Elucidation of sex from mature Palmer amaranth (Amaranthus palmeri) leaves using a portable Raman spectrometer

Palmer amaranth (Amaranthus palmeri) is a pervasive and troublesome weed species that poses significant challenges to agriculture in the United States. Identifying the sex of Palmer amaranth plants is crucial for developing tailored control measures due to the distinct characteristics and reproductive strategies exhibited by male and female plants. Traditional methods for sex determination are expensive and time-consuming, but recent advancements in spectroscopic techniques offer new possibilities. This study explores the potential of portable Raman spectroscopy for determining the sex of mature Palmer amaranth plants in-field. Raman analysis of the plant leaves reveals spectral differences associated with nitrate salts, lipids, carotenoids, and terpenoids, allowing for high accuracy and reliable identification of the plant's sex; male plants had higher concentrations of these compounds compared to females. It was also found that male plants had higher concentrations of these compounds compared to the females. Raman spectra were analyzed using a machine learning tool, partial least squares discriminant analysis (PLS-DA), to generate accuracies of no less than 83.7% when elucidating sex from acquired spectra. These findings provide insights into the sex-specific characteristics of Palmer amaranth and suggest that Raman analysis, combined with PLS-DA, can be a promising, non-destructive, and efficient method for sex determination in field settings. This approach has implications for developing sex-specific management strategies to monitor and control this invasive weed in real-world environments, benefiting farmers, agronomists, researchers, and master gardeners.

Rate of crop‐weed hybridization in Sorghum bicolor × Sorghum halepense is influenced by genetic background, pollen load, and the environment

The potential for gene flow between cultivated species and their weedy relatives poses agronomic and environmental concerns, particularly when there are opportunities for the transfer of adaptive or agronomic traits such as herbicide resistance into the weedy forms. Grain sorghum (Sorghum bicolor) is an important crop capable of interspecific hybridization with its weedy relative johnsongrass (Sorghum halepense). Previous findings have shown that triploid progenies resulting from S. bicolor × S. halepense crosses typically collapse with only a few developing into mature seeds, whereas tetraploids often fully develop. The objective of this experiment was to determine the impact of S. bicolor genotype and pollen competition on the frequency of hybridization between S. bicolor and S. halepense. A total of 12 different cytoplasmic male sterile S. bicolor genotypes were compared with their respective male fertile lines across 2 years, to assess the frequency of hybridization and seed set when S. halepense served as the pollinator parent. Results indicate significant differences in the frequency of interspecific hybridization among the S. bicolor genotypes, and pollen fertility in S. bicolor reduced the rate of this interspecific hybridization by up to two orders of magnitude. Further, hybridization rates greatly varied across the two study environments. Results are helpful for developing appropriate gene flow mitigation strategies and indicate that gene flow could be reduced by the selection of appropriate seed parents for sorghum hybrids.

Nitrogen use efficiency-a key to enhance crop productivity under a changing climate

Nitrogen (N) is an essential element required for the growth and development of all plants. On a global scale, N is agriculture's most widely used fertilizer nutrient. Studies have shown that crops use only 50% of the applied N effectively, while the rest is lost through various pathways to the surrounding environment. Furthermore, lost N negatively impacts the farmer's return on investment and pollutes the water, soil, and air. Therefore, enhancing nitrogen use efficiency (NUE) is critical in crop improvement programs and agronomic management systems. The major processes responsible for low N use are the volatilization, surface runoff, leaching, and denitrification of N. Improving NUE through agronomic management practices and high-throughput technologies would reduce the need for intensive N application and minimize the negative impact of N on the environment. The harmonization of agronomic, genetic, and biotechnological tools will improve the efficiency of N assimilation in crops and align agricultural systems with global needs to protect environmental functions and resources. Therefore, this review summarizes the literature on nitrogen loss, factors affecting NUE, and agronomic and genetic approaches for improving NUE in various crops and proposes a pathway to bring together agronomic and environmental needs.

A deep learning-based method for classification, detection, and localization of weeds in turfgrass

BACKGROUND

Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate the weeds on the input images. The objectives of this research were to: (i) investigate the feasibility of training deep learning models using grid cells (subimages) to detect the location of weeds on the image by identifying whether or not the grid cells contain weeds; and (ii) evaluate DenseNet, EfficientNetV2, ResNet, RegNet and VGGNet to detect and discriminate multiple weed species growing in turfgrass (multi-classifier) and detect and discriminate weeds (regardless of weed species) and turfgrass (two-classifier).

