New technology can evaluate the early nutritional status of Maize

As long as we observe the leaves of corn, experienced farmers can determine which nutrition corn lacks, but this is only when the overall situation of corn growth and harvest yield has been determined According to local media, an interdisciplinary research team from the University of Sao Paulo has developed a method that can assess the nutritional status of corn early, so that farmers can intervene in time to ensure the harvest and avoid losses The research project, entitled "the application of computer vision in plant nutrition", is jointly carried out by the school of physics of Sao Paulo University and the Department of animal husbandry and food engineering The technology uses digital images of leaves combined with computer vision to determine in a matter of minutes which nutrients corn lacks in its early stages of development Researchers said the technology uses artificial intelligence to identify the leaves of plant seedlings to determine whether plants lack micronutrients such as nitrogen, phosphorus, magnesium, sulfur, potassium, copper, iron, zinc and manganese The growing leaves of plants record their nutritional deficiencies visually At the early stage of plant growth, i.e during one or two weeks, this signal has appeared, but it is not in the visible stage This technology uses a scanner to interpret the digital image of the blade After the interpretation, the image is displayed as a mathematical model, and the software is used to compare with the pre built model Researchers have built a mathematical model of leaves with normal nutritional status Through the software, with these normal information, they have made a new mathematical model, and compared the new model with the normal leaf model to confirm the nutritional deficiency It's too late to find the nutritional deficiency when the plant is mature Serious nutritional deficiency can lead to a 50% reduction in corn production The technology can evaluate the nutritional status of corn in one or two weeks, and farmers can have several months to correct it The experiment shows that the technology has 87% accuracy and is close to practical application The research team is conducting field experiments and has applied for patents, and will apply the technology to other crops for research in the future.