Yanbo Huang

Agronomic and environmental implications of enhanced s-triazine degradation.

Novel catabolic pathways enabling rapid detoxification of s-triazine herbicides have been elucidated and detected at a growing number of locations. The genes responsible for s-triazine mineralization, i.e. atzABCDEF and trzNDF, occur in at least four bacterial phyla and are implicated in the development of enhanced degradation in agricultural soils from all continents except Antarctica. Enhanced degradation occurs in at least nine crops and six crop rotation systems that rely on s-triazine herbicides for weed control, and, with the exception of acidic soil conditions and s-triazine application frequency, adaptation of the microbial population is independent of soil physiochemical properties and cultural management practices. From an agronomic perspective, residual weed control could be reduced tenfold in s-triazine-adapted relative to non-adapted soils. From an environmental standpoint, the off-site loss of total s-triazine residues could be overestimated 13-fold in adapted soils if altered persistence estimates and metabolic pathways are not reflected in fate and transport models. Empirical models requiring soil pH and s-triazine use history as input parameters predict atrazine persistence more accurately than historical estimates, thereby allowing practitioners to adjust weed control strategies and model input values when warranted.

Development of a Spray System for an Unmanned Aerial Vehicle Platform

Application of crop production and protection materials is a crucial component in the high productivity of American agriculture. Agricultural chemical application is frequently needed at specific times and locations for accurate site-specific management of crop pests. Piloted agricultural aircraft are typically used to treat large, unobstructed, continuous acreage crops and are not as efficient when working over small or obstructed plots. An Unmanned Aerial Vehicle (UAV), which can be remotely controlled or fly autonomously based on pre-programmed flight plans, may be used to make timely and efficient applications over these small area plots. This research developed a low volume spray system for use on a fully autonomous UAV to apply crop protection products on specified crop areas. This article discusses the development of the spray system and its integration with the flight control system of a fully autonomous, unmanned vertical take-off and landing helicopter. Sprayer actuation can be triggered by preset positional coordinates as monitored by the equipped Global Positioning System (GPS). The developed spray system has the potential to provide accurate, site-specific crop management when coupled with UAV systems. It also has great potential for vector control in the areas that are not easily accessible by personnel or equipment.

Biological responses to glyphosate drift from aerial application in non-glyphosate- resistant corn

BACKGROUND Glyphosate drift from aerial application onto susceptible crops is inevitable, yet the biological responses to glyphosate drift in crops are not well characterized. The objectives of this research were to determine the effects of glyphosate drift from a single aerial application (18.3 m swath, 866 g AE ha(-1)) on corn injury, chlorophyll content, shikimate level, plant height and shoot dry weight in non-glyphosate-resistant (non-GR) corn. RESULTS One week after application (WAA), corn was killed at 3 m from the edge of the spray swath, with injury decreasing to 18% at 35.4 m downwind. Chlorophyll content decreased from 78% at 6 m to 22% at 15.8 m, and it was unaffected beyond 25.6 m at 1 WAA. Shikimate accumulation in corn decreased from 349% at 0 m to 93% at 15.8 m, and shikimate levels were unaffected beyond 25.6 m downwind. Plant height and shoot dry weight decreased gradually with increasing distance. At a distance of 35.4 m, corn height was reduced by 14% and shoot dry weight by 10% at 3 WAA. CONCLUSIONS Corn injury and other biological responses point to the same conclusion, that is, injury from glyphosate aerial drift is highest at the edge of the spray swath and decreases gradually with distance. The LD(50) (the lethal distance that drift must travel to cause a 50% reduction in biological response) ranged from 12 to 26 m among the biological parameters when wind speed was 11.2 km h(-1) and using a complement of CP-09 spray nozzles on spray aircraft.

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Leaf area index (LAI) and evapotranspiration (ET) are two crucial biophysical variables related to crop growth and grain yield. This study presents a crop model-data assimilation framework to assimilate the 1-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET products (MCD15A3 and MOD16A2, respectively) into the soil water atmosphere plant (SWAP) model to assess the potential for estimating winter wheat yield at field and regional scales. Since the 1-km MODIS products generally underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we constructed a new cost function by comparing the generalized vector angle between the observed and modeled LAI and ET time series during the growing season. We selected three parameters (irrigation date, irrigation depth, and emergence date) as the reinitialized parameters to be optimized by minimizing the cost function using the shuffled complex evolution method-University of Arizona (SCE-UA) optimization algorithm, and then used the optimized parameters as inputs into the SWAP model for winter wheat yield estimation. We used four data-assimilation schemes to estimate winter wheat yield at field and regional scales. We found that jointly assimilating MODIS LAI and ET data improved accuracy (R2 = 0.43, RMSE = 619 kg · ha-1) than assimilating MODIS LAI data (R2 = 0.28, RMSE = 889 kg · ha-1) or ET data (R2 = 0.36, RMSE = 1561 kg·ha-1) at the county level, which indicates that the proposed estimation method is reliable and applicable at a county scale.

Development of an airborne remote sensing system for crop pest management: system integration and verification.