RESULTS

The VGGNet multi-classifier exhibited an F₁ score of 0.950 when used to detect common dandelion and achieved high F₁ scores of ≥0.983 to detect and discriminate the subimages containing dallisgrass, purple nutsedge and white clover growing in bermudagrass turf. DenseNet, EfficientNetV2 and RegNet multi-classifiers exhibited high F₁ scores of ≥0.984 for detecting dallisgrass and purple nutsedge. Among the evaluated neural networks, EfficientNetV2 two-classifier exhibited the highest F₁ scores (≥0.981) for exclusively detecting and discriminating subimages containing weeds and turfgrass.

CONCLUSION

The proposed method can accurately identify the grid cells containing weeds and thus precisely locate the weeds on the input images. Overall, we conclude that the proposed method can be used in the machine vision subsystem of smart sprayers to locate weeds and make the decision for precision spraying herbicides onto the individual map cells. © 2022 Society of Chemical Industry.

Deep learning for detecting herbicide weed control spectrum in turfgrass

BACKGROUND

Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides.

RESULTS

GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F₁ scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated.

CONCLUSION

These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.

A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations

Nutrient regulation in aquaponic environments has been a topic of research for many years. Most studies have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been conducted on commercial-scale applications. In our model, the input data were sourced on a weekly basis from three commercial aquaponic farms in Southeast Texas over the course of a year. Due to the limited number of data points, dimensionality reduction techniques such as pairwise correlation matrix were used to remove the highly correlated predictors. Feature selection techniques such as the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors, and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentration to be maintained in the aquaponic solution to sustain healthy growth of tilapia fish and lettuce plants in a coupled set-up. To accomplish this, Vernier sensors were used to measure the nutrient values and actuator systems were built to dispense the appropriate nutrient into the ecosystem via a closed loop.

A novel deep learning-based method for detection of weeds in vegetables

BACKGROUND

Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep-learning-based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds.

RESULTS

The optimal confidence threshold values for YOLO-v3, CenterNet, and Faster R-CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep-learning models had average precision (AP) above 97% in the testing dataset. YOLO-v3 was the most accurate model for detection of vegetables and yielded the highest F 1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO-v3 was similar to CenterNet, but significantly shorter than that of Faster R-CNN. Overall, YOLO-v3 showed the highest accuracy and computational efficiency among the deep-learning architectures evaluated in this study.

CONCLUSION

These results demonstrate that deep-learning-based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site-specific robotic weed control in vegetable crops.

History of Herbicide-Resistant Traits in Cotton in the U.S. and the Importance of Integrated Weed Management for Technology Stewardship

This paper reviews the history of herbicide-resistant (HR) traits in U.S. cotton since the beginning, highlighting the shortcomings of each trait over time that has led to the development of their successor and emphasizing the importance of integrated weed management (IWM) going forward to ensure their long-term sustainability. Introduction of glyphosate-resistant cropping systems has allowed for expansion of no-till systems more reliant on herbicides, favored less diverse crop rotations, and heavily relied on a single herbicide mode of action (MOA). With repeated applications of glyphosate over the years, biotypes of glyphosate-resistant (GR) A. palmeri and other weeds became economically damaging pests in cotton production systems throughout the U.S. Moreover, the reported cases of weeds resistant to different MOA across various parts of the United States has increased. The dicamba- (XtendFlex^®) and 2,4-D-resistant (Enlist^®) cotton traits (with stacks of glyphosate and glufosinate resistance) were introduced and have been highly adopted in the U.S. to manage HR weeds. Given the current rate of novel herbicide MOA discovery and increase in new HR weed cases, the future of sustainable weed management relies on an integrated approach that includes non-herbicidal methods with herbicides to ensure long-term success.

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

BACKGROUND

In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat.

RESULTS

The object detection neural networks, including CenterNet, Faster R-CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels.

