As long as the corn leaves are observed, experienced farmers can determine what nutrient the corn lacks, but this is only the time when corn has grown and harvested.
An interdisciplinary team from the University of Sao Paulo developed a method for early assessment of corn nutritional status so that farmers can intervene in time to ensure the harvest and avoid losses.
The research project entitled "Application of Computer Vision in Plant Nutrition" was jointly conducted by the School of Physics of the University of Sao Paulo and the Department of Animal Husbandry and Food Engineering. This technique uses digital images of leaves combined with computer vision to determine which nutrient is lacking in early developmental stages of corn in a matter of minutes.
Researchers said that the technology uses artificial intelligence to identify the leaves of plant seedlings to determine whether plants lack nutrients such as nitrogen, phosphorus, magnesium, sulfur, potassium, copper, iron, zinc, and manganese. Plant grown leaves visually recorded their various nutritional deficiencies. In the early stages of plant growth, that is, one week or two weeks, this signal has already appeared, but it is not yet in the visible stage. The technology uses a scanner to interpret the digital image of the blade. After interpretation, the image is displayed as a mathematical model, and the software is compared with the pre-built model.
The researchers constructed a mathematical model of the normal nutritional status of the leaves, through the software, with these normal information, to create a new mathematical model, and compare this new model with the normal leaf model to confirm the nutritional deficiency.
When the plant matures, its nutritional deficiencies are discovered and it is too late. Severe nutritional deficiencies can lead to a 50% reduction in corn production. The technology can assess the nutritional status of corn when it grows for a week or two, and farmers can correct it for months. Experiments have shown that the technology has an accuracy of 87% and is already close to practical use. The research team is conducting field experiments and has applied for a patent. In the future, the technology will be applied to other crops for research.
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