David Michael Glenn

Characteristics of Active Spectral Sensor for Plant Sensing

Plant stress has been estimated by spectral signature using both passive and active sensors. As optical sensors measure reflected light from a target, changes in illumination conditions critically affect sensor response. Active spectral sensors minimize the illumination effects by producing their own illumination, which is reflected from the target and measured by the detector. Although active sensors use modulated radiation that can be differentiated from ambient illumination, sensor performance characteristics must be well understood and examined in different target conditions of plant foliage in order to validate the data and increase the accuracy. In this article, the performance of a commercial active spectral sensor, GreenSeeker, was evaluated to study the effects of: partial canopy coverage, target off-center, standoff distance, target surface tilting, wetness of target surface, illumination and temperature, bidirectional solar angle, and diurnal solar radiation. Experiments examined a valid range of sensor responses and identified a major effect of relative humidity that was amplified by moistened surfaces, resulting in an increase of NDVI response up to 41%. These evaluations illustrate the potentials and limitations of active spectral sensors for plant sensing and provide a guideline to understanding sensor performance in order to improve measurement accuracy.

Hyperspectral Image Analysis for Plant Stress Detection

Plant stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress allows for timely intervention and mitigation of the problem before critical thresholds are exceeded, thereby maximizing productivity. A hyperspectral camera analyzed the spectral signature of plant leaves to identify the plant water stress. Five different levels of water treatment were created on young apple trees (Buckeye Gala) in a greenhouse and continuously monitored with a hyperspectral camera along with an active-illuminated spectral vegetation sensor and a digital color camera. Individual spectral images over a 400 – 1000 nm wavelength range were extracted at a specific wavelength to estimate reflectance and generate spectral profiles for five groups of apple trees at different water treatment levels. Various spectral indices were investigated and correlated to stress levels. The highest correlation was found with Red Edge NDVI at 705 nm and 750 nm in narrowband indices and NDVI at 680 nm and 800 nm in broadband indices. The experimental results indicate that intelligent optical sensors could deliver decision support for plant stress detection and management.

Active Spectral Sensor Evaluation under Varying Conditions

Plant stress has been estimated by spectral signature using both passive and active sensors. As optical sensors measure reflected light from a target, changes in illumination characteristics critically affect sensor response. Active sensors minimize the illumination effects by producing their own illumination that is reflected from the target and measured by the detector. Although active sensors use modulated radiation that can be differentiated from ambient illumination, in order to validate data and increase the accuracy, sensor performance characteristics must be well understood and examined in different target conditions of plant leaves. The performance of an active NDVI sensor was evaluated to study the effect of: 1) partial canopy coverage, 2) target off-center, 3) standoff distance, 4) target surface tilting, 5) solar bidirectional effect, 6) temperature, and 7) diurnal radiation change. These evaluations provide a valid range of sensor measurements and a motivation to improve the measurement accuracy by using selective data that can be validated by supplemental sensors.

Effect of Ultraviolet Radiation Environment on Leaf Quantum Efficiencies and Photosynthesis for Tropical and Temperate Species

ABSTRACT Ultraviolet radiation has detrimental effects on plants and levels are expected to rise through mid-century. Photosystem II is the most vulnerable site in chlorophyll affecting productivity. The purpose of this study was to evaluate leaf-level interactions with UV radiation in tropical and temperate crops. The species sampled were (1) citrus, (2) banana, (3) coffee, (4) pineapple, (5) olive, (6) grape, (7) apple, and (8) tobacco. Plants of each species were placed beneath a polycarbonate structure that excluded 98% of UV radiation, but maintained ambient environmental conditions and transmitted 96% PAR (UV– treatment). Plants of each species were maintained nearby under ambient conditions (i.e., without a UV filter; UV+ treatment). The maximum quantum efficiency (Fv/Fm) was measured with and without UV radiation at the time of measurement. Photosynthesis (A) and quantum efficiency (ΦII) were measured in the same experimental system using only apple, citrus, banana, and coffee. Only banana and coffee did not fully recover their Fv/Fm potential when they had not been previously exposed to UV, indicating that these species probably had not induced background repair mechanisms. The ΔFv/Fm is small in magnitude for banana and coffee due to UV treatment but when A is measured, the treatment impact has greater magnitude and banana and coffee A do not recover and coffee ΦII continues to decline while banana ΦII appears to stabilize. Banana and coffee have the potential to benefit from cultural practices that reduce the UV irradiance to the leaf or canopy surface.

Evaluation of Five Methods to Measure Normalized Difference Vegetation Index (NDVI) in Apple and Citrus

ABSTRACT Normalized Difference Vegetation Index (NDVI) is a common remote sensing calculation used to assess green biomass in addition to nutrient, pest, and water stress. The approach used in NDVI has potential for a broader use of remote sensing of plant physiological status. The purpose of the study was to evaluate techniques to that utilize combined near-infrared (NIR) and visible detection by low cost web cameras in comparison with a dedicated NDVI sensor and direct measurement of red-green-blue (RGB) and NIR bands to calculate NDVI and identify water stress. Established techniques as well as new techniques for NDVI computation from cameras were evaluated and compared to soil plant analysis development (SPAD) chlorophyll meter values to assess the accuracy of NDVI for predicting plant stress in apple and citrus. NDVI is a useful tool when evaluating long-term crop changes such as pest damage, chronic water shortage, and nutrient deficiencies that affect chlorophyll but NDVI is not useful for acute stresses such as an irrigation pump failure or plugged irrigation lines that have an effect within days.