In the coming decades, the world’s population will continue to grow. To ensure sufficient food far all, food production will have to increase at an equally fast rate. As the resources in terms of land, water and nutrients are limited, this will have to be realized by increasing the efficiency of agriculture in a sustainable way. Precision agriculture or smart farming has emerged as a promising methodology to increase crop productivity, while reducing environmental and production costs. The aim of precision agriculture to apply the right dose at the right spot at the right time is simple, but not easy to realize in practice. Over the last two decades, the opportunities for precise management of agricultural field operations have increased due to the availability of new geospatial and information technologies (GNSS, sensors, electronics, agricultural machinery controllers and high-resolution remote sensing). This article contains two practical examples of GNSS/RTK technology as applied to agriculture in the US and Europe, demonstrating improved productivity in precision weed management systems.
Robotic cultivator
Complete elimination of herbicide applications while achieving a high percentage of weed control is a very attractive proposition and is critical for organic growers to reduce production costs. However, it is a very challenging task.
Professor David Slaughter and his research group at the department of Biological and Agricultural Engineering (UC Davis) have developed a centimetre-level accuracy geoposition plant mapping system for transplanted row crops that utilized a RTK-GNSS receiver mounted on the tractor or planter (Figure 1).
Slaughter’s team have also designed, using a systems approach based upon GNSS/RTK technology,an automatic intra-row, automatic weeding system using robotically controlled cultivation knives that remove the weeds along the crop row in a transplanted processing tomato crop using an RTK-GNSS based plant map obtained during the transplanting operation(Figure 2). Field test results indicated that this RTK-GNSS based automatic weeding system did not damage any plants while performing intra-row cultivation at travel speeds of 0.8 and 1.6 km/h. Additional information on automatic intra-row weeding system can be found in Perez-Ruiz et al., 2014.
Figure 1: Automatically generated crop geoposition map. The map shows the crop plant locations determined by the automatic GNSS mapping transplanter during planting (orange triangles). The inset photo shows the manual RTK GNSS survey measurement of the plant location’s ground truth. The ground truth points (black circles) were overlaid on the automatically generated map for comparison
Intelligent Sprayer Boom In the Robot Fleets for Highly Effective Agriculture and Forestry Management (RHEA) project, an experimental patch sprayer was designed to deliver a variable-rate application. Each nozzle was independently controlled. A microcontroller mounted on the autonomous tractor used the GNSS-RTK position and the application rate map information to determine the spray control signals to be sent to the boom controller. An unmanned aerial vehicle (UAV or “drones”) equipped with a multi-spectral camera, which is capable of acquiring multi-spectral images at the desired locations and time, was used to map the weed patches using GNSS. This allowed for providing a variable-rate application based on weed infestation maps. Such weed-specific chemical application can reduce the amount of chemical by 24% to 51%, thus reducing cost and protecting the environment from the harmful effects of the chemical (Pérez-Ruiz et al., 2015).
Figure 2: Automatic intra-row mechanical weeding robot. Schematic drawing of the weeding robot showing the miniature pair of intra-row hoes (red triangles) and the odometer sensor (ground wheel on the left side).
References Pérez-Ruiz, M.; Slaughter, D.C.; Fathallah, F.A.; Gliever, C.J.; Miller, B.J. 2014. Co-robotic intra-row weed control system. Biosystems Engineering, Vol. 126, Pages 45-55. Pérez-Ruiz, M.; Gonzalez-de-Santos, P.; Ribeiro, A.: Fernández-Quintanilla, C.; Peruzzi, A.; Vieri, M.; Tomic, S.; Agüera, J. 2015. Highlights and preliminary results for autonomous crop protection. Computers and Electronics in Agriculture Vol. 110, Pages 150-161.
