Abstract
Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm−2), leaf area index (RMSE = 0.67 m2·m−2), canopy chlorophyll (RMSE = 0.24 g·m−2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm−2, 0.85 m2·m−2, 0.28 g·m−2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system.
Highlights
In-season monitoring of crop development can assist management practices in different aspects, allowing farmers to optimize agricultural operations according to site-specific characteristics [1].Information obtained in previous seasons can be considered for planning the cultivation of subsequent crops, taking into account the spatiotemporal variability of yield and of different factors related to the production [1,2]
This continuous decrease in leaf chlorophyll content is observed for leaf nitrogen in potato and it is associated to biomass accumulation in shoots and tubers during the crop growth [71]
Models derived from measurements of the different sensors resulted in comparable prediction accuracies (Section 3.2, Tables 3 and 4), and intercomparison of vegetation indices calculated from Unmanned Aerial Vehicle (UAV) and ground-based data indicated that integration of the sensors streams is possible in some cases (Section 3.3, Figure 11), differences could be observed between sensing solutions, especially concerning the first and second data acquisitions (43 and 62 DAP)
Summary
In-season monitoring of crop development can assist management practices in different aspects, allowing farmers to optimize agricultural operations according to site-specific characteristics [1].Information obtained in previous seasons can be considered for planning the cultivation of subsequent crops, taking into account the spatiotemporal variability of yield and of different factors related to the production [1,2]. High-throughput approaches for field-based phenotyping can assist plant breeding in order to identify genotypes with better performance under diverse abiotic and biotic constraints, under realistic production conditions [3]. In these cases, proximal and remote sensing offers a multitude of techniques that can be applied to estimate crop traits over time in non-destructive and spatially explicit ways. The estimation of leaf pigments content and canopy structural traits through optical remote sensing, usually with measurements of reflectance in spectral bands between 450 and 900 nm (i.e., from visible to near-infrared), is performed using approaches that can be broadly divided in three categories [6]: empirical-statistical (i.e., through parametrical or non-parametrical regressions), physical-based and hybrid. In order to mitigate these limitations, RTMs can be inverted using non-parametric regressions trained upon simulated data originating hybrid methods, which give a simplified inversion alternative to physical approaches
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