Variation in the expression of a transmembrane protein influences cell growth in Arabidopsis thaliana petals by altering auxin responses.

Background: The same species of plant can exhibit very diverse sizes and shapes of organs that are genetically determined. Characterising genetic variation underlying this morphological diversity is an important objective in evolutionary studies and it also helps identify the functions of genes influencing plant growth and development. Extensive screens of mutagenised Arabidopsis populations have identified multiple genes and mechanisms affecting organ size and shape, but relatively few studies have exploited the rich diversity of natural populations to identify genes involved in growth control.

SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination.

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring.

Variation in Expression of the HECT E3 Ligase Modulates LEC2 Levels, Seed Size, and Crop Yields in .

Identifying genetic variation that increases crop yields is a primary objective in plant breeding. We used association analyses of oilseed rape/canola () accessions to identify genetic variation that influences seed size, lipid content, and final crop yield. Variation in the promoter region of the HECT E3 ligase gene made a major contribution to variation in seed weight per pod, with accessions exhibiting high seed weight per pod having lower levels of expression.

Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production.

Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery.

What is cost-efficient phenotyping? Optimizing costs for different scenarios.

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios.

CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management.

Background: High-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales.

The cannabinoid receptor agonists, anandamide and WIN 55,212-2, do not directly affect mu opioid receptors expressed in Xenopus oocytes.

A functional link between the cannabinoid and opioid receptor pathways has been proposed based on data showing that cannabinoid effects can be blocked by opioid receptor antagonists and that cannabinoids can bind to opioid receptors. To explore this link in more detail at the receptor level, we tested the hypothesis that cannabinoids directly activate or modulate mu opioid receptor function. The G-protein coupled mu opioid receptor, MOR-1, and its effector, the G-protein activated potassium channel, GIRK2 (Kir3.