causing great crop loses per annum and is the most important factor limiting sugarcane productivity. The pest has been monitored using a traditional visual approach whereby a representative sample of stalks is taken from a field and split longitudinally to assess damage and count the number of E. saccharina larvae and pupae. However, this approach is time-consuming, labour intensive and sometimes biased as only easily accessible areas are often surveyed. In order to investigate a more economical but equally effective survey methodology, this study aimed to determine the potential of using hyperspectral remote sensing (spectroradiometry) for identifying sugarcane attacked by E. saccharina. A hand-held spectroradiometer ASD Field Spec® 3 was used to collect leaf spectral measurements of sugarcane plants from a potted-plant trial taking place under shade house conditions at the South African Sugarcane Research Institute (SASRI). In this trial, nitrogen (N) and silicon (Si) fertilizers were applied at known levels to sugarcane varieties. Varieties were either resistant or intermediate resistant or susceptible to E. saccharina attack. In addition, watering regimes and artificial infestation of E. saccharina were carefully controlled. Results illustrated that severe E. saccharina infestation increased spectral reflectance throughout the whole spectrum range (400 – 2500 nm) and caused a red-edge shift to the shorter wavelength. Eldana saccharina stalk damage was also linearly related to modified normalized difference vegetation index (mNDVI) using R2025 and R2200 (R2 = 0.69). It was concluded that hyperspectral data has a potential for use in monitoring E. saccharina in sugarcane rapidly and non-destructively under controlled conditions. A followup study is recommended in field conditions and using airborne and/or spaceborne hyperspectral sensors