Automated process to identify optimal conditions and practices to grow plants with specific attributes

A computerized system for identifying optimal growing conditions to achieve specific outcomes and for predicting a probability of successfully growing a crop with desired outcomes comprising:

• at least one measuring device for measuring growing conditions and outcomes comprising:
• a spectrometer, an alcohol analyzer, a refractometer, a titrator, and a
• moisture analyzer configured to test and measure location-specific attributes;
• wherein said at least one measuring device transmits collected data to a computer and a database;
• and wherein said computer is programmed to:
• capture said data needed to minimize redundancy and duplication;
• derive said data via captured samples of cropping related elements at specified locations;
• apply causative relationships between growing conditions and crop attributes to a plurality of fields;
• predict, within said plurality of fields, said probability of successfully growing said crop with desired outcomes;
• score a performance of said crop with desired outcomes;
• and
• identify issues that are limiting attributes via a plurality of machine learning operations comprising a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Guassian network, a decision tree learning operation, an artificial neural network a recurrent neural network, a reinforcement learning operation, a linear/non-linear regression operation, a support vector machine, a clustering operation, and a genetic algorithm operation, wherein said issues are based on at least one result from said at least one measuring device;
• and
• said database containing data collected from said at least one measuring device, desired outcomes, and their relationships.