FTIR transmission spectroscopy for measurement of algae neutral lipids

Algae lipids can be used to produce biofuels and are considered a potential source of energy to supplant fossil fuel. Cultivation practices of algae grown in large ponds can be tailored to maximize lipid content. Laboratory methods of measuring lipid content are time-consuming and labor-intensive, so a real-time measuring technique is needed to efficiently control the addition of pond nutrients.  The objective of this research was to determine the effectiveness of measuring algae lipid content with Fourier Transform Infrared (FTIR) transmission spectroscopy. Six algae samples (Nannochloropsis salina) with varying lipid contents were centrifuged and then dried in an oven at 40° C for 12 hours. Dried algae were mixed with potassium bromide (KBr) powder at a mass ratio of 1:150 (algae:KBr) and pressed into pellets. A Thermo-Nicolet 6700 FTIR spectrometer was used to collect spectral data in transmission mode. Three relevant absorption bands centered at 2920, 2855, and 1742 cm-1 were identified. A linear regression analyses showed that the band depth at 2920 cm-1 was strongly correlated (R2 = 0.92) with lipid content measured by gas chromatography (GC).  The results of this research provide insight into the development of a real-time lipid-content sensor.

Differential effects of 24-hydroxycholesterol and 27-hydroxycholesterol on β-amyloid precursor protein levels and processing in human neuroblastoma SH-SY5Y cells

<p>Abstract</p> <p>Background</p> <p>Activation of the liver × receptors (LXRs) by exogenous ligands stimulates the degradation of β-amyloid 1–42 (Aβ42), a peptide that plays a central role in the pathogenesis of Alzheimer's disease (AD). The oxidized cholesterol products (oxysterols), 24-hydroxycholesterol (24-OHC) and 27-hydroxycholesterol (27-OHC), are endogenous activators of LXRs. However, the mechanisms by which these oxysterols may modulate Aβ42 levels are not well known.</p> <p>Results</p> <p>We determined the effect of 24-OHC and/or 27-OHC on Aβ generation in SH-SY5Y cells. We found that while 27-OHC increases levels of Aβ42, 24-OHC did not affect levels of this peptide. Increased Aβ42 levels with 27-OHC are associated with increased levels of β-amyloid precursor protein (APP) as well as β-secretase (BACE1), the enzyme that cleaves APP to yield Aβ. Unchanged Aβ42 levels with 24-OHC are associated with increased levels of sAPPα, suggesting that 24-OHC favors the processing of APP to the non-amyloidogenic pathway. Interestingly, 24-OHC, but not 27-OHC, increases levels of the ATP-binding cassette transporters, ABCA1 and ABCG1, which regulate cholesterol transport within and between cells.</p> <p>Conclusion</p> <p>These results suggest that cholesterol metabolites are linked to Aβ42 production. 24-OHC may favor the non-amyloidogenic pathway and 27-OHC may enhance production of Aβ42 by upregulating APP and BACE1. Regulation of 24-OHC: 27-OHC ratio could be an important strategy in controlling Aβ42 levels in AD.</p

GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated that, of the 31 counties in southern Texas that may have habitat for volunteer cotton, Hidalgo, Cameron, Nueces, and San Patricio are the counties at the greatest risk. Moreover, a method based on unmanned aerial vehicle (UAV) remote sensing was proposed to detect the precise locations of volunteer cotton plants in potential areas for their subsequent removal. In this study, a UAV was used to scan limited samples of potential volunteer cotton growth areas identified with the GIS model. The results indicated that UAV remote sensing coupled with the proposed image analysis methods could accurately identify the precise locations of volunteer cotton and could potentially assist in the elimination of volunteer cotton along transport routes

Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.Comment: 38 Page

Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications

To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields. This leads to many VC plants growing in the middle of fields remain undetected that continue to grow side by side along with corn and sorghum. When they reach pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll weevil pests. Therefore, it is required to detect, locate and then precisely spot-spray them with chemicals. In this paper, we present the application of YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel) multispectral imagery for detecting and locating VC plants growing in the middle of tasseling (VT) growth stage of cornfield. Our results show that VC plants can be detected with a mean average precision (mAP) of 79% and classification accuracy of 78% on images of size 1207 x 923 pixels at an average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the application of a customized unmanned aircraft systems (UAS) for spot-spray applications based on the developed computer vision (CV) algorithm and how it can be used for near real-time detection and mitigation of VC plants growing in corn fields for efficient management of the boll weevil pests.Comment: 39 page

Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges

Crop and animal production techniques have changed significantly over the last century. In the early 1900s, animal power was replaced by tractor power that resulted in tremendous improvements in field productivity, which subsequently laid foundation for mechanized agriculture. While precision agriculture has enabled site-specific management of crop inputs for improved yields and quality, precision livestock farming has boosted efficiencies in animal and dairy industries. By 2020, highly automated systems are employed in crop and animal agriculture to increase input efficiency and agricultural output with reduced adverse impact on the environment. Ground and aerial robots combined with artificial intelligence (AI) techniques have potential to tackle the rising food, fiber, and fuel demands of the rapidly growing population that is slated to be around 10 billion by the year 2050. This Issue Paper presents opportunities provided by ground and aerial robots for improved crop and animal production, and the challenges that could potentially limit their progress and adoption. A summary of enabling factors that could drive the deployment and adoption of robots in agriculture is also presented along with some insights into the training needs of the workforce who will be involved in the next-generation agriculture

Report from the conference, ‘identifying obstacles to applying big data in agriculture’

Data-centric technology has not undergone widespread adoption in production agriculture but could address global needs for food security and farm profitability. Participants in the U.S. Department of Agriculture (USDA) National Institute for Food and Agriculture (NIFA) funded conference, “Identifying Obstacles to Applying Big Data in Agriculture,” held in Houston, TX, in August 2018, defined detailed scenarios in which on-farm decisions could benefit from the application of Big Data. The participants came from multiple academic fields, agricultural industries and government organizations and, in addition to defining the scenarios, they identified obstacles to implementing Big Data in these scenarios as well as potential solutions. This communication is a report on the conference and its outcomes. Two scenarios are included to represent the overall key findings in commonly identified obstacles and solutions: “In-season yield prediction for real-time decision-making”, and “Sow lameness.” Common obstacles identified at the conference included error in the data, inaccessibility of the data, unusability of the data, incompatibility of data generation and processing systems, the inconvenience of handling the data, the lack of a clear return on investment (ROI) and unclear ownership. Less common but valuable solutions to common obstacles are also noted

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

Review of the cultivation program within the National Alliance for Advanced Biofuels and Bioproducts

The cultivation efforts within the National Alliance for Advanced Biofuels and Bioproducts (NAABB)were developed to provide four major goals for the consortium, which included biomass production for downstream experimentation, development of new assessment tools for cultivation, development of new cultivation reactor technologies, and development of methods for robust cultivation. The NAABB consortium test beds produced over 1500 kg of biomass for downstream processing. The biomass production included a number of model production strains, but also took into production some of the more promising strains found through the prospecting efforts of the consortium. Cultivation efforts at large scale are intensive and costly, therefore the consortium developed tools and models to assess the productivity of strains under various environmental conditions, at lab scale, and validated these against scaled outdoor production systems. Two new pond-based bioreactor designs were tested for their ability to minimize energy consumption while maintaining, and even exceeding, the productivity of algae cultivation compared to traditional systems. Also, molecular markers were developed for quality control and to facilitate detection of bacterial communities associated with cultivated algal species, including the Chlorella spp. pathogen, Vampirovibrio chlorellavorus,which was identified in at least two test site locations in Arizona and New Mexico. Finally, the consortium worked on understanding methods to utilize compromised municipal waste water streams for cultivation. This review provides an overview of the cultivation methods and tools developed by the NAABB consortium to produce algae biomass, in robust low energy systems, for biofuel production