CONCLUSION

CenterNet, Faster R-CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

Seed Shattering: A Trait of Evolutionary Importance in Plants

Seed shattering refers to the natural shedding of seeds when they ripe, a phenomenon typically observed in wild and weedy plant species. The timing and extent of this phenomenon varies considerably among plant species. Seed shattering is primarily a genetically controlled trait; however, it is significantly influenced by environmental conditions, management practices and their interactions, especially in agro-ecosystems. This trait is undesirable in domesticated crops where consistent efforts have been made to minimize it through conventional and molecular breeding approaches. However, this evolutionary trait serves as an important fitness and survival mechanism for most weeds that utilize it to ensure efficient dispersal of their seeds, paving the way for persistent soil seedbank development and sustained future populations. Weeds have continuously evolved variations in seed shattering as an adaptation under changing management regimes. High seed retention is common in many cropping weeds where weed maturity coincides with crop harvest, facilitating seed dispersal through harvesting operations, though some weeds have notoriously high seed shattering before crop harvest. However, high seed retention in some of the most problematic agricultural weed species such as annual ryegrass (Lolium rigidum), wild radish (Raphanus raphanistrum), and weedy amaranths (Amaranthus spp.) provides an opportunity to implement innovative weed management approaches such as harvest weed seed control, which aims at capturing and destroying weed seeds retained at crop harvest. The integration of such management options with other practices is important to avoid the rapid evolution of high seed shattering in target weed species. Advances in genetics and molecular biology have shown promise for reducing seed shattering in important crops, which could be exploited for manipulating seed shattering in weed species. Future research should focus on developing a better understanding of various seed shattering mechanisms in plants in relation to changing climatic and management regimes.

Raman Spectroscopy Can Distinguish Glyphosate-Susceptible and -Resistant Palmer Amaranth (Amaranthus palmeri)

The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations. The glyphosate-resistant populations used in this study were 11-, 32-, and 36-fold more resistant compared to the susceptible standard. The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number for these resistant populations ranged from 86 to 116. We found that Raman spectroscopy could be used to differentiate herbicide-treated and non-treated susceptible populations based on changes in the intensity of vibrational bands at 1156, 1186, and 1525 cm-1 that originate from carotenoids. The partial least squares discriminant analysis (PLS-DA) model indicated that within 1 day of glyphosate treatment (D1), the average accuracy of detecting herbicide-treated and non-treated susceptible populations was 90 and 73.3%, respectively. We also found that glyphosate-resistant and -susceptible populations of Palmer amaranth can be easily detected with an accuracy of 84.7 and 71.9%, respectively, as early as D1. There were relative differences in the concentration of carotenoids in plants with different resistance levels, but these changes were not significant. The results of the study illustrate the utility of Raman spectra for evaluation of herbicide resistance and stress response in plants under field conditions.

Seed traits correlate with herbicide resistance in Italian ryegrass (Lolium perenne ssp. multiflorum)

BACKGROUND

Italian ryegrass (Lolium perenne ssp. multiflorum) is one of the major winter annual weeds worldwide. In this research, diversity for seed morpho-physiological traits such as seed weight, seed size, awnedness, dormancy, speed of germination, and seed vigor among Italian ryegrass populations collected from the Texas Blacklands region were assessed, and potential association with herbicide resistance was investigated.

RESULTS

A high degree of diversity was observed among the populations for 100-seed weight (125-256 mg), seed length (4.8-6.6 mm), awn length (0-6 mm), and total seedling length (9-14 cm at 21 days after seed germination). Inter-population range for seed dormancy was higher in the freshly harvested seed (31-85%), which reduced to 18 to 62% at 9 months after harvest. Populations with high initial seed dormancy (> 70% dormancy) released dormancy at a faster rate than the low dormancy group (< 40%). Percent survival status to multiple postemergence herbicides was positively correlated with 100-seed weight and fresh or initial seed dormancy.

CONCLUSION

Early emerging cohorts are easily controlled by pre-plant tillage and preemergence herbicides, whereas late emerging cohorts (facilitated by seed dormancy) are exposed to postemergence herbicides wherein greater opportunities exist for resistance evolution, likely explaining the occurrence of high seed dormancy in Italian ryegrass populations resistant to postemergence herbicides. High seed weights can further allow seedling emergence from greater burial depth, thereby exposing more seedlings to postemergence herbicides and increasing the likelihood of resistance evolution. Results provide unique insights into the association between seed traits and herbicide resistance in this species. © 2021 Society of Chemical Industry.

