11-08-2021 | | By Sam Brown
Researchers from the US have recently developed a non-invasive wearable patch that enables farmers to determine whether crops are diseased or stressed. What challenges does crop monitoring face, what have the researchers developed, and why is such monitoring key to future farming success?
Farming is arguably the oldest industry in the history of humanity, and the importance of farming cannot be understated. Without farming, there is no stable supply of food. Without the stable supply of healthy crops, humans would spend most of their winters hunting for whatever game they can find, and without a stable food supply, there is no time to think about the wonders of the universe. Simply put, farming has enabled modern technology as plentiful amounts of food give us time to do nothing, which leads to boredom and deep thought and pondering.
Nevertheless, farming is a tricky business, and no matter how advanced human technology seems to be, farmers are always finding problems, whether it is unstable weather, inability to find suitable fertile land or damage from pests and disease. While it is possible to mitigate drought using aqueducts, and unfertile land can be made fertile through crop rotation or fertiliser, fighting plant diseases is notoriously trick and invasive.
When growing crops, farmers must be vigilant at spotting plants that may be diseased as such diseases can spread across an entire field of crops. However, some diseases can be hard to identify without physically pulling a plant out of the ground and testing various plant parts. Furthermore, if such a plant was already perfectly healthy, returning the plant to the ground in the same condition after testing would be almost impossible. Plants carrying the disease may look healthy for a time, but only plants that show physical signs of disease will be reliably identified.
Recently, North Carolina State University researchers have developed a wearable sensor that can detect if a plant is diseased or if the plant is stressed. Since tissue samples can kill plants, the researchers looked for alternative methods to indicate disease or stress.
The solution to the invasive sample problems was to utilise volatile organic sensors as the researchers discovered that plants emit volatile organic compounds when either diseased or under stress from physical damage. Thus, the researchers created a VOC sensor using graphene and flexible silver nanowires coated with chemical ligands (the choice of ligands determines which VOC the sensor responds to).
To demonstrate the effectiveness of the sensor, the researchers applied a 30mm long sensor to a tomato leaf in the late stages of blight. After three hours, the sensor began detecting the volatile compounds due to blight, demonstrating that such sensors could provide farmers with vital information before diseases spread.
Plant disease is tough to combat, and unlike other living creatures, plants cannot simply get sprayed with antibiotics or antifungals (such practices are heavily looked down on). The use of non-invasive sensors combined with IIoT technologies could enable farmers to monitor their crops in real-time remotely.
The first few plants that trigger a detection could easily be isolated and removed, with a further area around those crops destroyed if the disease has begun to spread. Such action not only helps improve crop yield but prevents the spread of disease to other farmers lands. Furthermore, being able to detect diseases in the early stages also significantly helps farmers control disease spread.
Farmers who can remotely monitor crops can spend more time managing other tasks such as maximising crop yield, monitoring environmental damage, and identifying other challenges their crops may face. Such sensors could also dramatically help farmers in developing countries where disease identification and control may be more challenging.
The next stage for the researchers is to create a sensor that operates in all weather and can easily interface with IoT devices. Furthermore, their sensors will need to be tuned to identify diseases much earlier, ideally before the visual detection stage.