Agronomy and Horticulture Department

 

Date of this Version

2018

Citation

An ASABE Meeting Presentation

Paper Number: 1701398

Comments

DOI: 10.13031/aim.201701398

Abstract

Basil (Ocimum basilicum) is a high value crop, currently grown in the field and greenhouses in Nebraska. Winter-time, greenhouse studies were conducted during 2015 and 2016, focusing on cultivars of basil grown on a Cap MAT II® system with various levels of fertilizer application. The goal was to select high value cultivars that could be grown in Nebraska greenhouses. The studies used water content, electrical conductivity, photosynthetically active radiation (PAR), and relative humidity, air and soil media temperature sensors. Greenhouse systems can be very complex, even though controlled by mechanical heating and cooling. Uncertain or ambiguous environmental and plant growth factors can occur, where growers need to plan, adapt, and react appropriately. Plant harvest weights and electronic sensor data was recorded over time and used for training and internally validating fuzzy logic inference and classification models. Studies showed that GENFIS2 ‘subtractive clustering’ of data, prior to ANFIS training, resulted in good correlations for predicted growth (R2 > 0.85), with small numbers of effective rules and membership functions. Cross-validation and internal validation studies also showed good correlations (R2 > 0.85). Decisions on basil cultivar selection and forecasting as to how quickly a basil crop will reach marketable size will help growers to know when to harvest, for optimal yield and predictable quantity of essential oils. If one can predict reliably how much essential oil will be produced, then the methods and resultant products can be proposed for USP or FDA approval. Currently, most plant medicinal and herbal oils and other supplements vary too widely in composition for approval. The use of fuzzy set theory could be a useful mathematical tool for plant and horticultural production studies.

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