Remote sensing is being used with Global Positioning Systems, Geographic Information Systems, and variable rate technology to ultimately help farmers maximize the economic and environmental benefits of crop pest management through precision agriculture. Airborne remote sensing is flexible and versatile because fields can be flown at variable altitude depending on the spatial resolution required. Although the use of airborne hyperspectral remote sensing in agricultural research and applications has been steadily increasing in the last decade, the airborne multispectral technique is still a good source of crop, soil, or ground cover information. The MS-4100 is a multispectral camera that produces and aligns images from different bands with a built-in prism. Data can be analyzed from the composite image or individual band images. The camera system evaluated herein uses a camera control system to physically compensate for roll, pitch, and yaw and maintain the camera at vertical nadir orientation. This article describes the automated airborne multi-spectral imaging system and image processing using sample imagery to demonstrate the capability and potential of the system for crop pest management.

Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features.

Biological Response of Soybean and Cotton to Aerial Glyphosate Drift

When glyphosate is applied to glyphosate-resistant (GR) crops, drift on to off-target sensitive crops may cause injury and mortality. An aerial application drift study was conducted in 2009 to determine biological effects of glyphosate on non-glyphosate-resistant (non-GR) cotton (Gossypium hirsutum L.) and non-GR soybean [Glycine max (L.) Merr.]. Glyphosate at 866 g ae/ha was applied using an Air Tractor 402B agricultural aircraft in an 18.3 m spray swath to crops at the two- to three-leaf stage. Visual plant injury, chlorophyll, shikimate, plant height, and shoot dry weight were determined at one, two, and three weeks after application (WAA) of glyphosate. Biological responses differed between crops as a function of downwind drift distance. For example, at 3 WAA soybean was dead 6 m downwind from the spray swath, whereas cotton sustained 85% visual injury. Plant injury was not observed beyond 25.6 m downwind in soybean and 35.4 m downwind in cotton at 3 WAA. Chlorophyll reduction was higher (80%) in soybean compared with cotton (43%) at 0 m from the edge of the spray at 1 WAA. Shikimate levels 1 WAA decreased from 1518% at 0 m to 209% at 35.4 m downwind in soybean; at the same sampling time shikimate levels in cotton decreased from 464% at 0 m to 0% at 35.4 m. At 35.4 m downwind, shoot dry weight (5–13%) and plant height (6–8%) were reduced in both crops at 3 WAA. The biological response of soybean and cotton to glyphosate drift decreased with increased distance from the edge of spray swath. These biological data suggested that soybean was more susceptible to glyphosate drift than cotton and elevated shikimate level could be used as a sensitive indicator to confirm plant exposure to glyphosate drift.

Automation for Food Engineering : Food Quality Quantization and Process Control

Introduction Food Quality: A Primary Concern of Food Industry Automated Evaluation of Food Quality Food Quality Quantization and Process Control Typical Problems in Food Quality Evaluation and Process Control How to Learn the Technologies Data Acquisition Sampling Concepts and Systems for Data Acquisition Image Acquisition Data Analysis Data Pre-Processing Data Analysis Image Processing Modeling Modeling Strategy Linear Statistical Modeling ANN Modeling Prediction Prediction and Classification One-Step-Ahead Prediction Multiple-Step-Ahead Prediction Control Process Control Internal Model Control Predictive Control Systems Integration Food Quality Quantization Systems Integration Food Quality Process Control Systems Integration Food Quality Quantization and Process Control Systems Development Concluding Remarks

Development of a PWM Precision Spraying Controller for Unmanned Aerial Vehicles

This paper presents a new Pulse Width Modulation (PWM) controller for Unmanned Aerial Vehicle (UAV) precision sprayer for agriculture using a TL494 fixed-frequency pulse width modulator together with a data acquisition board and developed software. An UAV can be remotely controlled or flown autonomously by pre-programmed flight plans. The PWM controller was implemented through the guidance system on the UAV with control commands sent between the UAV helicopter and the ground control station via a wireless telemetry system. The PWM controller was tested and validated using LabVIEW 8.2. Several analyses were performed in a laboratory to test different control signals. The results show that the PWM controller has promise as a higher precision technique for spray applications, which will improve efficiency of pesticide application, especially in crop production areas.

Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification

BACKGROUND Palmer amaranth (Amaranthus palmeri S. Wats.) is a troublesome agronomic weed in the southern United States, and several populations have evolved resistance to glyphosate. This paper reports on spectral signatures of glyphosate-resistant (GR) and glyphosate-sensitive (GS) plants, and explores the potential of using hyperspectral sensors to distinguish GR from GS plants. RESULTS GS plants have higher light reflectance in the visible region and lower light reflectance in the infrared region of the spectrum compared with GR plants. The normalized reflectance spectrum of the GR and GS plants had best separability in the 400-500 nm, 650-690 nm, 730-740 nm and 800-900 nm spectral regions. Fourteen wavebands from within or near these four spectral regions provided a classification of unknown set of GR and GS plants, with a validation accuracy of 94% for greenhouse-grown plants and 96% for field-grown plants. CONCLUSIONS GR and GS Palmer amaranth plants have unique hyperspectral reflectance properties, and there are four distinct regions of the spectrum that can separate the GR from GS plants. These results demonstrate that hyperspectral imaging has potential application to distinguish GR from GS Palmer amaranth plants (without a glyphosate treatment), with future implications for glyphosate resistance management. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.