DEVELOPMENT AND EVALUATION OF A CONTROLLER AREA NETWORK BASED AUTONOMOUS VEHICLE

Through the work of researchers and the development of commercially availableproducts, automated guidance has become a viable option for agricultural producers.Some of the limitations of commercially available technologies are that they onlyautomate one function of the agricultural vehicle and that the systems are proprietary toa single machine model.The objective of this project was to evaluate a controller area network (CAN bus)as the basis of an automated guidance system. The prototype system utilized severalmicrocontroller-driven nodes to act as control points along a system wide CAN bus.Messages were transferred to the steering, transmission, and hitch control nodes from atask computer. The task computer utilized global positioning system data to determinethe appropriate control commands.Infield testing demonstrated that each of the control nodes could be controlledsimultaneously over the CAN bus. Results showed that the task computer adequatelyapplied a feedback control model to the system and achieved guidance accuracy levelswell within the range sought. Testing also demonstrated the system\u27s ability tocomplete normal field operations such as headland turning and implement control

Real-Time Pressure and Flow Dynamics Due to Boom Section and Individual Nozzle Control on Agricultural Sprayers

Most modern spray controllers when coupled with a differential global positioning system (DGPS) receiver can provide automatic section or swath (boom section or nozzle) control capabilities that minimize overlap and application into undesirable areas. This technology can improve application accuracy of pesticides and fertilizers, thereby reducing the number of inputs while promoting environmental stewardship. However, dynamic system response for sprayer boom operation, which includes cycling or using auto-swath technology, has not been investigated. Therefore, a study was conducted to develop a methodology and subsequently perform experiments to evaluate tip pressure and system flow variations on a typical agricultural sprayer equipped with a controller that provided both boom section and nozzle control. To quantify flow dynamics during boom section or nozzle control, a testing protocol was established that included three simulation patterns under both flow compensation and no-compensation modes achieved via the spray controller. Overall system flow rate and nozzle tip pressure at ten boom locations were recorded and analyzed to quantify pressure and flow variations. Results indicated that the test methodology generated sufficient data to analyze nozzle tip pressure and system flow rate changes. The tip pressure for the compensated section control tests varied between 6.7% and 20.0%, which equated to an increase of 3.7% to 10.6% in tip flow rate. The pressure stabilization time when turning boom sections and nozzles off approached 25.2 s but only approached 15.6 s when turning them back on for the flow compensation tests. Although extended periods were required for the tip pressure to stabilize, the system flow rate typically stabilized in less than 7 s. The tip flow rate was consistently higher (up to 10.6%) than the target flow rate, indicating that system flow did not truly represent tip flow during section control. The no-compensation tests exhibited tip pressure increases up to 35.7% during boom and nozzle control, which equated to an 18.2% increase in tip flow. Therefore, flow compensation over no-compensation had better control of tip flow rate. A consistent difference existed in dynamic pressure response between boom section and nozzle control. Increased tip pressure and delayed pressure stabilization times indicated that application variability can occur when manually turning sections on and off or implementing auto-swath technology, but further testing is needed to better understand the effect on application accuracy of agricultural sprayers

An Interactive Spray Drift Simulator

The off target movement of pesticides, known as spray drift, results in a reduction in application rates, damage to non-target organisms, and environmental concerns. Much of this drift can be eliminated if its prevalence is understood and best management practices are implemented. Drift prediction software has been developed to serve as a management tool in determining the effects of applying pesticides under certain operating conditions. To further increase the usefulness and instructiveness of such programs, a program was developed which links spray drift prediction software (DRIFTSIM) with a GPS simulator to obtain a two dimensional representation of drift for simulated ground based spraying event. The program was evaluated using a variety of operating conditions to determine their respective effects on drift deposition levels. Results from the simulations show the importance of choosing the largest sufficient nozzle size, operating under low wind speeds, and spraying at the lowest possible boom height. Analysis of multi-swath simulations showed patterns of increased and reduced application rates due to spray drift

Using Yield Monitors to Assess On-Farm Test Plots

Farmer test plots have become a staple for production agriculture. These plots can range from simple side-by-side demonstration plots to a replicated research study. The rush of harvest often creates a challenge for harvesting these plots. Yield monitor data were collected from field scale plots in multiple states to assess ability to measure on-farm research. Grain mass was also measured for each plot with a weigh wagon or certified scale. The variability of yield monitor error (standard deviation) was not correlated with the magnitude of the error (mean). Thus calibration in and of itself will likely not result in more consistent yield monitor error. Determining if treatments or observations from non-replicated studies are different will be challenging. Depending on the chosen probability level, this data indicate that distinguishing a 3 to 9 percent difference was possible. Statistical analysis of replicated trials results in similar conclusions with reference and yield monitor data. Mass flow rate is one factor impacting yield monitor error

