AgriEngineering, Volume 2, Issue 2 (June 2020) – 13 articles

In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue; 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m × 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r² = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r² = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r² = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management. Full article

This paper presents a design for a feedstock logistics system to supply a bioenergy plant located in the Southeast USA, specifically Piedmont, a physiographic region covering part of five states (VA, NC, SC, GA, and AL). The design uses a perennial grass (switchgrass) as the feedstock. Harvest is done with a round baler, and round bales are stored in single-layer ambient storage in satellite storage locations. New technology, 20-bale racks, was designed as the multi-bale handling unit. The analysis shows how proper design of the interactions between the several unit operations in a “logistics chain” can be used to minimize average delivered cost for the feedstock required for 24/7 operation. Racks are loaded at the satellite storage and delivered by hauling contractors hired by the plant and controlled by a “Feedstock Manager” at the plant to insure approximately the same number of loads are received each day. Single-bale handling at the plant is eliminated, thus the truck unload time is reduced and truck productivity (tons/day) is increased. At-plant handling and storage in 20-bale racks increases plant receiving facility productivity, and gives a reduction in plant cost to supply a continuous steam of material for 24/7 operation. Full article

Mechanical harvesting of wild blueberries remains the most cost-effective means for harvesting the crop. Harvesting of wild blueberries is heavily reliant on operator skill and full automation of the harvester will rely on precise and accurate determination of the picking reel’s height. This study looked at developing a control system which would provide feedback on harvester picking reel height on up to five harvester heads. Additionally, the control system looked at implementing three quality of life improvements for operators, operating multiple heads until the point when full automation is achieved. These three functions were a tandem movement function, a baseline function, and a set-to-one function. Each of these functions were evaluated for their precision and accuracy and returned absolute mean discrepancies of 3.10, 2.20, and 2.50 mm respectively. Both electric and hydraulic actuators were evaluated for their effectiveness in this system however, the electric actuator was simply too slow to be deemed viable for the commercial harvesters. To achieve the full 203.2 mm stroke required by the harvester head, the electric actuator required 13.96 s while the hydraulic actuator required only 2.30 s under the same load. Full article

The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of interest when placed against a background of similar color. They do this by mimicking the human visual detection system, focusing on differences in texture rather than color as the primary detection paradigm. The key to leveraging the CNNs is the development of extensive image datasets required for training. One of the impediments to this methodology is the need for large image datasets where each image must be annotated with bounding boxes that surround each object of interest. As this requirement is labor-intensive, there is significant value in these image datasets. This report details the included image dataset as well as the system design used to collect the images. For acquisition of the image dataset, a prototype detection system was developed and deployed into a commercial cotton gin where images were collected for the duration of the 2018–2019 ginning season. A discussion of the observational impact that the system had on reduction of plastic contamination at the commercial gin, utilizing traditional color-based machine learning algorithms, is also included. Full article

Ultrasound-assisted extraction was used to extract propolis from a dark and resinous substance harvested from a beehive of Heterotrigona itama, which is commonly known as stingless bees. The propolis extracts were prepared using ethanol and water at different pH values of 3, 6, and 9. The yield of the ethanolic extract was significantly higher than the water extract, but there were no significant differences at different pH values. The ethanolic extract was found to have a lower 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity than the water extract at pH values of 6 and 9. However, the acidic propolis extracts, particularly the ethanolic extract, were found to have the highest antioxidant capacity. The addition of 20% polyethylene glycol 400 in the solvent systems was unlikely to improve propolis extraction. This can be seen from the antioxidant capacity and metabolite profile of the propolis extracts. Gas chromatography–mass spectrometry (GC–MS)-based high throughput screening of the propolis extracts showed them to have small metabolites of hydrocarbons, esters, terpenes, and alkaloids, as well as high antioxidative 2,4-di-tert-butylphenol. The detection of mangostin, mangiferin, and a few flavanones in the acidic ethanolic extract by liquid chromatography tandem mass spectrometry LC–MS/MS proved its high antioxidant capacity compared to the water extract. Full article

