Glen Rains - Academia.edu (original) (raw)

Papers by Glen Rains

Frontiers in agronomy, May 16, 2024

Injury Prevention, Oct 1, 2012

Smart Agricultural Technology

Sensors

Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied... more Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, co...

Frontiers in Plant Science, 2021

Weeds are a persistent problem on sod farms, and herbicides to control different weed species are... more Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, grass weeds, spurge, sedge, and no weeds at a precision between 0.68 and 0.87, 0.57 and 0.82, 0.68 and 0.83, 0.66 and 0.90, and 0.80 and 0.88, respectively, when using UAS images at 0.57 cm–1.28 cm ...

Industrial Crops and Products, 2021

Abstract Growth temperature and genotype can influence seedling vigor in cotton (Gossypium hirsut... more Abstract Growth temperature and genotype can influence seedling vigor in cotton (Gossypium hirsutum L.), and identifying genotypes that can perform well under different temperature extremes may broaden the range of temperatures over which optimum growth could be obtained. To this end, a controlled environment study was conducted to evaluate the response of advanced breeding lines to growth temperature and to evaluate the utility of rapid, fluorescence-based measurements (the OJIP test) as indicators of plant growth response to day/night temperature regimes (20/15, 30/20, 35/25, and 40/30 °C). At two weeks after planting, growth analysis, chlorophyll fluorescence measurements, and pigment concentrations were obtained. Significant genotype, temperature, and interaction effects were observed for seedling growth parameters, and some of the key components of the thylakoid reactions. Specifically, the 35/25 °C treatment had the highest values for all growth parameters, with genotypic differences in growth primarily being observed at this regime. Energy trapping by photosystem II (PSII) (φPO), intersystem electron transport (φEO), and photosystem I end electron acceptor reduction (φRO) were significantly affected by temperature, but each component differed in heat sensitivity. All growth parameters were significantly correlated with a number of OJIP parameters including quantum efficiencies and performance indices. However, the strongest positive associations were observed between quantum efficiencies and growth metrics, with φEO exhibiting the strongest correlations with growth. Our observations indicate that rapid OJIP assessments can potentially be used as indicators of early season growth responses to temperature.

Peanut Science, 2021

Growers have rapidly adopted auxin-resistant cotton and soybean technologies. In Georgia, growers... more Growers have rapidly adopted auxin-resistant cotton and soybean technologies. In Georgia, growers who plant auxin-resistant cotton/soybean are required to utilize nozzles that produce larger (coarser) droplets when spraying auxin herbicides to minimize potential off-target movement of pesticides. Consequently, these nozzles are also used in peanut (an important rotational crop with cotton) since changing nozzles between crops is uncommon for growers. However, larger droplets can result in reduced spray coverage which may lead to less effective pest control. Therefore, seven on-farm trials were conducted in commercial peanut fields using commercial sprayers from 2018 to 2020 across four different locations in Georgia to compare the spray performance of air-induction (AI) nozzles that produce very coarse to ultra coarse droplets (VMD50 ≥ 404 microns) with non-AI (conventional flat fan) nozzles that produce medium to coarse droplets (403≥VMD50≥236 microns) for pest management in peanut...

Applied engineering in agriculture

A pure-pursuit navigational control algorithm in an autonomous articulated-steer vehicle was stud... more A pure-pursuit navigational control algorithm in an autonomous articulated-steer vehicle was studied as a simple method for path-following. Paths were created using GPS coordinates and the articulated-steer vehicle programmed to follow those paths using a pure-pursuit (goal-seeking) path-following algorithm. A GUI was developed in LabVIEW to read RTK-GPS coordinates, hydraulic pressure and articulation angle, and control the articulated-steer vehicle. The vehicle was tested for its ability to follow five different paths with two speeds and three control pulse signals. Path following succeeded with lower vehicle speed and full pulse signal for the straight and sinusoidal paths. Averaged path-following errors ranged from 6 to 19 cm as the turn radius increased. For a 90 degree turn, the path-following error was almost 80 cm. Path-following improvements could be made with faster control signal updates and a variable look-ahead distance based on vehicle speed and required turning angle....

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Encyclopedia of Pest Management (Print), 2002

Contemporary Topics in Entomology, 2015

2011 Louisville, Kentucky, August 7 - August 10, 2011, 2011

A key step to enhance the efficiency of the highbush blueberry production is to improve current m... more A key step to enhance the efficiency of the highbush blueberry production is to improve current mechanical harvesting technologies which create excessive bruising and make blueberries unmarketable to the fresh market. The overall goal of this study was to quantitatively measure the dynamic interactions between the blueberry and the mechanical harvester and to understand how bruising is created during the harvesting process. A custom-made miniature instrumented sphere, known as blueberry impact recording device (BIRD), was used to measure the mechanical impacts created by a rotary mechanical harvester (Korvan 8000, Oxbo International, Lynden, WA). A close-up, visual recording of the harvesting process was made with a hand-held digital camcorder to pinpoint critical control points that create most impacts on the harvester. Four contacting surfaces (the catch pan, conveyer belt, steel tunnel, and empty lug box) on the harvester were evaluated by the BIRD sensor with regard to the impact they created. The results showed that the catch pans of the rotary harvester accounted for over 30% of all mechanical impacts imposed on a blueberry, followed by the lug (>20%), conveyer belt (13%), and shaking rod (13%). However, the high number of impacts in the lug might be overestimated by measuring only empty lugs. Thus, the most significant reduction in bruising could be achieved through improvements of the catch pans, conveyor belts, and lugs. Harvester surface evaluation confirmed that the catch pan was the hardest surface in the mechanical harvester. This study has shed light on how to reduce blueberry bruising by improving current mechanical harvesters, which will be invaluable to enhance blueberry production efficiency in the long run.

2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010, 2010

... Ron Gitaitis, Professor University of Georgia, Tifton, Plant Pathology Department EW Tollner,... more ... Ron Gitaitis, Professor University of Georgia, Tifton, Plant Pathology Department EW Tollner, Professor ... 2002. Classification of sweet onions based on internal defectsusing image processing and neural network techniques. ...

The Journal of Rural Health, 2014

Journal of Agricultural Safety and Health, 2000

Journal of Adolescent Health, 2014

Journal of food science

The off-flavor boar taint associated with the substances skatole, androstenone, and possibly indo... more The off-flavor boar taint associated with the substances skatole, androstenone, and possibly indole represents a significant problem in the pig husbandry industry. Boar taint may occur in meat from uncastrated sexually mature male pigs; consumers commonly show a strong aversion to tainted meat. Consequently, there is a need for rapid methods to sort out and remove tainted carcasses at the slaughterline. We tested the ability of wasps, Microplitis croceipes to perceive and learn the 3 boar taint compounds both individually and in combination using classical conditioning paradigms. We also established the effectiveness and reliability of boar taint odor detection when wasps were used as biosensors in a contained system called the "wasp hound" using a cohort of trained wasps. We found that the wasps are able to successfully learn indole, skatole and to also detect them when presented a 1:1:1 mixture of all 3 compounds. This was shown for both a single hand-manipulated wasp bi...