Prediction Of Grain Yield Research Articles

Impacts of climate change on spatial wheat yield and nutritional values using hybrid machine learning

Abstract Wheat’s nutritional value is critical for human nutrition and food security. However, more attention is needed, particularly regarding the content and concentration of iron (Fe) and zinc (Zn), especially in the context of climate change (CC) impacts. To address this, various controlled field experiments were conducted, involving the cultivation of three wheat cultivars over three growing seasons at multiple locations with different soil and climate conditions under varying Fe and Zn treatments. The yield and yield attributes, including nutritional values such as nitrogen (N), Fe and Zn, from these experiments were integrated with national yield statistics from other locations to train and test different machine learning (ML) algorithms. Automated ML leveraging a large number of models, outperformed traditional ML models, enabling the training and testing of numerous models, and achieving robust predictions of grain yield (GY) (R 2 > 0.78), N (R 2 > 0.75), Fe (R 2 > 0.71) and Zn (R 2 > 0.71) through a stacked ensemble of all models. The ensemble model predicted GY, N, Fe, and Zn at spatial explicit in the mid-century (2020–2050) using three Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, and MRI-ESM2-0 under two shared socioeconomic pathways (SSPs) specifically SSP2-45 and SSP5-85, from the downscaled NEX-GDDP-CMIP6. Averaged across different GCMs and SSPs, CC is projected to increase wheat yield by 4.5%, and protein concentration by 0.8% with high variability. However, it is expected to decrease Fe concentration by 5.5%, and Zn concentration by 4.5% in the mid-century (2020–2050) relative to the historical period (1980–2010). Positive impacts of CC on wheat yield encountered by negative impacts on nutritional concentrations, further exacerbating challenges related to food security and nutrition.

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

Combining canopy spectral reflectance and RGB images to estimate leaf chlorophyll content and grain yield in rice

Predicting rice grain yield using multi-source remotely sensed data is crucial for improving prediction accuracy, optimizing nitrogen management, and advancing precision agricultural development. However, the feasibility and reliability of using multi-source remotely sensed data to predict the grain yield remain unclear. Therefore, this study aimed to explore the possibility of providing rice leaf chlorophyll content (LCC) estimations and predictions of the grain yield using multi-source remotely sensed data. Two rice field experiments were conducted with various rice cultivars and nitrogen rates, and a field spectrometer and an unmanned aerial vehicle (UAV) equipped with a digital camera were employed to acquire the spectral reflectance and red–green–blue (RGB) images at the tillering, jointing, and full-heading stages. Destructive sampling was then conducted to measure the LCC and grain yield. The LCC was used as a bridge to develop remotely sensed prediction models for the grain yield. First, the linear relationship between grain yield and the LCC was determined at the tillering, jointing, and full-heading stages. A multiple linear regression (MLR) model was then developed to predict grain yield using multi-temporal LCCs at three growth stages. Second, multiple stepwise regression, support vector regression, and back propagation neural network were used to evaluate the estimation performance of spectral reflectance, RGB image data, and their combination for LCC. Third, the most accurate LCC estimation model was selected and coupled with the linear and MLR models of grain yield. The results showed that grain yield was significantly and positively related to the LCC at the tillering, jointing, and full-heading stages, and that the MLR model of grain yield achieved the best estimation accuracy using multi-temporal LCCs. The fusion models established by combining spectral reflectance and RGB image data improved LCC estimation accuracies. Using multi-growth stages, the most accurate predictions of grain yield were obtained from LCC estimation models (R2 = 0.698, RMSE = 0.742 t ha−1, rRMSE = 9.004 %) compared to those using single growth stages. Our study concluded that multi-source remotely sensed data fused from ground-based spectral reference and UAV-based RGB images can better predict and explain the grain yield for both single and multi-growth stages. This study provides a novel method of estimating the crop chlorophyll content and grain yield.

Large‐scale breeding applications of unoccupied aircraft systems enabled genomic prediction

AbstractBreeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Prediction of Grain Yield and Gluten Content in Winter Bread Wheat Based on Nutrient Content in Plant Parts during the Critical Cereal Window

