CLASSIFYING SOIL MOISTURE CONTENT USING REFLECTANCE-BASED REMOTE SENSING

The ability to quantify soil moisture spatial variability and its temporal dynamics over entire fields through direct soil observations using remote sensing will improve early detection of water stress before crop physiological or economic damage has occurred, and it will contribute to the identification of zones within a field in which soil water is depleted faster than in other zones of a field. The overarching objective of this research is to develop tools and methods for remotely estimating soil moisture variability in agricultural crop production. Index-based and machine learning methods were deployed for processing hyperspectral data collected from moisture-controlled samples. In the first of five studies described in this dissertation, the feasibility of using “low-cost” index-based multispectral reflectance sensing for remotely delineating soil moisture content from direct soil and crop residue measurements using down-sampled spectral data were determined. The relative reflectance from soil and wheat stalk residue were measured using visible and near-infrared spectrometers. The optimal pair of wavelengths was chosen using a script to create an index for estimating soil and wheat stalk residue moisture levels. Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples; however, this index worked poorly on estimating stalk residue moisture. In the second of five studies, 20 machine learning algorithms were applied to full spectral datasets for moisture prediction and comparing them to the index-based method from the previous objective. Cubic support vector machine (SVM) and ensemble bagged trees methods produced the highest composite prediction accuracies of 96% and 93% for silt-loam soil samples, and 86% and 93% for wheat stalk residue samples, respectively. Prediction accuracy using the index-based method was 86% for silt-loam soil and 30% for wheat stalk residue. In the third study, a spectral measurement platform capable of being deployed on a UAS was developed for future use in quantifying and delineating moisture zones within agricultural landscapes. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous reflectance data acquisition. A similar ground-based system was developed to keep track of ambient light during reflectance target measurement. The systems were tested under varying ambient light conditions during the 2017 Great American Eclipse. In the fourth study, the data acquisition system from the third study was deployed for recognizing different targets in the grayscale range using machine learning methods and under ambient light conditions. In this study, a dynamic method was applied to update integration time on spectrometers to optimize sensitivity of the instruments. It was found that by adjusting the integration time on each spectrometer such that a maximum intensity across all wavelengths was reached, the targets could be recognized simply based on the reflectance measurements with no need of a separate ambient light measurement. Finally, in the fifth study, the same data acquisition system and variable integration time method were used for estimating soil moisture under ambient light condition. Among 22 machine learning algorithms, linear and quadratic discriminant analysis achieved the maximum prediction accuracy. A UAS-deployable hyperspectral data acquisition system containing three portable spectrometers and an embedded computer was developed to classify moisture content from spectral data. Partial least squares regression and machine learning algorithms were shown to be effective to generate predictive models for classifying soil moisture

A Low-Cost Method for Collecting Hyperspectral Measurements from a Small Unmanned Aircraft System

Small unmanned aircraft systems (UAS) are a relatively new tool for collecting remote sensing data at dense spatial and temporal resolutions. This study aimed to develop a spectral measurement platform for deployment on a UAS for quantifying and delineating moisture zones within an agricultural landscape. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous data acquisition. A second set of identical spectrometers were fitted with calibrated irradiance lenses to capture ambient light during data acquisition. Data were collected during the 2017 Great American Eclipse while observing a reflectance target to determine the ability to compensate for ambient light conditions. A calibration routine was developed that scaled raw reflectance data by sensor integration time and ambient light energy. The resulting calibrated reflectance exhibited a consistent spectral profile and average intensity across a wide range of ambient light conditions. Results indicated the potential for mitigating the effect of ambient light when passively measuring reflectance on a portable spectral measurement system. Future work will use multiple reflectance targets to test the ability to classify targets based on spectral signatures under a wide range of ambient light conditions

A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples

Reflectance indices are a method for reducing the dimensionality of spectral measurements used to quantify material properties. Choosing the optimal wavelengths for developing an index based on a given material and property of interest is made difficult by the large number of wavelengths typically available to choose from and the lack of homogeneity when remotely sensing agricultural materials. This study aimed to determine the feasibility of using a low-cost method for sensing the moisture content of background materials in traditional crop remote sensing. Moisture-controlled soil and wheat stalk residue samples were measured at varying heights using a reflectance probe connected to visible and near-infrared spectrometers. A program was written that used reflectance data to determine the optimal pair of narrowband wavelengths to calculate a normalized difference water index (NDWI). Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples. Probe height above samples and moisture content were examined for statistical significance using the selected wavelengths. The effect of moisture was significant for both bare soil and wheat stalks, but probe height was only significant for wheat stalk samples. The index, when applied to all samples, performed well for soil samples but poorly for wheat stalk samples. Index calculations from soil reflectance measurements were highly linear (R2 \u3e 0.95) and exhibited small variability between samples at a given moisture content, regardless of probe height. Index calculations from wheat stalk reflectance measurements were highly variable, which limited the usefulness of the index for this material. Based on these results, it is expected that crop residues, such as wheat stalks, will reduce the accuracy of remotely sensed soil surface moisture measurements

Classifying Reflectance Targets under Ambient Light Conditions using Passive Spectral Measurements

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables