## 3 Indices Related to Plant Productivity
Over the past few years, there has been growing interest among researchers in the practical application of indices related to plant productivity [140; 141; 142; 143; 144]. For instance, to support the assessment of the condition of potato crops, spectral data obtained from the Sentinel-2 satellite were used [73]. The research data covered farmland in the south of the Netherlands, which, after being obtained from the satellite, were compared with the results obtained from measurements with a manual radiometer. The analysis indicated that satellite data can be successfully used to determine parameters such as LAI, CCCI, and WDVI. A different study used data from the Sentinel-2 satellite to predict the yield of commercial potato tubers grown in Segovia, Spain. Eight different machine learning algorithms were applied in the research: Support vector machine radial, random forest,k-nearest neighbors, multivariate adaptive regression splines, model averaged neural network, quantile regression with LASSO penalty, and linear regression with backward selection. The following parameters were applied in the study: the anthocyanin reflectance index, which estimates the content of anthocyanins; the carotenoid reflectance index, which assesses the content of carotenoids; the inverted red-edge chlorophyll index, which determines the content of chlorophyll in the canopy; and the leaf chlorophyll content, which determines the concentration of chlorophyll in a leaf area unit. Additionally, the researchers used the NDVI; the plant senescence reflectance index (PSRI), reflecting the senescence of leaves; and the weighted difference vegetation index, which is a parameter similar to LAI. All measurements were recorded between the tuberization period and the ageing of the plants. Despite the lack of results related to weather and soil properties, researchers managed to create a model (support vector machine radial) that was able to forecast tuber yield several weeks before harvest. The mentioned model had an MAE of 8.64% and RMSE of 11.70% [132]. Leaf area index (LAI), chlorophyll content in leaves, and different nitrogen fertilization levels were used as input data to forecast winter wheat grain yield [145]. The wheat plantation was located in Belm, in the northwest of Germany, and was grown in the second half of October 2016, whereas LAI and SPAD measurements (later used to calculate the content of chlorophyll (\(\upmu\)g\(\cdot\)cm\({}^{-2}\))) were recorded on 20 June 2017. The MLR model was used in the research to estimate the yield size. The RMSE of the model was on average 4183 dt-ha\({}^{-1}\). In another case study, [PERSON] et al. [146] used, inter alia, the NDRE index to predict the yield of mango fruit. Plant productivity indices were measured in the early phase of fruition of trees. The study used a feed-forward model with backpropagation, and the covered orchards were located in Northern Australia. The combination of plant vegetation indices, including NDRE and tree crown area, allowed the authors to obtain an ANN model an R\({}^{2}\) of 0.7. The RMSE for the total fruit weight was 13.83 kg\(\cdot\)tree\({}^{-1}\). [PERSON] et al. [7] forecasted the yield of potato tubers, in Canada on Prince Edward Island and in New Brunswick. In the following studies, independent soil parameters were used as variables: electrical conductivity, organic matter content, volumetric moisture, soil pH, and cation-exchange capacity. The NDVI was also used in the study, which was measured at the end of July (60 days after planting potatoes), in mid-August (80 days after planting potatoes), and at the end of August, in each study year. By including soil data and the plant vegetation index, the authors obtained models with RMSEs ranging from 4.62 to 6.60 t-ha\({}^{-1}\). The NDVI was also used as a predictor in forecasting sugar cane yield in Brazil [142]. The ANN-based predictive model forecasted the yield three months before harvest, and the RRMSE did not exceed 8%. [PERSON] et al.