Farmers could monitor their crops more precisely with a hyperspectral handheld sensor that is claimed to provide up-to-the-minute data on plant physiology.
The device, developed by Jian Jin, an assistant professor in Purdue University’s Department of Agricultural and Biological Engineering, scans a plant for features including moisture, nutrient and chlorophyll levels. It can also determine the effects of chemical spraying and identify symptoms of disease.
Jin believes the device will help farmers detect changes in plant health in the field hours to days before they are visible to the naked eye. It will also provide information that will give farmers the option to use fewer resources, such as fertiliser and water.
“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,” Jin said. “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.”
The handheld sensor, which can reportedly scan a plant in under five seconds, can detect hundreds of bands of colour in each pixel compared with the three bands of colour detected by traditional cameras. One version also fires fluorescent light off the plant. Both are used to measure stress and nutrition levels of the plant.
“We implemented both the hardware and software technologies into a handheld device that is light and easy to carry,” Jin said.
The sensor integrates the advanced image processing algorithm and plant features prediction models developed by Purdue scientists. These models were developed with Purdue’s database containing years of plant research assays in both greenhouse and field. The models are also constantly improved and updated.
“So we always have the most accurate predictions for the farmer,” Jin said.
According to Purdue, there has been a rapid development of plant phenotyping in the past decade as 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 manually check plant health, which lacks precision and efficiency.
Jin said his sensor is more precise than current devices used by plant scientists that clamp down on a 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,” Jin said. “It’s also constantly getting better because we scan plants every day and are upgrading both hardware and software technologies.”
Although the handheld sensor is self-contained, users have the option to upload the measurements with geo-location to a web-based cloud map service developed by Carol Song and her team at Purdue’s Advanced Computing Group.
The system is said to generate plant stress and nutrition heat maps based on the sensor measurements and provides interactive ag data querying functions at farm and regional levels.
“If we can successfully distribute the sensors around the region, 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,” he said.
Jin’s group at ABE is working on automation of this device. He and his graduate students worked last winter with a group from Purdue’s School of Mechanical Engineering and implemented a robot to scan the leaves with the sensor automatically in the greenhouse.
The robot’s machine vision was able to recognise the target leaves and carry the sensor to them for a quick scan operation along the leaf’s natural slope. Encouraged by the success in the greenhouse, Jin and his team is moving on for the design of the next robot in the farm field environment.
The robot system travels between crop rows, with each leg equipped with a sensor, waving and scanning leaves in the field with a very high speed. Jin expects the prototype to be functioning during the 2019 growing season.
Jin is looking for collaborators who could help commercialise the device. The Purdue Office of Technology Commercialization has filed three applications for provisional patents for the technology.