Cross and multiple herbicide resistance in annual bluegrass (Poa annua) populations from eastern Texas golf courses

BACKGROUND

Annual bluegrass is a troublesome weed in managed turf systems. A survey was conducted to evaluate the prevalence of herbicide resistance in golf course populations of annual bluegrass in eastern Texas. Screenings were conducted for two photosystem II (PS II)-inhibitor herbicides [simazine preemergence (PRE), amicarbazone postemergence (POST)], two acetolactate synthase (ALS) inhibitors (foramsulfuron POST, trifloxysulfuron POST) and one microtubule assembly inhibitor (pronamide PRE/POST).

RESULTS

Ninety percent of the populations were found to be resistant to at least one of the tested herbicides. The TX15-14 population was >490-, 178-, 10-, 26-, 4.3- and 3.8-fold, and the TX15-27 population was >490-, 16-, 28-, 84-, 5.2- and 4.1-fold less sensitive to simazine, amicarbazone, foramsulfuron, trifloxysulfuron, pronamide POST and pronamide PRE, respectively, compared to the susceptible standard TX15-SUS. Populations resistant to pronamide POST were completely controlled by pronamide PRE at the label recommended rate. The ALS and psbA gene sequence analysis indicated the presence of target site mutations (Ser-264-Gly in the psbA gene of TX15-14 and Trp-574-Leu in the ALS gene of TX15-27). However, given the absence of any target-site mutation in the ALS gene of TX15-14, the psbA gene of TX15-27 and α-tubulin of both populations, nontarget site mechanisms of resistance are suspected.

CONCLUSION

This is the first case of multiple herbicide resistance in annual bluegrass populations to three herbicide modes of action. Results show the widespread occurrence of multiple herbicide resistance in golf course annual bluegrass populations in eastern Texas and emphasize the need for the development and implementation of effective resistance management practices. © 2020 Society of Chemical Industry.

Current outlook and future research needs for harvest weed seed control in North American cropping systems

Harvest weed seed control (HWSC) comprises a set of tools and tactics that prevents the addition of weed seed to the soil seed bank, attenuating weed infestations and providing a method to combat the development and spread of herbicide-resistant weed populations. Initial HWSC research efforts in North America are summarized and, combined with the vast area of crops suitable for HWSC, clearly indicate strong potential for this technology. However, potential limitations exist that are not present in Australian cropping systems where HWSC was developed. These include rotations with crops that are not currently amenable to HWSC (e.g. corn), high moisture content at harvest, untimely harvest, and others. Concerns about weeds becoming resistant to HWSC (i.e. adapting) exist, as do shifts in weed species composition, particularly with the diversity of weeds in North America. Currently the potential of HWSC vastly outweighs any drawbacks, necessitating further research. Such expanded efforts should foremost include chaff lining and impact mill commercial scale evaluation, as this will address potential limitations as well as economics. Growers must be integrated into large-scale, on-farm research and development activities aimed at alleviating the problems of using HWSC systems in North America and drive greater adoption subsequently. © 2020 Society of Chemical Industry.

First case of glyphosate resistance in weedy sunflower (Helianthus annuus)

BACKGROUND

Weedy sunflower (Helianthus annuus L.) is a troublesome weed in row-crop production fields in South Texas. Populations with suspected resistance to glyphosate were evaluated with 1X and 4X rates (X = 868 g ae ha-1 ) of the herbicide, followed by a dose-response assay of the most resistant population. Molecular studies were conducted to determine if target-site mechanisms were responsible for resistance in these populations. Additionally, field experiments were conducted at two locations (Somerville and Granger, TX) to evaluate the effectiveness of different tank-mix combinations in controlling naturally infesting glyphosate-resistant (GR) weedy sunflower populations in GR corn.

RESULTS

In a study conducted in the growth chamber, seven of the 11 tested populations survived up to the 4X rate of glyphosate. The most-resistant population (TX15-11) was 29-fold more resistant to glyphosate, compared to the susceptible standard. In resistant populations, 5-21 more copies of the EPSPS gene were observed compared to the susceptible standard. In the field studies, tank-mix applications of glyphosate + halosulfuron-methyl, glyphosate + prosulfuron, glyphosate + a premix of halosulfuron-methyl and dicamba or glyphosate + a premix of diflufenzopyr and dicamba effectively controlled GR weedy sunflower populations.

