Periodically taken photographs reveal the effect of pollinator insects on seed set in lotus flowers.

Understanding of pollination systems is an important topic for evolutionary ecology, food production, and biodiversity conservation. However, it is difficult to grasp the whole picture of an individual system, because the activity of pollinators fluctuates depending on the flowering period and time of day. In order to reveal effective pollinator taxa and timing of visitation to the reproductive success of plants under the complex biological interactions and fluctuating abiotic factors, we developed an automatic system to take photographs at 5-s intervals to get near-complete flower visitation by pollinators during the entire flowering period of selected flowers of Nelumbo nucifera and track the reproductive success of the same flowers until fruiting.

Automatic estimation of heading date of paddy rice using deep learning.

Background: Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming.

EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions.

Understanding interactions of genotype, environment, and management under field conditions is vital for selecting new cultivars and farming systems. Image analysis is considered a robust technique in high-throughput phenotyping with non-destructive sampling. However, analysis of digital field-derived images remains challenging because of the variety of light intensities, growth environments, and developmental stages.

Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images.

Background: Flowering (spikelet anthesis) is one of the most important phenotypic characteristics of paddy rice, and researchers expend efforts to observe flowering timing. Observing flowering is very time-consuming and labor-intensive, because it is still visually performed by humans. An image-based method that automatically detects the flowering of paddy rice is highly desirable.

Monitoring system for farming operations with wearable devices utilized sensor networks.

In order to automatically monitor farmers' activities, we propose a farm operation monitoring system using "Field Servers" and a wearable device equipped with an RFID reader and motion sensors. Our proposed system helps in recognizing farming operations by analyzing the data from the sensors and detected RFID tags that are attached to various objects such as farming materials, facilities, and machinery. This method can be applied to various situations without changing the conventional system.