Satellite imagery contains valuable large-scale information for precision farming. However, the low-resolution of satellite images can make it challenging to extract crop status information due to mixed pixels, in particular within multi-species crop stands like grass-clover for silage. In contrast, proximal high-resolution images with centimeter to sub-millimeter scale contain cures for single crop species pixels. However, these are often sparsely sampled due to computational limitations. In this paper, we present a preliminary attempt to enrich multispectral satellite images with crop stand population intelligence extracted from sparsely proximal RGB samples. The system attempts to reinforce satellite imagery based on proximal indicators lowering the risk of faulty interpretation knowledge base for future farm management information system (FMIS). A semantic segmentation algorithm is utilized to find the ratio of grass, clover, and soil across proximal images. Sentinel-2 as satellite imagery is employed as the 10-meter ground sampling distance input of the system and the grass, clover, and soil ratios are the output gained simultaneously. The system includes 1) a method where the proximal images and satellite imagery are preprocessed and then aligned with each other; and 2) a non-linear Multi-Layer Perceptron (MLP) extracting grass, clover, and soil ratio. Estimation results present promising correlation between clover, grass, soil, and Sentinel-2. Although, more data with higher diversity of clover-grass mixture is required to confirm the distinction of clover and grass.
This paper shows the potential of using robotics for data acquisition within full-scale field trials. Robotics ensured simultaneously measurements from several sensors from GPS targeted sampling points. This was demonstrated by supporting a project developing methods to measure gap fraction and canopy structure in cereals. The project required measurements from ordinary barley canopy areas using a high- dynamic-range RGB camera, and a multi-spectral Cropscan radiometer. Further, the RGB camera required images from 12 different angles relative to the rows.
In fulfilling these demands an existing robotic platform at Research Center Bygholm, Denmark, was enhanced. A payload software system was developed ensuring a simple and efficient interface between the robotic platform and the multiple sensor systems. The software of the Cropscan Multispectral Radiometer System was also altered to support
remote control by the payload software. The robotic system collected data from a full scale field at two occasions.
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