Advanced functional materials and manufacturing processes

Guest Editors Jessica Winter, Jawwad Darr, and John Wang introduce the Materials Advances themed collection on advanced functional materials and manufacturing processes

Nozzle and droplet size effects on pesticide performance and drift

Efficient spray application requires applying the correct amount of pesticide in a proper manner to successfully reduce the pest population. In addition pesticide should not be allowed to drift to non-target off-site plants, insects, animals, or humans. Balancing between use of a droplet size small enough for efficacious application, yet large enough to avoid off-target drift is an important consideration for most applications

Proper Implementation of Precision Agricultural Technologies for Conducting On-Farm Research

Precision agricultural technologies have provided farmers, practitioners and researchers the ability to conduct on-farm or field scale research to refine farm management, improve long term crop production decisions, and implement site-specific management strategies. The limitations of these technologies must be understood by those using them to conduct field scale research to gain useful knowledge from such investigations. Therefore, this paper will address how several precision agriculture technologies can be successfully used to conduct research at a field scale level. Discussions will include yield monitors, variable-rate, auto-swath technologies, guidance systems and GPS/GNSS correction services along with proper setup of machinery equipped with these technologies. The importance of selection, calibration, maintenance, and management will be covered and how these can impact results and thereby decisions made from utilizing these technologies for research purposes. Users must understand the limitations of these technologies. Performance expectations that exceed systematic capabilities may produce research data that are dubious at best. Understanding the limitations of precision agriculture technologies will provide useful knowledge for proper setup and analyses of investigations

Generalizable semi-supervised learning method to estimate mass from sparsely annotated images

Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a key component to precision agriculture and mass flow is the critical factor to measure. Measuring mass flow allows for field productivity analysis, cost minimization, and adjustments to machine efficiency. Methods such as volume or force-impact have been used to measure mass flow; however, these methods are limited in application and accuracy. In this work, we use deep learning to develop and test a vision system that can accurately estimate the mass of sugarcane while running in real-time on a sugarcane harvester during operation. The deep learning algorithm that is used to estimate mass flow is trained using very sparsely annotated images (semi-supervised) using only final load weights (aggregated weights over a certain period of time). The deep neural network (DNN) succeeds in capturing the mass of sugarcane accurately and surpasses older volumetric-based methods, despite highly varying lighting and material colors in the images. The deep neural network is initially trained to predict mass on laboratory data (bamboo) and then transfer learning is utilized to apply the same methods to estimate mass of sugarcane. Using a vision system with a relatively lightweight deep neural network we are able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a select season of sugarcane.Comment: 22 pages, 21 figures, Computers and Electronics in Agriculture. arXiv admin note: text overlap with arXiv:1908.0438

Volumetric based mass flow estimation on sugarcane harvesters

Yield monitors on harvesters are a key component of precision agriculture. Mass flow estimation is the critical factor to measure, and having this allows for field productivity analysis, adjustments to machine efficiency, and cost minimization by ensuring trucks are filled maximally without exceeding weight limits. Several common technologies used on grain harvesters, including impact plate sensors, are accurate enough on combines to be valuable but suffer from issues such as drift. Sugarcane is composed of a mixture of billets and trash, which is a very dispersed material with much less consistency than grains. In this study, a 3d point cloud approach is used to estimate volume, from which a calibration factor is derived [density] to translate to mass. The system was proved in concept in a controlled environment using bamboo, achieving an R2 of 97.4% when fitting average volume flow per test against average mass flow after correcting for bulk density changes with volume. The system was also tested on field data, which was collected from nearly 1700 wagon loads from the southern U.S. and Brazil over the course of 3 seasons in both green and burnt cane. Results indicated that the concept is very robust with good accuracy, having seasonal CVs for density values ranging from 6.9% to 16.2%. The camera concept proves relatively robust to environmental conditions. The same approach could be used in sugar beets, potatoes or other sparse/non-flowing crops with highly varying material properties, where traditional mass flow sensors do not work.Comment: 14 pages, 6 figures, computers and electronics in agriculture journa