Computer-implemented methods, computer readable medium and systems for a precision agriculture platform

A computer readable medium, embodied in a non-transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes the processor to perform a set of acts, the acts comprising:

• conducting a field test at an orchard data site exhibiting one or more output conditions by recording a band of frequencies, using radio spectrometry, to generate a reflectance dataset associated with at least one of the output conditions so as to simulate, with the band of frequencies, reflectance data received by a satellite for crops exhibiting a condition detected by the output conditions, wherein the output condition comprises at least one of the following output conditions:
• detecting diseases, detecting salt damage, detecting soil problems, detecting a water leak and detecting generic anomalies;
• storing, in a computer platform, a satellite image and the reflectance dataset associated with at least one of the output conditions for the crops grown on the orchard data site;
• storing, in the computer platform, a plurality of site and crop datasets for at least one orchard data site with permanent crops under analysis, wherein the site and crop datasets comprise a plurality of variables with data values from the following types of site and crop datasets:
• tree age, tree density, soil, weather, quality, yield, alternative bearing factor and phenology;
• processing, in the computer platform, at least one of the site and crop datasets by generating a plurality of tree age groups based on categorization of data values of the tree age for the orchard site;
• executing, on the computer platform, machine-learning techniques to discover an orchard data learned model for the orchard site that predicts at least one of the output condition, by:
• traversing, by the tree age groups, the datasets comprising the tree density, the soil, the weather, the quality, the yield, the alternative bearing factor and the phenology variables, and by discovering applicability of one or more of the variables in the site and crop datasets to at least one output condition for each tree age group;
• storing, in the computer platform, the orchard data learned model for the orchard site;
• processing, in the computer platform, by comparing the satellite image to the reflectance dataset and by generating at least one predictor to predict whether the output condition exists for the orchard data site;
• and
• executing, in the computer platform, the orchard data learned model to predict whether at least one of the output conditions exist for the orchard site.

A system comprising:

• a storage medium, having stored thereon, a sequence of instructions;
• at least one processor, coupled to the storage medium, that executes the instructions to causes the processor to perform a set of acts comprising:
• conducting a field test at an orchard data site exhibiting one or more output conditions by recording a band of frequencies, using radio spectrometry, to generate a reflectance dataset associated with at least one of the output conditions so as to simulate, with the band of frequencies, reflectance data received by a satellite for crops exhibiting a condition detected by the output conditions, wherein the output condition comprises at least one of the following output conditions:
• detecting diseases, detecting salt damage, detecting soil problems, detecting a water leak and detecting generic anomalies;
• storing, in a computer platform, a satellite image and the reflectance dataset associated with at least one of the output conditions for the crops grown on the orchard data site;
• storing, in the computer platform, a plurality of site and crop datasets for at least one orchard data site with permanent crops under analysis, wherein the site and crop datasets comprise a plurality of variables with data values from the following types of site and crop datasets:
• tree age, tree density, soil, weather, quality, yield, alternative bearing factor and phenology;
• processing, in the computer platform, at least one of the site and crop datasets by generating a plurality of tree age groups based on categorization of data values of the tree age for the orchard site;
• executing, on the computer platform, machine-learning techniques to discover an orchard data learned model for the orchard site that predicts at least one of the output condition, by:
• traversing, by the tree age groups, the datasets comprising the tree density, the soil, the weather, the quality, the yield, the alternative bearing factor and the phenology variables, and by discovering applicability of one or more of the variables in the site and crop datasets to at least one output condition for each tree age group;
• storing, in the computer platform, the orchard data learned model for the orchard site;
• processing, in the computer platform, by comparing the satellite image to the reflectance dataset and by generating at least one predictor to predict whether the output condition exists for the orchard data site;
• and
• executing, in the computer platform, the orchard data learned model to predict whether at least one of the output conditions exist for the orchard site.