A computer-implemented method for managing crop harvesting activities is implemented by a harvest advisor computing device in communication with a memory. The method includes receiving an initial date of a crop within a field, receiving an initial moisture value associated with the crop and the initial date, and receiving a target harvest moisture value associated with the crop. The method also includes receiving field condition data associated with the field. The method further includes computing, by the harvest advisor, a target harvest date for the crop based at least in part on the initial date, the initial moisture value, the field condition data, and the target harvest moisture value, and displaying the target harvest date for the crop to the grower for harvest planning. The target harvest date indicates a date at which the crop will have a present moisture value approximately equal to the target harvest moisture value.

A computer-implemented method for managing crop harvesting activities is implemented by a harvest advisor computing device in communication with a memory. The method includes receiving an initial date of a crop within a field, receiving an initial moisture value associated with the crop and the initial date, and receiving a target harvest moisture value associated with the crop. The method also includes receiving field condition data associated with the field. The method further includes computing, by the harvest advisor, a target harvest date for the crop based at least in part on the initial date, the initial moisture value, the field condition data, and the target harvest moisture value, and displaying the target harvest date for the crop to the grower for harvest planning. The target harvest date indicates a date at which the crop will have a present moisture value approximately equal to the target harvest moisture value.

A vertical micro-farm system, the system may include an external structure; a nursery positioned inside the external structure for an initial growth process; a plurality of grow towers inside the external structure for a second growth process; an autonomous robot configured to move baby plants from the nursery to the plurality of grow towers after completion of the first growth process; and a produce processing and packing machine for receiving matured plants for processing after the baby plants have completed the second growth process in the plurality of grow towers, wherein the matured plants are transferred from the plurality of grow towers to the produce processing and packing machine by the autonomous robot.

A vertical micro-farm system, the system may include an external structure; a nursery positioned inside the external structure for an initial growth process; a plurality of grow towers inside the external structure for a second growth process; an autonomous robot configured to move baby plants from the nursery to the plurality of grow towers after completion of the first growth process; and a produce processing and packing machine for receiving matured plants for processing after the baby plants have completed the second growth process in the plurality of grow towers, wherein the matured plants are transferred from the plurality of grow towers to the produce processing and packing machine by the autonomous robot.

A computer system and computer-implemented techniques for determining crop harvest times during a growing season based upon hybrid seed properties, weather conditions, and geo-location of planted fields is provided. In an embodiment, determining crop harvest times for corn fields may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including seed type and relative maturity, and weather data for the specific geo-location of the agricultural field. Weather data includes temperature, humidity, and dew point for a specified period of days. Using digitally programmed equilibrium moisture content logic within the computer system to create and store, in computer memory, an equilibrium moisture content time series for the specific geo-location that is based upon weather data. The equilibrium moisture content is used to determine the rate of grain dry down because it gives a basis for how strongly water vapor will dissipate from a kernel to open air. Using digitally programmed grain moisture logic of the computer system to calculate and store in computer memory R6 moisture content for a specific hybrid seed based on a plurality of hybrid seed data. Using digitally programmed grain dry down logic of the computer system to create and store in computer memory a grain dry down time series model for the specific hybrid seed at the specific geo-location that represents the estimated moisture content of the kernel over specified time data points. The grain dry down time series is based upon the equilibrium moisture content time series, the estimated R6 date, the estimated R6 moisture content value, and specific hybrid seed properties. Using digitally programmed harvest recommendation logic of the computer system to determine and display a harvest time recommendation for harvesting crop grown from a specific hybrid seed plant based on the grain dry down time series and the desired moisture level of the grower.

A computer system and computer-implemented techniques for determining crop harvest times during a growing season based upon hybrid seed properties, weather conditions, and geo-location of planted fields is provided. In an embodiment, determining crop harvest times for corn fields may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including seed type and relative maturity, and weather data for the specific geo-location of the agricultural field. Weather data includes temperature, humidity, and dew point for a specified period of days. Using digitally programmed equilibrium moisture content logic within the computer system to create and store, in computer memory, an equilibrium moisture content time series for the specific geo-location that is based upon weather data. The equilibrium moisture content is used to determine the rate of grain dry down because it gives a basis for how strongly water vapor will dissipate from a kernel to open air. Using digitally programmed grain moisture logic of the computer system to calculate and store in computer memory R6 moisture content for a specific hybrid seed based on a plurality of hybrid seed data. Using digitally programmed grain dry down logic of the computer system to create and store in computer memory a grain dry down time series model for the specific hybrid seed at the specific geo-location that represents the estimated moisture content of the kernel over specified time data points. The grain dry down time series is based upon the equilibrium moisture content time series, the estimated R6 date, the estimated R6 moisture content value, and specific hybrid seed properties. Using digitally programmed harvest recommendation logic of the computer system to determine and display a harvest time recommendation for harvesting crop grown from a specific hybrid seed plant based on the grain dry down time series and the desired moisture level of the grower.

Disclosed is an automated walnut picking and collection method based on multi-sensor fusion technology, including: operation 1.1: when a guide vehicle for automated picking and collection is started, performing path planning for the guide vehicle; operation 1.2: remotely controlling the guide vehicle to move in a park according to a first predetermined rule, and collecting laser data of the entire park; operation 1.3: constructing a two-dimensional offline map; operation 1.4: marking a picking road point on the two-dimensional offline map; operation 2.1: performing system initialization; operation 2.2: obtaining a queue to be collected; operation 2.3: determining and sending, by the automated picking system, a picking task; operation 2.4: arriving, by the picking robot, at picking target points in sequence; operation 2.5: completing a walnut shaking and falling operation; and operation 2.6: collecting shaken walnuts. The provided method can obtain high-precision fruit coordinates and complete autonomous harvesting precisely and efficiently.

A method of harvesting crop material includes capturing an image of a plurality of regions 28 of a field 50. The respective image of the regions 28 is analyzed to determine data related to constituent species of the crop material located within each of the regions 28. The data is associated with a region identifier 48 assigned to the respective one of the regions 28, and saved in a memory 42 of a computing device 30. The crop material in the field 50 is then harvested and formed into a bale. The harvested crop material formed into the bale is gathered from a subset 46 of the regions 28. A region identifier 48 of each of the regions 28 included in the subset 46 of the regions 28 is associated with the bale identifier 38, such that the data related to constituent species of the crop material included in the bale is associated with the bale and may be obtained by querying the computing device 30.

A method of harvesting crop material includes capturing an image of a plurality of regions 28 of a field 50. The respective image of the regions 28 is analyzed to determine data related to constituent species of the crop material located within each of the regions 28. The data is associated with a region identifier 48 assigned to the respective one of the regions 28, and saved in a memory 42 of a computing device 30. The crop material in the field 50 is then harvested and formed into a bale. The harvested crop material formed into the bale is gathered from a subset 46 of the regions 28. A region identifier 48 of each of the regions 28 included in the subset 46 of the regions 28 is associated with the bale identifier 38, such that the data related to constituent species of the crop material included in the bale is associated with the bale and may be obtained by querying the computing device 30.

Systems and methods are disclosed for determining a ripeness, firmness, or consumption suitability for food items, such as produce and fruit. The disclosure can provide for generating a machine learning model to detect food item ripeness. The model can be generated using destructive and non-destructive measurements of one or more food items. The model can then be applied to spectral imaging data of food items in real-time. The spectral imaging data can be captured by a point spectrometer. Using the model and spectral imaging data, the ripeness of the food items can be determined in a non-destructive manner. The determined ripeness of the food items can then be used to determine one or more supply chain modifications.