Modern devices integrated withsmartphones penetrate into all spheres of human activity. They came smart gadgets and in agriculture. Development of scientists from the American University of North Carolina will allow agronomists and gardeners to quickly diagnose plant diseases in a matter of minutes in a matter of minutes.
Currently to determine the diseaseplants must be sampled and transported to the laboratory. The analysis also takes quite a long time, sometimes up to several hours. Therefore, the total time required to determine the disease is stretched for days or weeks. During this time, the disease can affect many plants and spread to large agricultural areas.
Scientists from North Carolina have developeda methodology for diagnosing a disease by analyzing volatile organic compounds (VOCs) secreted by the foliage of a plant. The device works in conjunction with a smartphone.
Every disease of the plant leads to changesthe chemical composition of VOCs produced by foliage. The analysis allows to uniquely identify each specific disease according to the composition of VOCs. The compact device allows you to analyze quickly and directly on the agricultural plot.
The analysis methodology is fairly simple. A freshly picked leaf of a diseased plant is placed in a sealed empty tube. After 15 minutes, necessary to saturate the air inside the tube produced by a sheet of VOC, air is drawn from the tube using a thin plastic tube.
The second end of the tube goes into the camera with a paperindicator, with special reagents that change color depending on the chemical composition of the air. Comparing the color of the indicative paper strip with the control images allows you to accurately determine the disease of the plant.
The composition of the reagents is based on organicdyes in half with special reagents based on gold nanoparticles. The device accurately diagnoses the disease even with a very low concentration of substances contained in the VOC. So Phytophthora infestans, causing late blight in tomato leaves, was diagnosed with an accuracy of 95%.
In the next stage of system development, scientistswant to automate the process of comparing the color of the indicator with control samples. A special application is being developed for this. In addition, several series of indicators are being developed to recognize a wider spectrum of diseases.