Compositions of the invention detailed herein pertain to the area of robots. More especially, but not by way of limitation, compositions of the invention enable an agricultural robot system and design of robotic harvesting, pruning, culling, weeding, measuring and managing of agricultural products.
The use of robots to automate jobs performed by people is increasing. Robots provide several essential benefits over human labor, including increased efficiency, less cost, more consistent and more critical quality work performed, and the capability to perform hazardous work without endangering people. Independently and collectively, these benefits help companies increase margins and profits, which is essential for sustaining competitiveness.
Agriculture is one business with traditionally low-profit margins and costly manual labor costs. In particular, harvesting can be costly. For some crops, such as tree fruit, collection labor represents the growers' single most considerable expense, up to 50% of total crop cost. Rising labor costs and labor shortages threaten the financial viability of many farms. Therefore, replacing manual labour with robots would be greatly beneficial for harvesting. Additional advantages could be obtained through automating other jobs currently done manually such as spraying, weeding, pruning, culling, thinning, measuring and handling of crops.
GPS controlled mechanical compact tractors and combines already work in wheat and other grain fields. Mechanized harvesters exist that can instinctively harvest fruit by causing the fruit to fall from a plant into a collection object. For example, Korvan Industries, Inc. makes things which shakes raspberries, oranges, grapes, blueberries, etc. off plants. These collection approaches have varying applicability but do not apply to the collection of all crops.
For example, while oranges can be harvested in mass by shaking the tree, this approach only works for the fruit which will be processed. Shaking can't be used for picking oranges sold as new, i.e. table fruit. The violent nature of this harvesting method can bruise the fruit and tear the skin, that is both unappealing to the customer and causes the fruit to rot speedily.
Thus, whole tree harvesting methods comprising “shaking,” are inappropriate for choosing fresh fruits and vegetables such as pears, apples, tomatoes and cucumbers that are to be marketed as whole fruit. A different approach is needed, one in which each piece of fruit is picked independently.
People have strived to develop mechanical pickers to pick entire fruits for years. For example, Pellenc, a French manufacturer, made a prototype orange picker, but dropped the project. One common breakdown mode for these picking systems was that they could not find fruit located on the inside of the tree that can't be seen from out of the canopy. To date, no machine exists that can pick fresh fruits and vegetables efficiently sufficient to compete with human labor in price or yield. Furthermore, tractor hedge cutters for sale have been used in an effort to hedge grape vines. Hedging grape vines with a tractor hedge cutter provide a rough cut to the vines that wildly shapes the vines. The final pruning of the canes on the grapevines is non-trivial and is ideally performed applying a holistic view of the grape vine and planning ere pruning is attempted. To date, no identified compact tractor attachments are configured to perform the final pruning of grape vines intelligently. Known absolute pruning methods utilize humans performing pruning devices by hand. In addition, there are no identified systems that scout and pre-plan harvesting, culling, pruning or different agricultural functions. So to harvesting and pruning, automating other jobs such as thinning, spraying, culling, weeding, timing and managing of agricultural crops can reduce costs and increase consistency and character.
A farmer's main index is the crop in the field. Managing that index requires knowledge about that inventory so as the count, size, color, etc. of the product on each tree, bush, or vine. To date, farmers evaluate these parameters from comparatively small samples taken by manual observation that are likely to errors when projecting parameters of the whole crop. Because of the time, cost, and effort needed to do these estimates, farmers often do not even do these estimates. Satellite imagery has lately enabled macro-level estimates of some of those crop parameters such as tree crop ripeness by color, vigor, or the presence of specific diseases. While this is useful data, it does not provide data at the single tree/bush/vine level. For at most insignificant the reasons described here, there is a requirement for an agricultural robot system and method.
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