Programmable plant system

Abstract

Systems, methods, and computer program products are described for generating and using a computer model that models development of a plant based on empirical data. The computer model may eliminate assumptions regarding growing conditions in order to improve outcome predictions. A method may include recommending, using the computer model, different growing conditions based on a particular plant species and observed growing conditions and / or phenotypic features of the plant. The observed growing conditions may be input by sensors and / or a grower. A method may include generating, using the computer model, a planning schedule that includes optimum growing conditions that facilitates optimal scheduling for plant being cultivated. The recommendations and / or planning schedule may facilitate comparison of phenotypic features of a model plant grown under optimal conditions and a plant being grown by a grower.

Description

[0001] related application [0002] This application claims priority to US Provisional Application No. 61 / 549,984, filed October 21, 2011, the contents of which are hereby incorporated by reference in their entirety. technical field [0003] The present disclosure relates to modeling the development of a plant species, and in particular, to generating growing condition recommendations, planning schedules, and / or computer modeling of the development of a plant species based on empirical data. Background technique [0004] Growers (eg, food producers, farmers, individuals, and other growers, etc.) often face the difficulty of producing crops of consistent quality that meet their production, financial, or personal goals. In many cases, growers use random try-and-fail techniques in an attempt to optimize growing conditions and maximize results while minimizing effort and time. [0005] Although several conventional plant models attempt to model plant development, these models a...

AI Technical Summary

Problems solved by technology

Additionally, conventional plant models are often too generic and not unique to individual cultivars or varieties

Flawed outcomes predicted by conventional models limit their usefulness and effectiveness

Furthermore, the routine use of plant models is insufficient because these plant models fail to take into account the actual phenotypic characteristics of the plant being cultivated when making recommendations for growing conditions

Additionally, conventional use of plant models fails to generate planning schedules that are based on empirical data and facilitate efficient planning

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