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection. Full article

The removal of plastic contamination in cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales, at the U.S. Department of Agriculture’s classing office, is plastic from the module wrap used to wrap cotton modules produced by the new John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during unwrapping of the seed cotton modules, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin; an inspection system was developed that utilized low-cost color cameras to see plastic on the module feeder’s dispersing cylinders, that are normally hidden from view by the incoming feed of cotton modules. This technical note presents the design of an automated intelligent machine-vision guided cotton module-feeder inspection system. The system includes a machine-learning program that automatically detects plastic contamination in order to alert the cotton gin personnel as to the presence of plastic contamination on the module feeder’s dispersing cylinders. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. This note describes the over-all system and mechanical design and provides an over-view and coverage of key relevant issues. Included as an attachment to this technical note are all the mechanical engineering design files as well as the bill-of-materials part source list. A discussion of the observational impact the system had on reduction of plastic contamination is also addressed. Full article

This research work studies the integrated application of satellite Remote Sensing (RS) and Geographic Information System (GIS) for the monitoring and forecasting of rice crop (Aman) production in Bangladesh. Normalized Difference Vegetation Index (NDVI) images of Terra MODIS products MOD13A1 (h25v06 and h26v06) with 500 m spatial resolution, composed using Maximum Value Composite (MVC) techniques, were used to cover Bangladesh for the period of 2011–2017. Country scale NDVI (district-wise summation) was calculated pixel-by-pixel to draw a regression curve while using Bangladesh Bureau of Statistics (BBS) estimations of Aman production for the months of September–November. The regression study of district-wise pixel-based summation of MODIS-NDVI and ground-based BBS-estimated Aman production shows a strong correlation (R² = 0.54–0.78); for the months of September and October, most of the regression coefficient indicates significant correlation due to maximum photosynthetic activities. Therefore, based on the highest regression coefficient value of September and October, Aman Crop Production (ACP) models were developed and the ACP Model-2 was exploited (from the derived set of coefficient values) to acquire year-wise rice production for all the years (2011–2017). The simulated ACP Model-2 demonstrates good agreement between the estimated and predicted yearly Aman rice production for the 2011–2017 time period with Mean Bias Error (MBE) = (−9435 to 23,156) M.Ton; Root Mean Square Error (RMSE) = 253–4426 M.Ton; Model Efficiency (ME) = (0.89–0.93); and, Correlation Coefficients = (0.72–0.94). Hence, the MODIS–NDVI-based regression model seems to be effective for Aman crop production forecasting in the context of food security issues in Bangladesh. The applied system is simple, rationally accurate, and fit for the generation of nationwide crop statistics. Full article

The current study investigated the effects of thermal processing of the microwave technology on nutritive value, crude protein solubility, urease activity and amino acid profile on linseed grains. Samples were treated in a SAMSUNG GE82N-B microwave oven at 450W for 0 (L1), 60 (L2), 180 (L3), 300 (L4), and 420 (L5) seconds, respectively. Microwave treatment for 300 seconds showed a significant (p ≤ 0.05) decrease in activity urease comparing to raw linseed. The raw and treated linseed protein solubility index (PDI) show statistical differences (p ≤ 0.05) between all the treatments compared. High performance liquid chromatography (HPLC) analyses of samples differences in the amino acid composition between controls and experimental treatments showed that amino acids were not significantly affected (p ≥ 0.05), except isoleucine and leucine amino acid (p ≤ 0.05). From the results of the present study, it is possible to identify that the best method for improving linseed quality for animal feed is the application of microwave for 60 second (treatment L2). Our results indicate that microwave thermal processing or micronizing dry thermal processing of grains could be successfully used in large industrial feed production with a short period of time and the improved nutritional parameters of grains, increased shelf-life and the unchanged amino acid profile of treated grains. Full article