CONCLUSION

Glyphosate-resistance was observed in 81% of the putative resistant weedy sunflower populations tested in this study. Resistance in these populations was conferred primarily by amplification of the EPSPS gene. Effective control of GR weedy sunflower can be achieved by tank-mixes tested in the current study, which provides acceptable levels of crop safety. © 2020 Society of Chemical Industry.

Late-season surveys to document seed rain potential of Palmer amaranth (Amaranthus palmeri) and waterhemp (Amaranthus tuberculatus) in Texas cotton

Weed escapes are often present in large production fields prior to harvest, contributing to seed rain and species persistence. Late-season surveys were conducted in cotton (Gossypium hirsutum L.) fields in Texas in 2016 and 2017 to identify common weed species present as escapes and estimate seed rain potential of Palmer amaranth (Amaranthus palmeri S. Watson) and waterhemp [A. tuberculatus (Moq.) J.D. Sauer], two troublesome species with high fecundity. A total of 400 cotton fields across four major cotton-producing regions in Texas [High Plains (HP), Gulf Coast (GC), Central Texas, and Blacklands] were surveyed. Amaranthus palmeri, Texas millet [Urochloa texana (Buckley) R. Webster], A. tuberculatus, ragweed parthenium (Parthenium hysterophorus L.), and barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] were reported as the top five weed escapes in cotton fields. Amaranthus palmeri was the most prevalent weed in the HP and Lower GC regions, whereas A. tuberculatus escapes were predominantly observed in the Upper GC and Blacklands regions. On average, 9.4% of an individual field was infested with A. palmeri escapes in the Lower GC region; however, 5.1 to 8.1% of a field was infested in the HP region. Average A. palmeri density ranged from 405 (Central Texas) to 3,543 plants ha^–1 (Lower GC). The greatest seed rain potential by A. palmeri escapes was observed in the upper HP region (13.9 million seeds ha^–1), whereas the seed rain potential of A. tuberculatus escapes was the greatest in the Blacklands (12.9 million seeds ha^–1) and the upper GC regions (9.8 million seeds ha^–1). Seed rain from late-season A. palmeri and A. tuberculatus escapes is significant in Texas cotton, and effective management of these escapes is imperative for minimizing seedbank inputs and impacting weed species persistence.

Correction: Surveying the spatial distribution of feral sorghum (Sorghum bicolor L.) and its sympatry with johnsongrass (S. halepense) in South Texas

[This corrects the article DOI: 10.1371/journal.pone.0195511.].

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (f_c) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April–October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and f_c were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, f_c and yield with R² values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and f_c were validated and proved to be accurate for estimating LAI and f_c from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (f_c, LAI and grain yield) suggests the applicability of UAS for within-season data collection of agricultural crops such as sorghum.

Transgenes and national boundaries - The need for international regulation

What happens when one nation cultivates a transgenic crop variety but neighboring nations do not? Using alfalfa as a case study, we argue that the potential for international transgene flow is substantial, and therefore, the need for international cooperation in regulatory decisions concerning transgenic crops is imperative. Alfalfa (Medicago sativa, L.) is the major forage crop in North America. Recently, genetically modified (GM) alfalfa received a moratorium on further cultivation in the US on the grounds that the approvals were based on inadequate environmental impact assessments. With their deep root system, symbiotic nitrogen fixation, prolific seed production and prolonged dormancy, alfalfa plants are capable of establishing self-perpetuating (feral) populations in unmanaged environments. Given what is known about alfalfa pollen dispersal, such feral populations could facilitate gene flow between GM and non-GM fields. The border between the US and Canada, particularly in farming areas, is very narrow (< 10 m wide). We surveyed along the US-Canada border and found both alfalfa fields and potentially feral alfalfa plants in the ditches along the border. Our survey results provide evidence of the possibility of cross-border transgene flow, suggesting a need for international co-operative risk assessment initiatives between the US and Canada. Such situations could occur for other crops, in other international border regions as well.