The Study on the Relationship between Normalized Difference Vegetation Index and Fractional Green Canopy Cover in Five Selected Crops

Abstract

Crop models are of great use and importance in modern agriculture. Most models imply spatial vegetation indices, such as NDVI, or canopy cover characteristics, such as FGCC, to provide estimation of crops conditions and forecast productivity. The purpose of the study was to (1) determine the possibility of mutual conversion between spatial NDVI and Canopeo-derived FGCC in five crops (grain corn, sunflower, tomato, millet, and winter wheat) and (2) estimate the precision of such a conversion. The data set of the study was formed by the OneSoil AI derived satellite imagery on NDVI for the studied crops in different stages of their growing season combined with Canopeo-processed photographs of vegetating crops in the field with FGCC percentage calculation. The sets of NDVI and FGCC values were paired up and then statistically processed to obtain polynomial equations of NDVI into FGCC and inverse conversion for each crop. The results of the study revealed that mutual conversion between spatial NDVI and Canopeo-derived FGCC is possible. There is a strong direct correlation (R2 within 0.6779–0.9000 depending on the crop) between the studied indices for all crops. Close-growing crops, especially winter wheat, showed the highest correlation, while row crops and especially tomatoes had a less strong relationship between vegetation indices. The models for mutual conversion between FGCC and NDVI could be incorporated into the yield simulation models to improve the forecasting capacities.

A simplified approach to satellite-based monitoring system of sugarcane plantation to manage yield decline at Wonji-Shoa Sugar Estate, central Ethiopia

“…In the current study, we investigated the relationships between FGCC determined by the Canopeo software and spectral VIs ( Table 7 ), where we found strong relationships. In a study conducted on other crops, NDVI was significantly correlated with FGCC [ [62] , [63] , [64] , 92 ], which is consistent with our findings. However, these studies have a limitation in that the researchers evaluated only NDVI.…”

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

“…Canopeo has been used as a high-throughput phenotyping tool for measuring biomass traits of sorghum cultivars [19] and shoot biomass of twenty lentil lines [20]. Canopeo's other novel applications include its usefulness as a tool for sensor-based yield prediction [21][22][23][24], investigating biomass partitioning [25], developing critical nitrogen dilution curves [26], making nitrogen rate recommendations [21,26], determining appropriate time to start grazing in tropical pasture management [27], and improving weed management programs [28]. However, Canopeo was not found useful for visually assessing symptoms of Verticillium wilt in potato [29].…”

The Productivity of Sareptsky Mustard Depends on the Sowing Rate and the Level of Biologization of the Crop Growing Technology

“…A typical feature of modern domestic agricultural production in all agrarian zones without exception, and in particular in the southern Steppe, is a significant imbalance of field agrocenoses, which is determined by the ever-increasing expansion of highly marginal crops in them, which, unfortunately, are quite infrequently defined at the same time as economic attractiveness, so and ecological tolerance regarding the preservation and increase of existing biodiversity [Blyshchyk et al, 1998;Zhuykov and Zhuykova, 2006; in domestic crop rotations in a certain way, is to relieve the acuteness of the problem, which is increasingly mentioned by domestic scientists, namely the shortage of satisfactory and optimal precursors for winter wheat, to expand the nomenclature of such crops of field agrocenoses, which at the same time with high indicators of economic efficiency of production would have soil-improving properties and high technology of the cultivation process, we see the involvement of Sarepta mustard in field crop rotations [Borisonik, 1988;Lykhovyd et al, 2022;Zhuikov et al, 2022].…”