Large area farms and even moderate sized farms employing custom applicators and harvesters have multiple machines in the same field at the same time conducting the same field operation.  As a method to control input costs and minimize application overlap, these machines have been equipped with automatic section control (ASC). Over application is a concern especially for more irregularly shaped fields; however modern technology including automated guidance combined with automatic section control allow reduced doubling of input application including seeds, fertilizer, and spray.  Automatic section control depends on coverage maps stored locally on each vehicle to determine whether or not to apply input products and up to now, there has not been a clear method to share these maps between vehicles in the same field. Telematics utilizes a cloud computing platform and cellular connectivity which in rural areas is known to have limited service levels. Planting operations were simulated for two 16-row planters, each using two John Deere GreenStar3 2630 monitors, simulated GPS location data stream, electronic rate control units, and individual row unit clutches to have control at the finest granularity. Each simulated planting unit is equipped with automatic section control and telematics gateways to share coverage map data from the first planting unit to JDLink cloud infrastructure then out to the second.  This study evaluates seed cost savings from reducing over application because coverage maps are shared between planting units.    Each field was run twice using parallel tracking, once each with and without coverage map sharing to observe the extent of over application. The field level data were then taken to examine a fictional 1,215 hectare farming operation where the field level data was used as a partial composition of the farm operation.  The average farm savings was $58,909 per year.  Additionally, using the 8,008 scenarios, time value of money was examined to determine the minimum area required annually for a five year breakeven for the technology. As farm input costs increase relative to crop prices, reducing over application will be critical to sustainability.

Sugarcane production system is dependent on a continuous cutting and regrowth of cane plants from their roots, on which traffic should be avoided to ensure the physiological integrity of regrowth and productivity.  This need for accuracy in sugarcane machine traffic boosted the adoption of automated steering systems, especially on harvesters. Tractors with the transshipment trailers, which continually accompany the harvesters in the field, yet do not adopt it or use technology with lower positioning accuracy. The goal of this study is to evaluate the patterns of lateral deviations occurring in transshipment trailers during harvest in straight and curved paths. We used a combination of a tractor and two transshipment trailers with three axles each. The tractor and trailers were each equipped with a GNSS receiver with RTK correction and the deviations of each trailers relative to the tractor were measured taking as a reference the line projected paths. The results show that the errors are far from accepted and 538% higher for curved paths than when in straight paths and that the major cause of deviations are the tractors that drive the whole set, showing the  necessity of further studies involving more variables.

The use of agricultural robots is emerging in a complex scenario where it is necessary to produce more food to feed a crescent population, decrease production costs, fight plagues and diseases, and preserve nature. Around the world, there are many research institutes and companies trying to apply mobile robotics techniques in agricultural fields. Mostly, large prototypes are being used and their shapes and dimensions are very similar to tractors and trucks. In the present study, a small-scale prototype was designed, aiming to facilitate the controller development phase and the execution of experiments in the university using a farm-like scenario (before validating the controllers in a real scenario). It is important to highlight that all control parameters were parameterized to allow the control portability to other prototypes. Helvis is an electric small-scale car-like platform whose traction and steering systems are powered by Maxon motors and driven by EPOS2 boards. Its navigation system uses 2 LiDAR sensors (UTM-30LX) to scan the environment (one in the front and the other in the back) and an Inertial Navigation System (IG500N) to estimate its orientation. In this paper we present experiments carried out in rows of a corn crop field. As previously mentioned, before making the experiments in a real farm, a farm-like scenario was constructed in the lab to calibrate the controller parameters. Since each cornrow constitutes itself a discontinuous wall, a filter based on the LiDAR data was developed in order to create virtual continuous walls. So, these virtual walls were used as references for a wall-follower control system. When it is possible to create walls in both sides of the robot, the navigation problem can be simplified to moving the robot in a virtual aisle. The filter calculates the distances between robot and virtual walls, which are used as input data for the fuzzy controller responsible to keep the robot in the path between corn rows. Its output signal acts in Helvis’ steering system. Real environment experiments allowed adjusting fuzzy rule set and improving robot performance.

The high cost of real-time kinematic (RTK) differential GPS units required for autonomous guidance of agricultural machinery has limited their use in practical auto-guided systems especially applicable to small-sized farming conditions. A laser range finder (LRF) scanner system with a pan-tilt unit (PTU) has the ability to create a 3D profile of objects with a high level of accuracy by scanning their surroundings in a fan shape based on the time-of-flight measurement principle. This paper describes the development of a LRF-based autonomous navigation algorithm for a head-feeding rice combine harvester that could automatically follow straight rice rows based on real-time detection of rice uncut edges. A motor-driven crawler type platform operated on a myRIO real-time controller was constructed to develop a steering control algorithm suitable for such a tracked type-driving mechanism. Noise data existing in raw dataset were removed to extract rice row profiles without unpredictable disturbances by using the revised random sample consensus (RANSAC) method. Boundary points between uncut and cut edges were then determined using the maximum method. The 3D points defined in terms of the LRF sensor coordinates were converted into the vehicle coordinates by considering the platform movement and PTU rotation in order to create a 3D field map of uncut edges for autonomous harvesting. A PID steering control algorithm based on a linear relationship between the lateral deviation and heading error of the mobile platform was implemented. Laboratory tests showed that the PTU operation improved the ability of the uncut edge detection algorithm to detect the target in the presence of interfering objects as compared to that measured without use of the PTU, showing a decrease in lateral RMSE from 21.8 to 5.7 cm whereas there was little change in heading RMSE < 2 deg. A fundamental navigation experiment showed that the mobile platform-mounted LRF scanner system could guide the motor-driven platform following straight and curved edge lines of artificial targets with an acceptable level of oscillation at a traveling velocity of 0.14 m/s. Therefore, the use of a laser based real-time path generation and tracking algorithm would be  feasible in automatically guiding the rice combine harvester.