GPS-Based Intra-Row Weed Control System: Performance and Labor Savings

Researchers at UC Davis and elsewhere have demonstrated the technical feasibility of using RTK-GPS technology to automatically create crop plant maps at planting (Sun et al., 2010; Perez-Ruiz, 2011). This paper describes the development and field evaluation of a completely automatic system for intra-row weed control in tomato fields. This system uses an automatically generated GPS crop plant map to automatically control the path of a set of mechanical intra-row weed knives in order to kill weeds growing between tomato plants in the seedline. The manual labor savings were evaluated in controlled field trials and the labor savings benefits associated with the use of this system are reported

Development of an RTK GPS plant mapping system for transplanted vegetable crops.

This study investigated the feasibility of using real-time kinematics (RTK) GPS to automatically map the locations of tomato transplants in the field as they are planted using a vegetable crop transplanter retrofitted with an RTK GPS receiver, and an on board real-time controller. Two detection methods were evaluated for sensing plant location during planting. One method used an infrared light beam sensor to detect the stem location of each plant immediately after planting. The second method used an absolute shaft encoder mounted on the planting wheel to sense the location that each plant was placed in the soil. Odometry was used to determine the actual Easting and Northing GPS coordinates of each plant by interpolation from the original RTK GPS data stream. A field test was conducted to compare the accuracy of this transplant map with actual plant location. The average absolute differences between the automatically generated transplant map and the plant location determined by GPS survey was 0.8 to 2.1 cm in the Northing direction and 1.6 to 3.8 cm in the Easting direction, which was also the travel direction. Results suggest the feasibility of creating an accurate plant map using an RTK GPS equipped transplanter

Design of a Soil Cutting Resistance Sensor for Application in Site-Specific Tillage

One objective of precision agriculture is to provide accurate information about soil and crop properties to optimize the management of agricultural inputs to meet site-specific needs. This paper describes the development of a sensor equipped with RTK-GPS technology that continuously and efficiently measures soil cutting resistance at various depths while traversing the field. Laboratory and preliminary field tests verified the accuracy of this prototype soil strength sensor. The data obtained using a hand-operated soil cone penetrometer was used to evaluate this field soil compaction depth profile sensor. To date, this sensor has only been tested in one field under one gravimetric water content condition. This field test revealed that the relationships between the soil strength profile sensor (SSPS) cutting force and soil cone index values are assumed to be quadratic for the various depths considered: 0–10, 10–20 and 20–30 cm (r2 = 0.58, 0.45 and 0.54, respectively). Soil resistance contour maps illustrated its practical value. The developed sensor provides accurate, timely and affordable information on soil properties to optimize resources and improve agricultural economyMinistry of Science and Innovation RTA2006-00058-C03-0

Automatic weed control system for processing tomatoes

WORLD CONGRESS OF THE INTERNATIONAL COMMISSION OF AGRICULTURAL AND BIOSYSTEMS ENGINEERING (17) (17.2010.QUEBEC CITY, CANADA)This study describes a fully automatic system developed at UC Davis for intra-row mechanical weed control for processing tomatoes in California. We developed a novel weed control system using a real-time kinematics (RTK) global positioning system (GPS) to automatically control the path of a pair of weed knives based upon an automatically generated GPS plant map. The system was capable of precisely guiding mechanical weed knives within the seedline of the crop row and around the crop plants as the system was pulled along the row. In this study, processing tomato plants were transplanted using a GPS-enabled transplanter, which developed a precision plant map documenting the geo-spatial location of each tomato plant. At the time of first cultivation, a few weeks after planting, the GPS-controlled weed knives were operated in seven tomato rows. The weed knives were set to "open" 6 cm prior to reaching, and "close" 6 cm after passing each tomato plant, killing weeds between tomato plants when the knives were in the closed position. Results show that the average distance between knife opening and closing events was 12.4 cm with a standard deviation of 1.4 cm. The standard deviation of the opening and closing positions (relative to the crop plant) was 2.08 and 2.11 cm, respectively. These results demonstrate the feasibility of using RTK-GPS to automatically control a mechanical weed control system for sustainable production of row crops

Active Optical Sensors for Tree Stem Detection and Classification in Nurseries.

Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops

Accuracy and Feasibility of Optoelectronic Sensors for Weed Mapping in Wide Row Crops

The main objectives of this study were to assess the accuracy of a ground-based weed mapping system that included optoelectronic sensors for weed detection, and to determine the sampling resolution required for accurate weed maps in maize crops. The optoelectronic sensors were located in the inter-row area of maize to distinguish weeds against soil background. The system was evaluated in three maize fields in the early spring. System verification was performed with highly reliable data from digital images obtained in a regular 12 m × 12 m grid throughout the three fields. The comparison in all these sample points showed a good relationship (83% agreement on average) between the data of weed presence/absence obtained from the optoelectronic mapping system and the values derived from image processing software (“ground truth”). Regarding the optimization of sampling resolution, the comparison between the detailed maps (all crop rows with sensors separated 0.75 m) with maps obtained with various simulated distances between sensors (from 1.5 m to 6.0 m) indicated that a 4.5 m distance (equivalent to one in six crop rows) would be acceptable to construct accurate weed maps. This spatial resolution makes the system cheap and robust enough to generate maps of inter-row weeds