Characterization of Physical and Biochemical Traits in Wheat and Corn Plants Using High Throughput Image Analysis

Plant phenotyping has been recognized as a rapidly growing field of research due to the labor-intensive, destructive, and time-consuming nature of traditional phenotyping methods. These phenotyping bottlenecks can be addressed by advancements in image-based phenotyping like RGB and hyperspectral imaging for the assessment of plant traits important for breeding purposes. This study aims (1) to characterize the physical and biochemical traits of wheat and corn plants using RGB and hyperspectral imaging in the greenhouse, and (2) to estimate leaf nitrogen (N), phosphorus (P), and potassium (K) content using hyperspectral imaging and an analytical spectral device (ASD spectrometer) and compare the performance from both datasets. Sixty wheat plants with 24 genotypes and 72 corn plants (a single genotype) with four different treatment combinations were manually measured and imaging was performed at different growth stages. RGB and hyperspectral images were processed to extract plant projected area (pixel count) and spectral reflectance, respectively. Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR) models were built to estimate N, P, and K contents from image-generated hyperspectral data, and from the ASD spectrometer. The results showed higher correlation for leaf area with plant pixel count with R2 of 0.75 for wheat and R2 of 0.68 for corn plants. For wheat plants, N was predicted more accurately with hyperspectral image datasets with R2 of 0.69 but P and K prediction was higher with ASD data using the PLSR model. For hyperspectral image datasets of corn plants, N prediction was higher using PLSR modeling with R2 0.66 whereas P and K prediction was higher using the RF model with R2 of 0.74 and 0.87 respectively. For corn plants using data from ASD, N, P, and K were predicted high by using the RF model with R2 of 0.67,0.41, and 0.69 respectively. RGB and hyperspectral imaging would reduce the need for manual measurement and chemical analysis of leaf tissue, and the technique can be validated in other crops with different architectures for high-throughput macronutrient estimation. The findings from this study can help integrate various disciplines of science, including plant breeding, agronomy, computer vision, mathematics, and engineering, for crop improvement. Advisor: Yufeng G

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments

Elucidating Sorghum Biomass, Nitrogen and Chlorophyll Contents With Spectral and Morphological Traits Derived From Unmanned Aircraft System

Unmanned aircraft systems (UAS) provide an efficient way to phenotype cropmorphology with spectral traits such as plant height, canopy cover and various vegetation indices (VIs) providing information to elucidate genotypic responses to the environment. In this study, we investigated the potential use of UAS-derived traits to elucidate biomass, nitrogen and chlorophyll content in sorghum under nitrogen stress treatments. A nitrogen stress trial located in Nebraska, USA, contained 24 different sorghum lines, 2 nitrogen treatments and 8 replications, for a total of 384 plots. Morphological and spectral traits including plant height, canopy cover and various VIs were derived from UAS flights with a true-color RGB camera and a 5-band multispectral camera at early, mid and late growth stages across the sorghum growing season in 2017. Simple and multiple regression models were investigated for sorghum biomass, nitrogen and chlorophyll content estimations using the derived morphological and spectral traits along with manual ground truthed measurements. Results showed that, the UAS-derived plant height was strongly correlated with manually measured plant height (r = 0.85); and the UAS-derived biomass using plant height, canopy cover and VIs had strong exponential correlations with the sampled biomass of fresh stalks and leaves (maximum r = 0.85) and the biomass of dry stalks and leaves (maximum r = 0.88). The UAS-derived VIs were moderately correlated with the laboratory measured leaf nitrogen content (r = 0.52) and the measured leaf chlorophyll content (r = 0.69) in each plot. The methods developed in this study will facilitate genetic improvement and agronomic studies that require assessment of stress responses in large-scale field trials

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

The Next-Gen Crop Nutrient Stress Identification with High-Precision Sensing Technology in Digital Agriculture

Crop yields are facing significant losses from nutrient deficiencies. Over-fertilizing also has negative economic and environmental impacts. It is challenging to optimize fertilizing without an accurate diagnosis. Recently, plant phenotyping has demonstrated outstanding capabilities in estimating crop traits. As one of the leading technologies, LeafSpec, provides high-quality crop image data for improving phenotyping quality. In this study, novel algorithms are developed for LeafSpec to identify crop nutrient deficiencies more accurately. Combined with UAV system, this technology will bring growers a robust solution for fertilizing diagnosis and scientific crop management

Estimating Crop Stomatal Conductance Through High-Throughput Plant Phenotyping

During photosynthesis and transpiration, crops exchange carbon dioxide and water with the atmosphere through stomata. When a crop experiences water stress, stomata are closed to reducing water loss. However, the closing of stomata also negatively affects the photosynthetic efficiency of the crop and leads to lower yields. Stomatal conductance (gs) quantifies the degree of stomatal opening and closing by using the rate of gas exchange between the crop and the atmosphere, which helps to understand the water status of the crop for better irrigation management. Unfortunately, gs measurement typically requires contact measuring instruments and manual collection in the field, which is time-consuming and labor-intensive. Thus, this study estimates gs in two ways. Firstly, plant phenotypic data and weather information were used to estimate gs for various types of crops. The plant phenotypic data were extracted from images captured by a thermal infrared camera, a multispectral camera, and a visible and near-infrared spectrometer integrated on field phenotyping platform. Weather information was obtained from a field weather station. The random forest regression (RFR) model performed the best with R2 of 0.69 and RMSE of 0.135 mol*m-2 *s-1 , while the model using weather parameters alone had R2 of 0.58 and RMSE of 0.161, and the model using phenotypic data alone had R2 values of 0.59 and RMSE of 0.158 mol*m-2 *s-1 . The results indicated that there was a complementary relationship between plant phenotypic data and weather information in estimating gs. The second aspect of the study was to estimate maize and soybean gs directly from near-infrared, thermal-infrared and RGB (Red Green Blue) images collected by the same platform. The results showed that the convolutional neural network (CNN) model outperformed the other models with an R2 of 0.52. In addition, adding soil moisture as a variable to the model improved its accuracy, which decreased the RMSE from 0.147 to 0.137 mol*m-2 *s-1 . This study highlights the potential of estimating gs from remote sensing and field phenotyping platforms to help growers obtain information about the water status of crops and plan irrigation more efficiently. Advisor: Yufeng G

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G