INTELLIGENT UAV SCOUTING FOR FIELD CONDITION MONITORING

Precision agriculture requires detailed and timely information about field condition. In less than the short flight time a UAV (Unmanned Aerial Vehicle) can provide, an entire field can be scanned at the highest allowed altitude. The resulting NDVI (Normalized Difference Vegetation Index) imagery can then be used to classify each point in the field using a FIS (Fuzzy Inference System). This identifies areas that are expected to be similar, but only closer inspection can quantify and diagnose crop properties. In the remaining flight time, the goal is to scout a set of representative points maximizing the quality of actionable information about the field condition. This quality is defined by two new metrics: the average sampling probability (ASP) and the total scouting luminance (TSL). In simulations, the scouting flight plan created using a GA (Genetic Algorithm) significantly outperformed plans created by grid sampling or human experts, obtaining over 99% ASP while improving TSL by an average of 285%

USING THE VEHICLE ROUTING PROBLEM (VRP) TO PROVIDE LOGISTICS SOLUTIONS IN AGRICULTURE

Agricultural producers consider utilizing multiple machines to reduce field completion times for improving effective field capacity. Using a number of smaller machines rather than a single big machine also has benefits such as sustainability via less compaction risk, redundancy in the event of an equipment failure, and more flexibility in machinery management. However, machinery management is complicated due to logistics issues. In this work, the allocation and ordering of field paths among a number of available machines have been transformed into a solvable Vehicle Routing Problem (VRP). A basic heuristic algorithm (a modified form of the Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed to solve the VRP. The solution considered optimization of field completion time as well as improving the field efficiency. Both techniques were evaluated through computer simulations with 2, 3, 5, or 10 vehicles working simultaneously to complete the same operation. Furthermore, the parameters of the VRP were changed into a dynamic, multi-depot representation to enable the re-route of vehicles while the operation is ongoing. The results proved both the Clarke-Wright and Tabu Search algorithms always generated feasible solutions. The Tabu Search solutions outperformed the solutions provided by the Clarke-Wright algorithm. As the number of the vehicles increased, or the field shape became more complex, the Tabu Search generated better results in terms of reducing the field completion times. With 10 vehicles working together in a real-world field, the benefit provided by the Tabu Search over the Modified Clarke-Wright solution was 32% reduction in completion time. In addition, changes in the parameters of the VRP resulted in a Dynamic, Multi-Depot VRP (DMDVRP) to reset the routes allocated to each vehicle even as the operation was in progress. In all the scenarios tested, the DMDVRP was able to produce new optimized routes, but the impact of these routes varied for each scenario. The ability of this optimization procedure to reduce field work times were verified through real-world experiments using three tractors during a rotary mowing operation. The time to complete the field work was reduced by 17.3% and the total operating time for all tractors was reduced by 11.5%. The task of a single large machine was also simulated as a task for 2 or 3 smaller machines through computer simulations. Results revealed up to 11% reduction in completion time using three smaller machines. This time reduction improved the effective field capacity

Using the Vehicle Routing Problem to Reduce Field Completion Times with Multiple Machines

The Vehicle Routing Problem (VRP) is a powerful tool used to express many logistics problems, yet unlike other vehicle routing challenges, agricultural field work consists of machine paths that completely cover a field. In this work, the allocation and ordering of field paths among a number of available machines has been transformed into a VRP that enables optimization of completion time for the entire field. A basic heuristic algorithm (a modified form of the common Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed for optimization. Both techniques were evaluated through computer simulations in two fields: a hypothetical basic rectangular field and a more complex, real-world field. Field completion times and effective field capacity were calculated for cases when 1, 2, 3, 5, and 10 vehicles were used simultaneously. Although the Tabu Search method required more than two hours to produce its solution on an Intel i7 processor compared to less than one second for the method based on Clarke-Wright, Tabu Search provided better solutions that resulted in reduced field completion times and increased effective field capacity. The benefit provided by Tabu Search was larger in the more complex field and as the number of vehicles increased. With ten vehicles in the real-world field, the benefit provided by Tabu Search over the modified Clarke-Wright resulted in reduced completion time of 32%, but even with only three vehicles a 15% reduction was obtained. While ten vehicles may only be applicable with future autonomous machines, simultaneous usage of three machines is not uncommon in current production. As producers consider using multiple machines to improve field completion times and effective field capacity, optimization of the vehicle routing will play an important role in ensuring those improvements are fully realized

Mobile Device-Based Location Services Accuracy

The objective of this study was to characterize the expected position accuracy when using popular mobile devices for location-based agricultural decision-making activities. This study utilized Android-based Nexus 7 tablets and tested the operation of the three location services available on this system in a 24-h fixed location test and a shorter duration multiple location field test. In the 24-h test, the “network” location system had a measured error of 37.19 m while reporting an accuracy of 55.56 m. The “gps” location system had a measured error of 2.57 m and a reported accuracy of 3.20 m. Multiple tests were conducted with the location system added by Google Services code be cause the measured error was much higher than the reported accuracy. With this system, the measured errors were 14.13, 3.4, 24.08, 14.01, and 16.15 m with reported accuracies of 3.95, 4.83, 3.99, 7.18, and 6.68 m, respectively. All the tests with the Google Services location system had much higher variability in location estimates than the “gps” location system. For both services, the high values for reported accuracy did not correspond with high values for measured error. Field testing was only performed with the Google Services and “gps” location systems as the “network” location system did not operate in the test field. Statistical analysis confirmed that the “gps” system was more accurate in this testing but the difference was not as dramatic as in the 24-h testing. The average reported accuracy level was 3.0 m in all field tests with the “gps” system and 3.9 m in all field tests with the Google Services system. The field test data were also used to estimate areas of 0.14-ha rectangular plots. Among all three tests with the “gps” system and all three tests with the Google Services system, the mean absolute area percent error varied from 4% to 7%, and in every test at least one plot was over- or underestimated by at least 10%. The error characteristics and patterns for all but the “gps” service differed significantly from the random walk pattern and/or other characteristics of GNSS locators to which precision farming engineers have become accustomed. Mobile platform creators like Apple and Google are either requiring (Apple) or strongly encouraging (Google) developers to switch to newer services that don’t provide access to the underlying locating mechanism. Therefore, it is clear that careful consideration of these differences and what they may mean to location based apps in agriculture will be important. This work highlights the importance of testing any “smart” devices to determine actual location accuracy before relying on them for making agricultural decisions based on their output