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

Influence of Hybridizing Flax and Hemp-Agave Fibers with Glass Fiber as Reinforcement in a Polyurethane Composite

In this study, six combinations of flax, hemp, and glass fiber were investigated for a hybrid reinforcement system in a polyurethane (PU) composite. The natural fibers were combined with glass fibers in a PU composite in order to achieve a better mechanical reinforcement in the composite material. The effect of fiber hybridization in PU composites was evaluated through physical and mechanical properties such as water absorption (WA), specific gravity (SG), coefficient of linear thermal expansion (CLTE), flexural and compression properties, and hardness. The mechanical properties of hybridized samples showed mixed trends compared to the unhybridized samples, but hybridization with glass fiber reduced water absorption by 37% and 43% for flax and hemp-agave PU composites respectively

Effect of Laboratory Aging on the Physical and Mechanical Properties of Wood-Polymer Composites

The long-term performance of wood-polymer composites (WPC) under severe weather conditions is not well known. This study evaluates the changes in physical and mechanical properties of three commercially available WPC and treated southern yellow pine (SYP) under a modified 6-cycle accelerated aging process. The accelerated aging causes warping, splitting, discoloration, and significant changes in physical and mechanical properties of SYP. The compressive and flexural strength of the WPCs show negligible changes whereas stiffness, hardness, and screw withdrawal force show considerable deterioration and some recovery during accelerated aging. The composition and manufacturing process influence the performance of WPC under accelerated aging

Impact of Fiber Treatment on the Oil Absorption Characteristics of Plant Fibers

Most plant fibers are good sorbents of oil; however, synthetic sorbents have a much higher sorption capacity (SC) than plant fibers. This study evaluated the effect of fiber treatments, specifically hot-water treatment and mercerization, on the absorption characteristics of selected plant fibers. Five common plant fibers—corn residues, soybean residues, cotton burr and stem (CBS), cattail, and oak—were evaluated for their absorption characteristics in crude oil, motor oil, deionized (DO) water, and a 80:20 mix of DO water. The fiber treatments included ground fiber (control), hot-water treatment at 80 °C for 4 h and 125 °C for 4 h, mercerization at room temp for 48 h, and mercerization at 300 °C for 1 h. The absorption capacity (AC) varied with fiber type, absorption medium, and fiber treatment. Mercerization at 300 °C increased the water absorption of soybean residue up to 8 g/g. Mercerization at room temperature and the hot-water treatment at 125 °C increased the crude oil absorption capacity. After certain treatments, the crude oil absorption capacity of CBS and corn fibers increased over 5 g/g, and the motor oil absorption capacity of cattail, corn, and soybean also increased to 4 to 5 g/g

Role of Hybrid Nano-Zinc Oxide and Cellulose Nanocrystals on the Mechanical, Thermal, and Flammability Properties of Poly (Lactic Acid) Polymer

Biopolymers with universal accessibility and inherent biodegradability can offer an appealing sustainable platform to supersede petroleum-based polymers. In this research, a hybrid system derived from cellulose nanocrystals (CNCs) and zinc oxide (ZnO) nanoparticles was added into poly (lactic acid) (PLA) to improve its mechanical, thermal, and flame resistance properties. The ZnO-overlaid CNCs were prepared via the solvent casting method and added to PLA through the melt-blending extrusion process. The composite properties were evaluated using SEM, a dynamic mechanical analyzer (DMA), FTIR TGA, and horizontal burning tests. The results demonstrated that the incorporation of 1.5% nano-CNC-overlaid ZnO nanoparticles into PLA enhanced the mechanical and thermal characteristics and the flame resistance of the PLA matrix. Oxidative combustion of CNC-ZnO promoted char formation and flame reduction. The shielding effect from the ZnO-CNC blend served as an insulator and resulted in noncontinuous burning, which increased the fire retardancy of nanocomposites. By contrast, the addition of ZnO into PLA accelerated the polymer degradation at higher temperature and shifted the maximum degradation to lower temperature in comparison with pure PLA. For PLA composites reinforced by ZnO, the storage modulus decreased with ZnO content possibly due to the scissoring effect of ZnO in the PLA matrix, which resulted in lower molecular weight

Deterioration in the Physico-Mechanical and Thermal Properties of Biopolymers Due to Reprocessing

Biopolymers are an emerging class of materials being widely pursued due to their ability to degrade in short periods of time. Understanding and evaluating the recyclability of biopolymers is paramount for their sustainable and efficient use in a cost-effective manner. Recycling has proven to be an important solution, to control environmental and waste management issues. This paper presents the first recycling assessment of Solanyl, Bioflex, polylactic acid (PLA) and PHBV using a melt extrusion process. All biopolymers were subjected to five reprocessing cycles. The thermal and mechanical properties of the biopolymers were investigated by GPC, TGA, DSC, mechanical test, and DMA. The molecular weights of Bioflex and Solanyl showed no susceptible effect of the recycling process, however, a significant reduction was observed in the molecular weight of PLA and PHBV. The inherent thermo-mechanical degradation in PHBV and PLA resulted in 20% and 7% reduction in storage modulus, respectively while minimal reduction was observed in the storage modulus of Bioflex and Solanyl. As expected from the Florry-Fox equation, recycled PLA with a high reduction in molecular weight (78%) experienced 9% reduction in glass transition temperature. Bioflex and Solanyl showed 5% and 2% reduction in molecular weight and experienced only 2% reduction in glass transition temperature. These findings highlight the recyclability potential of Bioflex and Solanyl over PLA and PHBV

