YIELD ESTIMATION OF SUNFLOWER PLANT WITH CNN AND ANN USING SENTINEL-2

Abstract

Abstract. Due to food security and agricultural land management, it is crucial for decision makers and farmers to predict crop yields. In remote sensing based agricultural studies, spectral resolutions of satellite images, as well as temporal and spatial resolution, are important. In this study, we investigated whether there is a relationship between the Normalized Different Vegetation Index (NDVI) and Normalized Different Vegetation Index Red-edge (NDVIred) indices derived from the Sentinel-2 satellite. In addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation. In this context, yield data of 48 sunflower parcels were obtained in 2018. The obtained results showed that both NDVI and NDVIred can be used to estimate the yield of sunflowers. The best results were obtained from the combination of the NDVI and the CNN technique with the RMSE equal to 20,874 Kg/da on 30 June 2018. Concerning the results, although there is not much superiority between the two indices, the best results were generally obtained from CNN as the method.

Plant phenotyping relevance

Sentinel-2画像からNDVI等を抽出し、CNN・ANN・回帰によってヒマワリの圃場収量を推定・比較しており、植物形質の取得・推定手法が中心である。

Code and data availability

The paper reports sunflower yield estimation from Sentinel-2 NDVI/NDVIred using linear regression, ANN, and CNN, but provides no public deposit of its 48-parcel yield data, Sentinel-2 imagery subsets, or author analysis code. The only code reference ('CNN Code') is a third-party tutorial, not the authors' code; the ESA

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