A team of scientists led by Yosuke Toda, Designated Assistant Professor at the Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, and Fumio Okura, Assistant Professor at the Institute of Scientific and Industrial Research,Osaka University, have developed a system which utilizes image analysis and artificial intelligence (AI) to automatically and precisely analyze the shape of large numbersof seeds from a single image. As the shape of the seed is an important agronomic trait that is closely linked to the yield and quality of crops, a method for automatically determining and evaluating suchfrom an image is an indispensable tool for plant breeding. However, creating the training dataset is laborious and time consuming, especially when the number of objects to annotate is as large as it is in the case of seeds. To date,it has been difficult to quickly and conveniently analyze the number of seeds of different crop species at once.
A team of scientists led by Yosuke Toda, Designated Assistant Professor at the Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, and Fumio Okura, Assistant Professor at the Institute of Scientific and Industrial Research,Osaka University, have developed a system which utilizes image analysis and artificial intelligence (AI) to automatically and precisely analyze the shape of large numbersof seeds from a single image. As the shape of the seed is an important agronomic trait that is closely linked to the yield and quality of crops, a method for automatically determining and evaluating suchfrom an image is an indispensable tool for plant breeding. However, creating the training dataset is laborious and time consuming, especially when the number of objects to annotate is as large as it is in the case of seeds. To date,it has been difficult to quickly and conveniently analyze the number of seeds of different crop species at once.
In this study, Dr. Toda's research team generated a training dataset to be used for machine learning (deep learning) by synthesizing randomized barley seed images on a virtual canvas. The trained model, using only the synthesized data, was ableto detect and segment the individual seeds from images of various barley cultivars with the same degree of accuracy as when done manually. Furthermore, it was also shown that the same method could be used to measure seeds of other crops, such as rice, wheat, oats and lettuce.
As appearance is highly variable between cultivars, the difficulty of image analysis module development often lies in the creation of the training data. Future use of this approach is expected to contribute to the acceleration of the development of machine learning models for measuring the various phenotypes of crops, beyond the measurement of their seeds.
Read More: Institute of Transformative Bio-Molecules (Itbm), Nagoya University
