Latest Reports and Publications

November, 2018

Previous research has identified a need for communication guidelines that bring together research and practical experience. This document highlights best practices when communicating agricultural technology to an audience.

October, 2018

Journal of Sensory Studies, October 5, 2018
The article is available here at a cost.

Abstract: This research enabled the creation of a predictive tool to determine consumer preference based on sensory characteristics and to understanding consumer liking for a large and genetically‐diverse apple population. Over two consecutive years, 71 and 83 apples were profiled using descriptive analysis for aroma, taste, and texture attributes. Sensory maps were created, which clustered apples into four groups with common profiles: aromatic‐sweet, acidic, balanced, and mealy. Acceptance data from 219 consumers was collected on a representative subset of 19 apples and related to the sensory properties through external preference mapping. Two consumers groups were identified both preferring juicy, crisp apple but differing in preference for fresh red apple aroma and sweetness (Group 1, 89%) versus more acidic apples with fresh green apple aroma (Group 2, 11%). For both groups, mealy texture was a strong detractor of liking. Preferred sensory characteristics did not differ based on consumer age, gender, or ethnic heritage.

October, 2018

Journal of Environmental Horticulture, September 2018, Vol. 36, No. 3, pp. 92-103
Click here to view the article

Abstract: Air-pruning can improve tree seedling root quality in propagation by subjecting root tips to desiccation, thereby avoiding deflections, but also increases substrate dry-out rates. Several studies have indicated that coconut (Cocos nucifera L.) coir dust can enhance water holding properties, possibly benefiting trees grown in air-pruning trays. However, water availability characteristics are influenced by particle size. In this experiment, coir dust was added into a sphagnum peat-perlite substrate mix at rates of 10, 15 and 20%. An industry standard peat-perlite mix was tested as a fourth substrate type. Red oak (Quercus rubra L.), red maple (Acer rubrum L.), quaking aspen (Populus tremuloides Michx.) and eastern white cedar (Thuja occidentalis L.) were grown from seed in these four substrate types. Physical and chemical properties of all substrate types were analyzed pre-experiment. The particle size distribution was finer and more even in the peat-perlite mix compared to the three coir mixes. The higher proportion of coarse particles in the 20% coir mix may have reduced water availability. Seedlings grown in the 15 and 20% coir mixes had lower above and below-ground growth compared to the 10% coir and peat-perlite mixes in all species except red oak.

Time to change the conversation around turfgrass
August, 2018

Vineland’s three-year research program on improving turfgrass in residential areas has just wrapped up. A summary of key findings on optimal grass variety selection and best fertilization practices can be reviewed in this report.

June, 2018

In: Mansouri A., El Moataz A., Nouboud F., Mammass D. (eds) Image and Signal Processing. ICISP 2018. Lecture Notes in Computer Science, vol 10884. Springer, Cham. pp. 319-328
The article is available here at a cost. 

Abstract: Olpin, A.J., Dara R., Stacey D. and M. Kashkoush. Conventional image processing techniques have been applied to the field of agricultural machine vision for the purposes of identifying crops for quality control, weed detection, automated spraying and harvesting. With the recent advancements in computational hardware Region-based Convolutional Networks have met with varying levels of success in the area of object detection and classification. In this study we found that a Region-based Convolutional Neural Network was able to achieve a 92% accuracy rating while a Region-based Fully Convolutional Network was able to achieve an 87% accuracy rating in the area of object detection operating on a newly create agricultural mushroom dataset.