To achieve continuous and detailed plant monitoring, growers must combine data from a large number of optical and other technologies, which requires sensor and data fusion. One grower that is using such an approach is Milan-based Agricola Moderna, a 1500-square-meter vertical farm that produces and sells leafy greens for salads to supermarkets and restaurants in the region.
The company is at the forefront of efforts toward fully automized and AI-driven vertical farming. Its platform uses several types of sensors, such as RGB-D cameras for canopy 3D reconstruction and various environmental sensors that measure temperature, light intensity, and CO2 levels. The company uses hyperspectral imaging to monitor plants’ nitrogen, phosphorus, and potassium levels. The information obtained is sent to a central database to be analyzed.
When Agricola Moderna scans crops, it captures several gigabytes of hyperspectral data. The company develops algorithms in-house to manipulate and analyze the data. This analysis can be used to detect whether plants are healthy or sick. The hyperspectral data enables the company to speed up the analysis of plants and take corrective actions to optimize yield growth. The grower has also managed to quantify some plant species’ color brightness, which provides valuable information about the company’s products.
Spectral data enables the analysis of the phenotypic characteristics of plants. Multispectral and hyperspectral imaging is used to monitor plant diseases, insect pests, and invasive plant species, and to estimate crop yield and classify crop distributions.
Revealing the invisible
Hyperspectral imaging combines the advantages of conventional spectroscopy and imaging techniques, providing valuable information for vertical farming. It is particularly well suited for performing spectral analysis of crops based on the light they reflect.
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