The University of Tokyo · Agricultural and Biological Sciences
Professor S. Ninomiya's research lab specializes in plant phenomics and agricultural image analysis, focusing on developing advanced computer vision and machine learning techniques to automate the assessment of plant growth, yield, and stress responses. The lab emphasizes non-invasive, high-throughput phenotyping for both model plants and field crops, with applications in yield prediction, seedling vigor evaluation, and soil amendment impact assessment. A key focus is on overcoming challenges related to environmental variability and threshold dependency in image-based plant trait estimation.
Figures are computed from collected data and may differ slightly.
Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detec
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is muc
Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This metho
Abstract. In response to human population increase, the utilization of acid sulfate soils for rice cultivation is one option for increasing production. The main problems associated with such soils are their low pH values and their associated high content of exchangeable Al, which could be detrimental to crop growth. The application of soil amendments is one approach for mitigating this problem, and calcium silicate is an alternative soil amendment that could be used. Therefore, the main objectiv
A new type of behavioural experiment was demonstrated using real variation in flower corolla shape in P. sieboldii. If the range in aspect ratios of petals expands much further, bumblebees may learn to exhibit selective behaviour. However, because discrimination by bumblebees under natural conditions was low, there may be no strong selective behaviour based on innate or learned preferences under natural conditions.
The rhythmic (circadian) leaf movements of soybean were entrained to various light/dark cycles. The phase relation of the rhythm to light/dark cycles varied depending on the light/dark schedules. The light intensity in the light periods, however, had no effect on the phase relation although the light intensity in continuous light schedules had a strong effect on the free-running period. Leaf movements also were controlled by a non-circadian factor which occasionally affect the lowest leaf positi
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