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Handheld agtech sensor measures plant health, collects crop data

Handheld agtech sensor measures plant health, collects crop data

Technology News |
By Rich Pell



Designed to help farmers detect changes in plant health in the field hours to days before they are visible to the naked eye, the hyperspectral-imaging device works by scanning a plant for physiological features – such as moisture, nutrient, and chlorophyll levels – as well as different chemical spraying effects and disease symptoms. It also will allow farmers to make necessary changes to grow more food using fewer resources, such as by reducing fertilizer and water use, say the researchers.

“My vision is this sensor will allow household farmers walking through a field to use a handheld device and a smartphone to get the same information available from very expensive phenotyping systems constructed by big companies and big universities in recent years,” says Jian Jin, an assistant professor in Purdue’s Department of Agricultural and Biological Engineering. “We have 600 million farmers worldwide, and very few of them are benefiting from high-end plant sensor technologies. Now, with this handheld device, most farmers can benefit.”

Designed to be light and easy to carry, the handheld sensor can scan a plant in less than five seconds and can detect hundreds of bands of color in each pixel compared with the three bands of color detected by traditional cameras. One version of the device also shoots a burst of fluorescent light off the plant. Both are used to measure stress and nutrition levels of the plant.

The device integrates an advanced image processing algorithm and plant features prediction models developed by Purdue scientists using the University’s database containing years of plant research assays in both greenhouse and field. The models are also constantly improved and updated.

Plant phenotyping – a quantitative description of a plant’s anatomical, ontogenetical, physiological and biochemical properties – has seen rapid development in recent years, say the researchers, as imaging technology is increasingly being used to improve efficiency based on current conditions instead of farmers relying on regional conditions and historical data to make decisions. Most farms check plant health manually, which lacks precision and efficiency. And current devices used by plant scientists clamp down on a single leaf and measure the health of only a portion of the plant.

“Due to multiple technical reasons, the sensor’s prediction quality is much more accurate than any other types of crop imaging sensors that people have in the existing market,” says Jin. “It’s also constantly getting better because we scan plants every day and are upgrading both hardware and software technologies.”

Users of the sensor have the option to upload measurements with geo-locations to a web-based cloud map service developed by Purdue’s Advanced Computing Group. The system generates plant stress and nutrition heat maps based on the sensor measurements, and provides interactive ag data querying functions at both farm and regional levels.

This digital ag map system with sensor data can support many potential applications, say the researchers. For example the data collected will provide valuable information to state and federal officials about steps they can take to help farmers during severe crop stress periods as well as information about what types of crop yields can be expected.

“If we can successfully distribute the sensors around the region,” says Jin, “we can generate this digital ag map service to monitor the plant growth all over the region — which areas are under stress and which areas are having a good performance.”

Looking ahead, the researchers are working on automation of the device. So far they say they have successfully implemented a robot to scan the leaves with the sensor automatically in a greenhouse.

The robot used machine vision to recognize the target leaves and carry the sensor over there for a quick scan operation along the leaf’s natural slope. Next, say the researchers, they are moving on to the design of the next robot in the farm field environment, with a functioning prototype expected during the 2019 growing season.

Meanwhile, the researchers are looking for collaborators who could lead in commercializing the device. Jin says he believes making the devices low in cost might be the best approach, with the data being where the value is. The Purdue Office of Technology Commercialization has filed three applications for provisional patents for the technology.

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Qualcomm invests in digital farming
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