Systems and methods for predictive management of plants. Agricultural (and natural resource) managers may have a multitude of data sets and data sources available, but often lack a meaningful or proven way to assimilate all available data and then conclusively select actions. For example, a vineyard manager may be able to collect data about local and regional weather, precipitation, disease prevalence, insect prevalence, pesticide use, crop varietal, cover crop selection and many other inputs to a predictive machine-learning vineyard management engine. As all this data is collected through local devices and third-party services, a prediction model may be used to determine specific outcomes or recommended actions based on the trained predictive model. For example, the model may be used to predict optimal harvest date, disease spread and vector spread, pest spread and impact, best pesticide use, irrigation plans, fruit quality, and the like.

Systems and methods for predictive management of plants. Agricultural (and natural resource) managers may have a multitude of data sets and data sources available, but often lack a meaningful or proven way to assimilate all available data and then conclusively select actions. For example, a vineyard manager may be able to collect data about local and regional weather, precipitation, disease prevalence, insect prevalence, pesticide use, crop varietal, cover crop selection and many other inputs to a predictive machine-learning vineyard management engine. As all this data is collected through local devices and third-party services, a prediction model may be used to determine specific outcomes or recommended actions based on the trained predictive model. For example, the model may be used to predict optimal harvest date, disease spread and vector spread, pest spread and impact, best pesticide use, irrigation plans, fruit quality, and the like.

The invention relates to a method, a system and a computer program product for ascertaining a plant property of a useful plant in a field, wherein external data concerning the useful plant are stored in a memory unit, wherein a measurement device ascertains a raw measured value relating to at least one plant property, and wherein a calibrated or corrected value for the plant property is ascertained on the basis of the raw measured value taking account of the external data from the memory unit.

The invention relates to a method, a system and a computer program product for ascertaining a plant property of a useful plant in a field, wherein external data concerning the useful plant are stored in a memory unit, wherein a measurement device ascertains a raw measured value relating to at least one plant property, and wherein a calibrated or corrected value for the plant property is ascertained on the basis of the raw measured value taking account of the external data from the memory unit.

A system and method for intelligent soil sampling has for a novelty robotic system 100 that samples soil based on the generation of sampling points through advanced artificial intelligence algorithms. The robotic system 100 comprises a robotic platform 101 with sampling modules 103, 105 and 108, which communicates with a server 111, that consists a localization module 113 containing artificial intelligence algorithms based on satellite images from multiple spectral channels and/or images from high-resolution drone for a given parcel 301, generates zones and determines the coordinates of points as the best representatives of the zones to take place efficiently and quickly sampling the land. Intelligent sampling takes place through several steps where the sampling limits are defined, so a mask is placed on a given plot, after which a pixel matrix with vegetation indices is formed, which is then normalized and K-mean algorithm in different spatial resolutions is worked on with calculation of probability that each pixel 315, 316 belongs to one of the K zones, taking into account its environment with a different number of pixels, where each pixel 315, 316 is associated with changes in spatial resolutions 311, 312, 313, diagonally 314, associated with new values of affiliation probabilities and finally in step 317 a consensus is reached where the final zones are determined and the probability of affiliation of pixels 315, 316 to zones is estimated based on local histograms of matrix entities 311, 312 and 313.

A system and method for intelligent soil sampling has for a novelty robotic system 100 that samples soil based on the generation of sampling points through advanced artificial intelligence algorithms. The robotic system 100 comprises a robotic platform 101 with sampling modules 103, 105 and 108, which communicates with a server 111, that consists a localization module 113 containing artificial intelligence algorithms based on satellite images from multiple spectral channels and/or images from high-resolution drone for a given parcel 301, generates zones and determines the coordinates of points as the best representatives of the zones to take place efficiently and quickly sampling the land. Intelligent sampling takes place through several steps where the sampling limits are defined, so a mask is placed on a given plot, after which a pixel matrix with vegetation indices is formed, which is then normalized and K-mean algorithm in different spatial resolutions is worked on with calculation of probability that each pixel 315, 316 belongs to one of the K zones, taking into account its environment with a different number of pixels, where each pixel 315, 316 is associated with changes in spatial resolutions 311, 312, 313, diagonally 314, associated with new values of affiliation probabilities and finally in step 317 a consensus is reached where the final zones are determined and the probability of affiliation of pixels 315, 316 to zones is estimated based on local histograms of matrix entities 311, 312 and 313.

A display unit is operationally connected to an agricultural implement to provide inputs and operational controls, as well as status and set up, of the implement. The display unit can be a touchscreen or other device that can receive inputs to set up, control, store information, and recall information associated with the operation of the implement. The display unit can provide a number of different types of inputs to allow for the control of the various components of the implement. Information shown, tracked, managed, communicated, or otherwise used by the system can be selected and set up by a user to customize the experience and to provide additional information useful for agricultural operations. The display unit can show and/or output alerts, messages, camera data, and/or other information based on aspects and/or functionality of the implement. The display unit can communicate with other display units associated with other agricultural implements.

A system for determining regenerative carbon footprint in agricultural parcels, including: a sequestration server, having: a management processor that builds first simulation inputs that correspond to baseline management practices, and that builds second simulation inputs that correspond to regenerative management practices; a crop simulation processor, that employs the first simulation inputs to simulate crop growth for a prescribed number of growing seasons to generate first outputs, and configured to employ the second simulation inputs to simulate crop growth for the prescribed number of growing seasons to generate second outputs; and a CO2E determination processor, that employs the first outputs and the second outputs to compute baseline annual carbon dioxide emissions and regenerative annual carbon dioxide emissions, and configured to subtract the baseline annual carbon dioxide emissions from the regenerative annual carbon dioxide emissions to yield a regenerative carbon footprint for the first agricultural parcel.

Disclosed is a method for maize cultivation in the black soil area of northeast China, including the following steps that can be conducted in any order: A. conservation tillage with straw returning: after a crop is harvested in autumn, raking and concentrating straws in rows; B. when sowing, disposing a front-loaded returning device on a sowing machine for a second concentration; C. when sowing, increasing the number of plants through a wide- and narrow-row effect; D. additional application of an organic bacterial fertilizer: where, the organic bacterial fertilizer includes a base fertilizer and a seed fertilizer; the base fertilizer refers to 500 kg of a 40% organic-inorganic mixed slow-release fertilizer; the seed fertilizer refers to 80 kg of an organic bacterial fertilizer; E. weeding with a herbicide treated by a secondary dilution method; and F. subsoiling to break a plow pan in summer: subsoiling flat land parcels to 40 cm.

Disclosed is a method for maize cultivation in the black soil area of northeast China, including the following steps that can be conducted in any order: A. conservation tillage with straw returning: after a crop is harvested in autumn, raking and concentrating straws in rows; B. when sowing, disposing a front-loaded returning device on a sowing machine for a second concentration; C. when sowing, increasing the number of plants through a wide- and narrow-row effect; D. additional application of an organic bacterial fertilizer: where, the organic bacterial fertilizer includes a base fertilizer and a seed fertilizer; the base fertilizer refers to 500 kg of a 40% organic-inorganic mixed slow-release fertilizer; the seed fertilizer refers to 80 kg of an organic bacterial fertilizer; E. weeding with a herbicide treated by a secondary dilution method; and F. subsoiling to break a plow pan in summer: subsoiling flat land parcels to 40 cm.