Visualizing the biological information of plants to benefit the future of agriculture

Establishing "strong agriculture" as a business

In any era, a stable supply of food is an essential issue. With farms in Japan largely family operated, Professor Takayama worries about their ability to continue operating in the future. In addition, the population of farmers continues to rapidly decline, and consumer habits will significantly change over the next ten years. In order to respond to both of these issues, Professor Takayama states that it is essential to create a new agricultural production system that can provide a stable supply of fresh vegetables and other goods.

According to Professor Takayama, "The best option is indoor farming and/or intelligent greenh ouses. If you look at the situation of about ten years ago, hand-grown vegetables from local farmers were significantly more popular than indoor-grown varieties, but today it is now possible to provide a stable supply of greenhouse-grown vegetables offering 100% traceability. Greenhouse-grown vegetables are now increasingly entering the mainstream for consumers due to their proven quality, safety and security. With middle-aged people focusing on their health, and the demand for fresh vegetables having grown by around 150% to 200% over the past ten years, I believe that, moving forward, reliance on large-scale indoor farms will gradually increase . On the other hand, operating such facilities requires adequate financing, which means the farms will need to establish proper business models. To achieve that, managing production will be essential. Up to now, this industry has relied mainly on the intuition and experiences of experts, but there was essentially no scientific approach," says Professor Takayama.

Knowledge about environmental controls for indoor farms first developed approximately 20 years ago, and automated computer controls are already being implemented across the world. However, testing has not been sufficient. To address this, Professor Takayama created an optimal production system to monitor plant growth and regulate the environment using extremely low-cost methods.

"A scientific approach is sure to improve productivity," he says. "However, even if yields increase by 400% or 500% compared to a normal greenhouse, it is pointless without a business model. We need to look at the balance between cost and revenue while providing optimal production. To do this, we first need to measure conditions, analyze the resulting data, and determine optimal production. If we can do that, I believe we can realize a strong agricultural industry that can compete in the marketplace," says Takayama.

Measuring and diagnosing plant photosynthesis, controlling the cultivation environment

Addressing this, Professor Takayama has adopted image-based measurements and analyses to understand plant growth conditions. He goes on to explains the background behind his methods.

"CCD cameras that would have cost 1,000,000 yen 10 years ago have become cheaper, and cameras with the same functions are now equipped on smartphones. It is now even possible to analyze images with smartphones. Due to this, image analysis technology can now be brought on site to the farm," he says.

Particularly revolutionary is his research that uses images to capture the plant's photosynthetic activity. In a world first, Takayama developed a robot that can automatically measure photosynthesis in indoor farms. In 2017, Iseki & Co., Ltd. began selling the robot as a diagnostic device for plant cultivation. Indoor farms are broadly divided into artificially lit facilities that use LEDs and other artificial light sources, and solar-powered varieties. Professor Takayama focuses on the latter.

"The robot automatically moves throughout the solar-powered indoor farm at night and measures fluorescent imagery of tomato chlorophyll (Chl)," he explains. "Chlorophyll absorbs light energy, and the chlorophyll fluorescence is a portion of leftover energy that was not used in photosynthesis. It is discharged as red light.

To measure this, the leaves of the tomatoes are illuminated at night using a blue LED excitation light. Then, the robot stimulates the photosynthesis reaction system artificially and the change in the intensity of the chlorophyll fluorescence is measured. The process by which the chlorophyll fluorescence intensity changes with time is called an ‘induction phenomenon.' In 1987, my instructor, Kenji Omasa, Professor Emeritus of the University of Tokyo, became the first person in the world to take image measurements of this process. Changes in fluorescence intensity show the plant's photosynthetic capabilities and stress impacts, so it is helpful in understanding plant conditions."

Upon actually using the robot to measure a 170- by 76-meter greenhouse growing 30,000 tomato plants, it became clear that photosynthesis activity varied by location. Visualizing the analysis results through a heat map makes it possible to improve the state of growth by controlling various environmental conditions such as water volume and temperature.

Using a variety of new technologies to bring about innovations in agriculture

Professor Takayama is also working on a real-time monitoring system to measure photosynthesis. With this system, the cultivated plants are wrapped in tubular, transparent vinyl bags, and the rate of photosynthesis and transpiration is examined by measuring the concentration difference of H₂O and CO₂ in air flowing in from below the tube, and in the air ejected from a fan above. The low-priced sensor's ability to precisely measure photosynthesis and transpiration has garnered attention, and in 2019, the system went on sale as a joint collaboration between Kyowa Co., Ltd. Division of Hyponica and Plant Data, a venture launched by Toyohashi University of Technology. The system is getting lots of attention, with inquiries even coming from countries with advanced agricultural industries, such as Holland.

"Though increasing CO₂ concentration encourages photosynthesis, injecting CO₂ is not necessary if there is already sufficient photosynthesis occurring. This reduces cost." explains Professor Takayama.

In addition, a cheaper lightweight hanging robot that can be used in conventional small and mid-size greenhouses to measure plant biology imagery information is also being offered. In addition to measuring chlorophyll fluorescence imagery, moving the camera up and down makes it possible to capture color images of the entire plant and precise details. Using deep learning to analyze the acquired images makes it possible to automatically detect changes in growth along with flowering and fruit bearing conditions. This is helpful in predicting harvest time and yield. Work is also underway to develop a visual user interface that will make this information intuitive to understand.

"Deep learning is an incredibly groundbreaking technology. You can quickly analyze and develop growth models that would have previously taken a significant amount of time. Gathering large volumes of data from daily measurements makes it possible to further increase precision. Deep learning-equipped AI makes it possible to dramatically change conventional agriculture." says Takayama.

In addition, research and development is advancing to measure plant stress conditions by utilizing super-compact odor sensors equipped with ultra-sensitive silicon CMOS ion imaging sensors developed at the university by Professor Kazuaki Sawada.

"In fact, neighboring plants communicate with smell, and diagnosing these odors shows us the health of the plant. Though I learned this when studying abroad in Holland in 2007, at the time I could have never imagined that we would be able to develop small, low-cost sensors. We can use these odor sensors as alarms to show us when there is insufficient water or too much pesticide. Furthermore, we expect these simple sensors will be able to be used at production sites," says Takayama.

With abnormal weather patterns and disasters common in today's world, more and more people are looking for a reliably priced supply of vegetables that can be consumed with peace of mind. Professor Takayama's accomplishments put him at the forefront of such future innovations in agriculture.

Reporter's Note

Fond of biology and physics from a young age, upon arriving at university, Professor Takayama studied agricultural machinery and agricultural civil engineering. His field of study focused mainly on the durability of agricultural machinery, which was not a popular field at the time. However, while studying under his instructor, Takayama had the opportunity to measure chlorophyll fluorescence, and he then began to work to apply his research to the agriculture industry.

"At the time, machinery was very expensive, and the idea of applying my research to the agricultural field was dismissed as nonsense. But as the technology developed into the present day, consumer needs significantly changed, and the field became a real focus of attention. Even I was surprised," says Professor Takayama.

Smart agriculture and smart food chains are now the biggest topics for humanity, and are essential in achieving a sustainable society. Professor Takayama humbly dismisses his successes as being down to good timing, but perhaps it is more likely that his insights were key.