An agricultural robot system and method of harvesting, pruning, culling, weeding, measuring and managing of agricultural crops. Uses autonomous and semi-autonomous robot(s) comprising machine-vision using cameras that identify and locate the fruit on each tree, points on a vine to prune, etc., or may be utilized in measuring agricultural parameters or aid in managing agricultural resources. The cameras may be coupled with an arm or other implement to allow views from inside the plant when performing the desired agricultural function. A robot moves through a field first to “map” the plant locations, number and size of fruit and approximate positions of fruit or map the cordons and canes of grape vines. Once the map is complete, a robot or server can create an action plan that a robot may implement. An action plan may comprise operations and data specifying the agricultural function to perform.

Agricultural robot system and method

Agricultural operations by applying flexible manufacturing software, robotics and sensing techniques to agriculture. In manufacturing operations utilizing flexible machining and flexible assembly robots, work pieces flow through a fixed set of workstations on an assembly line. At different stations are located machine vision systems, laser based raster devices, radar, touch, photocell, and other methods of sensing; flexible robot armatures and the like are used to operate on them. This flexible agricultural automation turns that concept inside out, moving software programmable workstations through farm fields on mobile robots that can sense their environment and respond to it flexibly. The agricultural automation will make it possible for large scale farming to take up labor intensive farming practices which are currently only practical for small scale farming, improving land utilization efficiency, while lowering manpower costs dramatically.

Flexible agricultural automation

A peanut combine towed and powered by a tractor during harvesting includes a collection basket having a basket support frame mounted on the top of the combine by four load cells. A respective load cell is located beneath each lower bottom corner of the base of the collection basket between the support frame and a support member, such as a steel support channel, fastened on the top of the combine. The load cells produce analog data signals representative of weight of the collection basket at any given instance as the crop is harvested. The load cells transmit data analog signals to a data acquisition system (DAS) via a summing device and anti-aliasing filter. The DAS includes a CPU and an analog to digital converter to convert the analog load cell data to digital signals and digitally filters the digital signal to reduce signal noise resulting from harmonic vibrations of the combine straw walker mechanism for further processing. The CPU is interfaced with a global positioning device that provides time, latitude and longitude information of the combine to the CPU and also with a sensor that inputs tractor/combine information to the CPU from which combine speed can be determined. An interface device is provided on the tractor and interfaces to the CPU for providing signals representative of the number of rows harvested (row counter) to the CPU as input by the tractor operator. The CPU includes software for determining the instantaneous crop yield, yield rate, and area harvested as a crop field is being harvested for display to the tractor operator.

Crop yield monitoring system

Field experiments for evaluating the incorporation of RFID and barcode registration and digital weighing technologies in manual fruit harvesting

Yield monitor apparatus and methods for a forage processing machinery are provided according to the invention. The yield monitor measures a forage yield by variously measuring a forage impingement force, a forage volume flow, a forage volume increment accumulation, or a forage processing machinery drive load. The yield monitor may generate a yield amount, with the yield amount capable of being stored and displayed to a forage processing machinery operator. Alternatively, the yield monitor may generate a groundspeed control signal that is used by the forage processing machinery to control a forage processing machinery groundspeed or other forage processing machinery parameters.

Yield monitor for forage crops

The Peanut Yield Monitoring System.

The most essential component of precision farming is the yield monitor, a sensor or group of sensors installed on
harvesting equipment that dynamically measure spatial yield variability. Yield maps, which are produced using data from
yield monitors, are extremely useful in providing the farmer a color–coded visual image clearly showing the variability of
yield across a field. University of Georgia scientists recently completed development work on PYMS, the Peanut Yield
Monitoring System. PYMS uses load cells for instantaneous load measurements of harvested peanuts and has proven to be
accurate to between 2% and 3% on a trailer–load basis and to approximately 1% on a field basis when using data collected
during combine operation. PYMS data are accurate to around 1% on a basket–load basis when using data collected under
static conditions. The instantaneous accuracy of PYMS was calculated to be 700 kg/ha. Basing management decisions on the
yield of individual pixels of PYMS yield maps is not realistic. The strength of PYMS is in differentiating yield trends and
evaluating management practices. The system was extensively and successfully field–tested over a 3–year period and
evaluated by 11 users during 1999, all of whom were able to use the resulting yield maps to evaluate current management
practices or to develop future management plans. The University of Georgia has submitted a patent application for PYMS,
and the technology has been licensed.

The peanut yield monitoring system

A yield monitoring system, which uses load cells mounted below the basket, was developed for application to
existing and new peanut combines. A tank weighing assembly (free movement in both lateral directions) mount was used
with double ended shear beam load cells in a 90° configuration to create the most flexibility with least opportunity for
binding at the mounts. Slopes less than 2° did not appreciably affect the load cell response. Moving average and
regression methods were used to translate the continuous loading of the basket to incremental loading rates. The system
was capable of reproducing input loading rates to within a 1.5 to 7.3% error over an assumed ground area of 20 m 2 to
10 m 2 (220 ft 2 to 110 ft 2 ), respectively (under static operating conditions). The larger the area (number of values)
included in the average, the smaller the error, but the less sensitivity to changing yield conditions. Additional integration
with position signals, improved filtering, and some method of adjusting for pod moisture and trash are necessary to fully
develop the system for commercial application.

Development and implementation of a load cell yield monitor for peanut

A prototype peanut yield monitoring system based on load cell transducers was evaluated for use in precision farming applications. Noise characteristics under simulated field conditions were examined, and the effect of mixing within the peanut combine during harvest was also investigated. Evaluation results showed that the system has potential for providing limited quality site-specific yield measurements for yield mapping applications.

J. S. Durrence

Papers

Crop yield monitoring system

The Peanut Yield Monitoring System.

The peanut yield monitoring system

Development and implementation of a load cell yield monitor for peanut

A Load Cell Based Yield Monitor for Peanut Feasibility Study