Crop breeding enhanced through machine learning

Science Notes: Artificial intelligence analyzes large numbers of seeds from a single image

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The establishment of a method to rapidly create elite crop varieties via selective breeding is a matter of urgency to maintain the food supply. In order to select such cultivars, it is necessary to define and evaluate how to identify superior varieties. The shape of seeds is a trait closely linked to the quality and yield of crops, and an important factor when conducting selective breeding.

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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 uses image analysis and artificial intelligence (AI) to analyze the shape of large numbers of seeds from a single image.

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 able to detect and segment the individual seeds from images of various barley cultivars as accurately as when done manually, as well as being able to analyze seeds of other crops.

Training data is required to make use of deep learning. Usually, training data is prepared by hand, for example by labeling every object in the images with different colours. However, for objects such as seeds, whose numbers are vast, creating the training data is very time consuming (for example, having to individually colour hundreds of seeds for 10s or 100s of images for each seed variety). Thus, it has been considered difficult to generate a machine learning model that can quickly and simply analyze the seed shapes of different varieties or species. Dr. Toda’s research group succeeded in creating a large volume of training data from only a small number of seeds to effectively train the machine learning model.

The study showed that the same method can readily be employed to measure the seeds of a variety of different crops, such as rice, wheat, oats and lettuce.

Beyond just a variety evaluation, this study is expected to contribute to the plant science domain by revealing characteristics of seeds not formerly observed by the human eye.

The majority of research into instance segmentation-based image analysis is conducted using existing datasets including things such as people and cars. On the other hand, plant image analysis has a variety of its own characteristics.

Since there is great variation in plants’ species, location and individual appearance, different training data is needed for respective applications. While this is also the case for others with multiple applications, the creation of new training data for plants is particularly difficult.

The method of generating synthetic training data employed in this study can be used in a variety of applications. Based on the initiative of this research, it is expected that it will be possible to go beyond the analysis of seeds, and accelerate the development of a machine learning model for the measurement of various plant phenotypes.

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