| CPC G06Q 30/0282 (2013.01) [G06Q 30/0203 (2013.01); G06Q 50/02 (2013.01)] | 8 Claims |
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1. A method, comprising:
receiving, at an information handling device utilizing a prediction engine, a plurality of consumer feedback comments regarding one of a plurality of agricultural food products, wherein each of the plurality of consumer feedback comments comprises information regarding a characteristic of a given agricultural food product, wherein each of the plurality of agricultural food products corresponds to an agricultural source producing an agricultural food product category, wherein a geographical location of each of the plurality of consumers of one of the plurality of agricultural food products is identified and identifies the agricultural source that produced the one of the plurality of food products consumed, wherein the receiving the plurality of consumer feedback comments comprises identifying agricultural food products having at least one of: a positive consumer feedback comment and a negative consumer feedback comment;
dynamically updating, in real-time as consumer feedback comments are received, a rating of each of the plurality of agricultural food products based upon the consumer feedback comments corresponding to a given agricultural food product, wherein the updating comprises aggregating the received consumer feedback comments with previously supplied consumer feedback comments for agricultural food products within the agricultural food product category of a given agricultural source, wherein the rating of each of the plurality of agricultural food products comprises identifying characteristics of agricultural sources of the identified food products;
ranking the plurality of agricultural food products based upon the ratings of the plurality of agricultural food products, wherein the ranking comprises ranking the plurality of agricultural food products against other agricultural food products within an agricultural food product category that are produced by different agricultural sources; training a machine-learning model based on a plurality of input, wherein the plurality of input includes the plurality of consumer feedback comments with respect to at least, one or more of, taste, quality, or nutrition, and characteristics of the agricultural sources identified, including, one or more of, a geographic region, weather data, farming practices, or a growth season, wherein the machine-learning model is trained to identify correlations between the ranking of the plurality of agricultural food products and the characteristics of the agricultural sources identified, wherein the machine-learning model is continuously trained over time based on at least additional input, previous predictions, and outcomes corresponding to the previous predictions;
generating, utilizing the prediction engine, a prediction of a characteristic for at least one agricultural food product not yet produced based upon at least one correlation identified by the machine-learning model between characteristics of the agricultural source and the ranking of the plurality of agricultural food products; and
providing, to the agricultural source, utilizing the prediction engine, and
based upon the prediction of a characteristic for at least one agricultural food product not yet produced, at least one recommendation with respect to a farming practice implemented by the given agricultural source, wherein the recommendation is based upon the ranking of an agricultural food product produced by the given agricultural source and the at least one correlation identified using the machine-learning model, wherein the at least one recommendation with respect to farming practice includes at least, one or more of, a change in harvest time, a change in irrigation technique, a change in seed type, or a change in fertilizer, correlating to at least one characteristic preferred by consumers from the taste, the quality, or the nutrition of the at least one agricultural food product not yet produced.
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