Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Construction or agricultural-type vehicle
Reexamination Certificate
1999-09-21
2002-01-01
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Construction or agricultural-type vehicle
C702S002000, C702S005000, C086S010000, C340S988000, C340S995190, C172S002000, C172S005000, C172S006000, C342S022000
Reexamination Certificate
active
06336066
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a process for using localized agricultural data to optimize the cultivation of perennial plants.
2. Description of Background and Relevant Information
The concept of Precision Agriculture is known, and it has been widely used for about ten years in the context of large annual crops (especially grains). This novel concept arose from the discovery of two types of sensors that proved to have sufficient precision: absolute positioning of the GPS type, and on-board yield sensors.
Absolute Positioning
The GPS (Global Positioning System) is a satellite positioning system. Its principle is as follows: the U.S. Department of Defense has a set of 24 satellites in orbit around the earth; these satellites transmit, toward the earth, radio waves indicating the time and their position in space. On earth, each GPS receiver receives, at all times, the waves from at least four of these satellites: those which, at an instant t, are visible to it, i.e. not blocked by the terrestrial globe; from the transit time of the wave between transmitter and receiver, the receiver calculates its distance with respect to each visible satellite; thus, if it picks up the signals from at least four satellites, the receiver can calculate its position in space, i.e. its longitude, latitude and altitude.
The precision of this calculation is from 30 to 100 m, with a simple (“natural”) GPS. To improve this precision, differential GPS (DGPS) is used: with the aid of stationary GPS receivers in known positions, the positioning errors transmitted by the satellite signals are calculated, and with the aid of these calculations, the values obtained by natural GPS receivers are corrected. The precision then becomes about 1 m.
For Precision Agriculture, DGPS is the solution adopted by most manufacturers of machines for harvesting or treating grains.
Yield Sensors
Weight measurement: There are several existing yield measuring principles for grains, but they are not adapted to the harvesting of grapes or moist products. This yield data must be corrected by the moisture content of the grain, which we also know how to measure.
These principles only work on a granular and largely dry product (about 90% dry matter). A moist product like grapes with more than 80% moisture, with a high juice content, cannot be measured with processes of this type.
That is why the Applicant developed a specific weighing system, which is the object of a Patent Application filed on Sept. 5, 1998 (Ser. No. 09/297,057), entitled “On-board Device and Method for Continuous Weighing of Harvest and Harvesting Machines Using Same,” the disclosure of which is hereby incorporated by reference thereto in its entirety.
Mapping Systems
A mapping system has multiple objects, all of them being intended to help the farmer in the management of his crops over a cycle of several years, due to:
information on the potentialities of the various zones of the parcel;
an orientation to the entry of other parameters (for example, positioning of soil samplings);
an orientation to future farming operations (concept of “recommendation”);
an evaluation of the results obtained through the controlled management of these crops.
The structure of a mapping system is known (see, for example, document WO 98/21928 A); it includes all or some of the following components:
an on-board absolute positioning sensor of the DGPS type;
an on-board sensor for real-time entry of the yield (or other data);
an on-board computer and entry software that synchronizes the preceding two pieces of information;
mapping software, for representing the data collected in the parcel; this software is either on-board, or available on a computer on the farm;
software to aid in the creation of maps for recommending subsequent farming operations, generally on the farm's computer.
FIG. 1
is a block diagram of a mapping system that is valid both in the general sense and in the scope of the present invention.
In the mapping software, each parcel is represented by a map with two dimensions (latitude and longitude), in which the various ranges of values are represented in specific ways: for example, it is possible to assign each range a specific color, or a specific type of hatching.
Moreover, the recommendations essentially apply to two farming operations, fertilization and spraying:
fertilization: this involves providing the plants with the nutrients, in the form of fertilizers, they lack, mainly nitrogen;
spraying (pesticides): this involves distributing products for killing the microorganisms or insects that are capable of attacking the crops, or the weeds capable of competing with them. The operation is repeated whenever a rain has washed away the previous products.
For these operations, the challenge is to optimize the quantities to be distributed, to economize on expensive products, and also to prevent the unabsorbed products from polluting the ground water.
It is possible to note some limitations of these systems, linked to the crops involved.
The known mapping software programs represent the parcel as a continuous surface, and filter the data in identical fashion in the two horizontal dimensions, so as to display homogeneous zones. There is currently no known intra-parcel mapping system adapted to grape vines or fruit trees that makes use of the row structure and displays the empty spaces between rows.
Recommendation software programs handle large-scale modulations, typically with 20 m×20 m sections. These scales correspond to the nature of these farming operations, which do not require precise positioning. There is no known modulation of these operations to the individual plant.
Finally, the precision of the positioning is limited by the rough precision of the DGPS sensor, without the possibility to improve it by using other points on the path. The paths of the machine in the field being irregular, there is no rule that makes it possible to link the points to one another in any precise way. For example, the straightness of a path, or the parallelism between two successive passes are extremely rough.
SUMMARY OF THE INVENTION
The present invention is intended for farmers growing perennial plants (particularly vine and tree growers), and it falls within the general scope of efforts to optimize the quality of the products, which is one of the major concerns of these farmers.
To contribute to the optimization of quality, Precision Agriculture tools are adapted for these crops. This requires specific tools for measuring the yields.
For viticulture, such a tool exists, due to the Applicant's invention of a grape weighing device.
For arboriculture, the concept of yield sensors only makes sense when the fruit harvest is automated. Within the scope of an automated fruit harvest, (for which the Applicant has also created machines using artificial intelligence), in which each fruit is picked individually and its size is determined, it is clear that the harvest yields are easy to establish using on-board computers.
However, even with the use of yield sensors, the simple adaptation of known Precision Agriculture concepts to perennial plants would not be of much use. In fact:
The spatial modulation of the fertilization and spraying operations provides far less of an advantage than in large crops. For example, the amounts of nitrogen constitute only a third of the quantities applied to grains.
For the other farming operations, which require locating the individual plant, especially winter pruning, the precision available with the DGPS sensor is likely to be insufficient.
The representation of the data in continuous surfaces is not adapted to the row structures of perennial plants.
When the main objective is quality, and not yield, it is essential to collect, in addition to the yields, data representing quality, particularly during harvest operations. This may include, for example, sugar content, acidity, the appearance of the grapes determined by artificial vision (presence of foreign bodies, rotting, etc.), and the size an
Bourely Antoine
Pellenc Roger
Cuchlinski Jr. William A.
Greenblum & Bernstein P.L.C.
Mancho Ronnie
Pellenc S.A.
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