Soybean Disease Monitoring with Leaf Reflectance

Crop disease detection with remote sensing is a challenging area that can have significant economic and environmental impact on crop disease management. Spectroscopic remote sensing in the visible and near-infrared (NIR) region has the potential to detect crop changes due to diseases. Soybean cyst nematode (SCN) and sudden death syndrome (SDS) are two common soybean diseases that are extremely difficult to detect in the early stages under mild to moderate infestation levels. The objective of this research study was to relate leaf reflectance to disease conditions and to identify wavebands that best discriminated these crop diseases. A microplot experiment was conducted. Data collected included 800 leaf spectra, corresponding leaf chlorophyll content and disease rating of four soybean cultivars grown under different disease conditions. Disease conditions were created by introducing four disease treatments of control (no disease), SCN, SDS, and SCN+SDS. Crop data were collected on a weekly basis over a 10-week period, starting from 71 days after planting (DAP). The correlation between disease rating and selected vegetation indices (VI) were evaluated. Wavebands with the most disease discrimination capability were identified with stepwise linear discriminant analysis (LDA), logistic discriminant analysis (LgDA) and linear correlation analysis of pooled data. The identified band combinations were used to develop a classification function to identify plant disease condition. The best correlation (>0.8) between disease rating and VI occurred during 112 DAP. Both LDA and LgDA identified several bands in the NIR, red, green and blue regions as critical for disease discrimination. The discriminant models were able to detect over 80% of the healthy plants accurately under cross-validation but showed poor accuracy in discriminating individual diseases. A two-class discriminant model was able to identify 97% of the healthy plants and 58% of the infested plants as having some disease from the plant spectra

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms

Graphene quantum dots/cellulose nanocrystal inclusion complex for enhancing the physical and thermal properties of HDPE polymer matrix

Cellulose nanocrystals (CNC) are desirable material due to universal accessibility, and superior mechanical properties. A major challenge is non-uniform dispersion of CNC in the hydrophobic matrices due to their tendency to agglomerate. A novel technique was evaluated to prepare a hybrid system of cellulose nanocrystal (CNC)/graphene quantum dots (GQD). Hybrid system of CNC/GQD was added to high density poly (ethylene) (HDPE) to manufacture composites. The CNC/GQD inclusion complex properties were evaluated using Zeta potential measurement, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis. The composite properties were analysed using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and tensile testing and analysis of electrical impedance spectra. Raman spectroscopy, XPS and XRD confirmed the interaction of CNC and GQD. The SEM micrographs of the cross-sections of GQD induced composites showed a uniform honeycomb like morphology and no sign of agglomeration. GQD incorporated composites exhibited better thermal stability and higher elastic modulus than neat HDPE. The composites showed a purely capacitive response for an AC electrical system measured over 4 Hz to 1 MHz. The results indicate significantly improved dispersion of CNC in the polymer matrix, compared to unmodified CNCs

Inverse Modeling of Beaver Reservoir\u27s Water Spectral Reflectance

Estimation of inherent optical properties (IOP) needed for water quality evaluation by remote sensing models is very complex, primarily due to the large number of model simulations needed to find optimal parameter values. This study presents an approach for optimally parameterizing the IOP values of a physical hyperspectral optical - Monte Carlo (PHO-MC) model. An artificial neural network (ANN) based pseudo simulator combined with the Nondominated Sorted Genetic Algorithm II (NSGA II) was used to efficiently perform a large number of model simulations and to search the optimal parameter values for IOP determination. Concentrations of suspended matter (sm), chlorophyll-a (chl), and total dissolved organic matter (DOM) along with the reflectance data at 16 different wavelengths were measured at 48 sampling stations in the Beaver Reservoir, Arkansas, between 2003 and 2005 and were used to evaluate the IOP values. Measured concentrations and reflectance data from 24 sampling stations were used to optimize IOP parameter values for sm, chl, and DOM. The data collected from the remaining 24 sampling stations were used for the validation of PHO-MC model-predicted reflectance by using optimized IOP values. PHO-MC predicted reflectance values were significantly correlated (r = 0.90, p \u3c 0.01) with the corresponding measured reflectance values, indicating that the pseudo simulator combined with the NSGA II accurately estimated the IOP values. An estimated 10 10 years of calculation time was reduced to less than 3 min by using the pseudo simulator and NSGA II to supplement the PHO-MC model for estimating the IOP values