THE EFFECT OF SPRING BURNING OF SEED ALFALFA FIELDS ON LEAF CHARACTERISTICS AND ON MANDIBULAR WEAR OF FEMALE LEAFCUTTER BEES [MEGACHILE ROTUNDATA (F.)] (HYMENOPTERA: MEGACHILIDAE)

1993 ◽  
Vol 125 (5) ◽  
pp. 881-886 ◽  
Author(s):  
B.D. Schaber ◽  
E.G. Kokko ◽  
T. Entz ◽  
K.W. Richards
Keyword(s):  
Image Analysis ◽  
Seed Yield ◽  
Consistent Difference ◽  
Analysis Method ◽  
Megachile Rotundata ◽  
Image Analysis Method ◽  
Leaf Characteristics ◽  
Leafcutter Bee ◽  
Seed Yields

AbstractIncreased seed yields often result when alfalfa fields are burned in spring. The main pollinator of seed alfalfa in Alberta is the alfalfa leafcutter bee, Megachile rotundata (F.), that cuts pieces of leaves to build thimble-like cells. One hypothesis for increased seed yield is that alfalfa leaves from burned fields may be easier for the bees to cut. Cutting of leaf pieces causes progressive wear to the mandibles, which could decrease bee efficiency, resulting in reduced pollination and lower seed yields. An image analysis method was used to measure the mandibular wear of leafcutter bees from burned and unburned alfalfa fields. No consistent difference in the amount of wear between bees foraging in burned or unburned fields was found.

QUANTIFICATION OF MANDIBULAR WEAR OF FEMALE ALFALFA LEAFCUTTER BEES, MEGACHILE ROTUNDATA (F.) (HYMENOPTERA: MEGACHILIDAE), BY IMAGE ANALYSIS

1993 ◽  
Vol 125 (1) ◽  
pp. 93-99 ◽  
Author(s):  
E.G. Kokko ◽  
B.D. Schaber ◽  
T. Entz
Keyword(s):  
Image Analysis ◽  
Digital Image Analysis ◽  
Megachile Rotundata ◽  
Wear Analysis ◽  
Southern Alberta ◽  
Before And After ◽  
Alfalfa Seed ◽  
Leafcutter Bee ◽  
Mandibular Teeth ◽  
Seed Yields

AbstractIn southern Alberta, alfalfa seed yields are related to the amount of pollination that occurs before mid-August by the major pollinator, the alfalfa leafcutter bee, Megachile rotundata (F.). Cutting the leaves causes wear to the bee’s mandibular teeth and could reduce the bee’s pollination efficiency and, ultimately, seed production. A method is described for employing digital image analysis to measure mandibular tooth areas for alfalfa leafcutter bees. The method is relatively quick and has high precision and repeatability. This method was used to measure the area of the mandibular teeth for leafcutter bees, before and after foraging in alfalfa seed fields, to evaluate differences in mandibular wear. Analysis of mandibles showed that foraging leafcutter bees collected in late July had significantly smaller tooth areas than pre-foraging bees collected prior to release in June.

scholarly journals Automated image analysis method for oil-release test of lipid-based materials

MethodsX ◽  
2021 ◽  
pp. 101447
Author(s):  
Fabio Valoppi ◽  
Petri Lassila ◽  
Ari Salmi ◽  
Edward Haeggström
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Release Test

A Computerized Image Analysis Method for Measuring Elastic Tissue.

1989 ◽  
Vol 93 (3) ◽  
pp. 358-362 ◽  
Author(s):  
Thomas J. Flotte ◽  
Johanna M. Seddon ◽  
Yuqing Zhang ◽  
Robert J. Glynn ◽  
Kathleen M. Egan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Elastic Tissue ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Computerized Image Analysis

ASSESSMENT OF OSTEOARTHRITIS INITIATIVE–KELLGREN AND LAWRENCE SCORING PROJECTS QUALITY USING COMPUTER ANALYSIS

2010 ◽  
Vol 13 (04) ◽  
pp. 197-201 ◽  
Author(s):  
Lior Shamir ◽  
David T. Felson ◽  
Luigi Ferrucci ◽  
Ilya G. Goldberg
Keyword(s):  
Image Analysis ◽  
Gold Standard ◽  
Population Studies ◽  
Detection Accuracy ◽  
X Rays ◽  
Analysis Method ◽  
Image Analysis Method ◽  
X Ray ◽  
Osteoarthritis Initiative ◽  
Kellgren And Lawrence

The detection of knee osteoarthritis (OA) is a subjective task, and even two highly experienced and well-trained readers might not always agree on a specific case. This problem is noticeable in OA population studies, in which different scoring projects provide significantly different scores for the same knee X-rays. Here we propose a method for quantitative assessment and comparison of knee X-ray scoring projects in OA population studies. The method works by applying an image analysis method that automatically detects OA in knee X-ray images, and comparing the consistency of the scores when using each of the scoring projects as "gold standard." The method was applied to compare the osteoarthritis initiative (OAI) clinic reading derived Kellgren and Lawrence (K&L) scores to central reading, and showed that when using the derived K&L scores the automatic image analysis method was able to accurately differentiate between healthy joints and moderate OA joints in ~70% of the cases. When the OAI central reading scores were used as gold standard, the detection accuracy was elevated to ~77%. These results show that the OAI central readings scores are more consistent with the X-rays, indicating that the central reading better reflects the radiographic features associated with OA, compared to the OAI K&L scores derived from clinic readings.

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