The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work presents the results of a long-term study to calibrate a Grassmaster II capacitance probe to estimate pasture productivity in two phases: (i) the calibration phase (2007–2018), which included measurements in 1411 sampling points in three parcels; and (ii) the validation phase (2019), which included measurements in 216 sampling points in eight parcels. A regression analysis was performed between the capacitance (CMR) measured by the probe and values of pasture green matter and dry matter (respectively, GM and DM, in kg ha⁻¹). The results showed significant correlations between GM and CMR and between DM and CMR, especially in the early stages of pasture growth cycle. The analysis of the data grouped by classes of pasture moisture content (PMC) shows higher correlation coefficients for PMC content >80% (r = 0.775; p < 0.01; RMSE = 4806 kg ha⁻¹ and CV_RMSE = 28.1% for GM; r = 0.750; p < 0.01; RMSE = 763 kg ha⁻¹ and CV_RMSE = 29.7% for DM), with a clear tendency for the accuracy to decrease when the pasture vegetative cycle advances and, consequently, the PMC decreases. The validation of calibration equations when PMC > 80% showed a good approximation between GM or DM measured and GM or DM predicted (r = 0.959; p < 0.01; RMSE = 3191 kg ha⁻¹; CV_RMSE = 23.6% for GM; r = 0.953; p <0.01; RMSE = 647 kg ha⁻¹ and CV_RMSE = 27.3% for DM). It can be concluded that (i) the capacitance probe is an expedient tool that can enable the farm manager to estimate pasture productivity with acceptable accuracy and support the decision-making process in the management of dryland pastures; (ii) the more favorable period for the use of this probe in dryland pastures in a Mediterranean climate, such as the Portuguese Alentejo, coincides with the end of winter and beginning of spring (February–March), corresponding to PMC > 80%. Full article

Olive leaves (OLL) are considered to be a highly appreciated bioresource of bioactive polyphenolic phytochemicals, embracing several different structures. However, extraction processes based on deep eutectic solvents (DES) are very limited despite the wide range of techniques developed for the efficient recovery of polyphenols. This study had as objective the development of a simple, green, high-performance extraction methodology for OLL polyphenols, using a recently reported effective DES, composed of L-lactic acid and glycine. Initially, a screening was performed to select the most appropriate L-lactic/glycine molar ratio and process optimization was then carried out with response surface methodology. The optimized process variable values were DES/water (78% w/v), liquid-to-solid ratio of 36 mL g⁻¹, and stirring speed of 500 rounds per minute, and the total polyphenol yield amounted to 97.53 ± 3.54 mg gallic acid equivalents per g dry matter. Extraction with DES at 80 °C did not significantly increase the total polyphenol yield, but it did enhance the total flavonoid yield and antioxidant activity. High-performance liquid chromatography analyses revealed that extraction with the DES resulted in extended oleuropein hydrolysis, to the favor of hydroxytyrosol formation. This finding might have a prospect in using properly tuned DES for polyphenol modification with improved bioactivities. Full article

In Namibia, the encroachment by a native, but invasive bush on Savannah land leads to both environmental and economic loss. This invasive bush is, however, suitable for harvesting as a source of biomass for local industry. Harvesting biomass from the invasive bush has been shown to restore biodiversity, improve water conservation efforts, and restore grazing lands. Beyond the environmental benefits of removing the invasive bush, the raw biomass harvested is amenable to simple value-added production. Although some efforts are underway to make use of harvested biomass, current harvesting practices are not selective enough to meet governmental requirements intended to protect several species of local fauna and flora. Limitations, such as a lack of knowledge, during the harvesting process, can be overcome to a significant degree through the introduction of a smart application. This can integrate geographical context with computer vision to provide a ground-level tool for the identification of areas suitable for harvesting. This study shows that this tool can identify indigenous taxonomies with an accuracy of 76%. Full article

Unmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps. Full article