Reliable prediction of winter bread wheat grain yield (GY) and its qualitative parameters (crude protein (CP) and wet gluten (GL) content, wet gluten yield (GLY)) requires evaluation of the plant nutritional status in the Critical Cereal Window (CCW). The reliability of the forecast depends on the dedicated plant characteristics and the correct selection of the diagnostic plant parts. This hypothesis was verified in a one-factor field experiment carried out in the 2013/2014, 2014/2015, and 2015/2016 growing seasons. The field experiment included applying 0, 40, 80, 120, 160, 200, and 240 kg N ha−1. The N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu content in wheat was determined in two growth stages: (i) beginning of booting (BBCH 40) and (ii) full flowering (BBCH 65). The evaluated plant components included the leaves and stem for BBCH 40 and the flag leaf, leaves, stem, and ear of BBCH 65. Grain yields were very high, significantly responding to the increased rates of fertilizer nitrogen (Nf), with a maximum yield of 11.3 t ha−1 achieved in 2014 (N rate of 209 kg N ha−1), 13.7 t ha−1 in 2015, and 8.6 t ha−1 in 2016 (N rate of 240 kg N ha−1). The CP and GL content also increased linearly in accordance with the Nf rates. At the beginning of the booting stage, the GY forecast based on the content of nutrients in the leaves or the stem was 94%. Meanwhile, a slightly higher yield prediction was obtained for leaves during the full flowering stage (95%). The key nutrients comprised K, Ca, and Mn, accounting for 93% of the GY variability. The accuracy of the GL prognosis at BBCH 40, regardless of the plant part, exceeded 99%. Three nutrients, namely, P, Mg, and Zn, explained 98% of the GL variability, and the GLY forecast was high (97%). Both wheat traits depended on Zn, which buffered the action of N and Mg. At the full flowering stage, the highest, yet slightly weaker, predictions of GL and GLY were obtained for leaves (95% and 92%, respectively). At this stage of winter wheat growth, the significant role of Zn and K and the buffering effect of Cu on the action of both nutrients was apparent. The obtained results unequivocally confirm that the game for winter wheat grain yield occurs within the Critical Cereal Window. In addition, the end result depends on the plant’s N supply during this period and the nutritional status of other nutrients. Application of 40–80 kg N ha−1 fertilizer critically impacted the GY and technological quality. Moreover, micronutrients, including Zn and Cu, influence the GY, GL, and GLY considerably. At the beginning of the booting phase (BBCH 40), winter wheat leaves serve as a highly reliable plant component indicator for evaluating nutrient content and quantitative (GY, GLY) and qualitative (GL) characteristics of grain. Moreover, analysis conducted during BBCH 40 allows the farmer to correct the nutritional status of the wheat, taking into account N and other nutrients as necessary.

Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses

Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research.

Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset

The prediction of grain yield is important for sowing, cultivar positioning, crop management, and public policy. This study aims to predict maize productivity by applying an artificial neural network and by building models of multilayer perceptrons (MLPs) using public data and maize experimental networks. The dataset included parameters of climate, soil water balance, and agronomic characteristics from maize hybrids of an experimental network of two agricultural years. The climatic and soil balance water parameters were divided according to the maize plant development stages. Six databases were obtained by combining the imputation of missing data with the agronomic characteristics of the maize hybrids, the climatic parameters/soil water balance, and the complete database with both. Hyper parameterization of the models was obtained using GridSearch and k-fold cross-validation. The models with imputation were more accurate than those without it. The model with climate data/soil water balance and the complete model with imputation presented the smallest errors of 71 kg ha−1. In all the models, cultivars, locations, and their interactions were important, and different climatic conditions had the greatest weight in predicting productivity. It was concluded that the MLP models performed adequately and captured the non-linear effects of the interaction between the environment and maize hybrids. Climatic and soil balance water parameters at different stages of maize plant development explain the productivity of maize hybrids more than the agronomic characteristics of the cultivars.

Genomics combined with UAS data enhances prediction of grain yield in winter wheat.

With the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best accuracy of grain yield when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. In general, the predictions with only phenotypic information were very competitive to using both sources of information, and in many cases using only phenotypic information provided the best accuracy. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating high quality phenotypic inputs in the models.

Effects of drought stress on water content and biomass distribution in summer maize(Zea mays L.).

The resource allocation of different component organs of crops under drought stress is a strategy for the coordinated growth of crops, which also reflects the adaptability of crops to drought condition. In this study, maize variety namely 'Denghai 618', under the ventilation shed, two treatment groups of light drought (LD) and moderate drought (MD), and the same rehydration after drought are set, as well as the normal water supply for control in shed (CS). The drought experiment was conducted in the jointing-tasseling stage in 2021. The effects of different drought stress on the water content and biomass allocation of each component organ were analyzed. The results showed that (1) during the drought period, the water content of each component organ of summer maize decreased in general, but the Water content distribution ratio (WCDR) of the root increased by 1.83%- 2.35%. The WCDR of stem increased by 0.52%- 1.40%. (2) Under different drought treatments, the root biomass (RB) increased 33.94% ~ 46.09%, and fruit biomass (FB) increased 1.46% ~ 2.49%, the leaf biomass (LB) decreased by 8.2% and 1.46% respectively under LD and MD. (3) The allometric growth model constructed under sufficient water is not suitable for drought stress; the allometric exponent α under drought stress is lower than that of the CS: CS (α=1.175) > MD (α = 1.136) > LD (α = 1.048), which also indicates that the impact of existing climate change on grain yield may be underestimated. This study is helpful to understand the adaptive strategies of the coordinated growth of maize component organs under drought stress and provide a reference for the prediction of grain yield under climate change.

Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.

Medium-Resolution Mapping of Evapotranspiration at the Catchment Scale Based on Thermal Infrared MODIS Data and ERA-Interim Reanalysis over North Africa

Accurate quantification of evapotranspiration (ET) at the watershed scale remains an important research challenge for managing water resources in arid and semiarid areas. In this study, daily latent heat flux (LE) maps at the kilometer scale were derived from the two-source energy budget (TSEB) model fed by the MODIS leaf area index (LAI), land surface temperature (LST) products, and meteorological data from ERA-Interim reanalysis from 2001 to 2015 on the Tensift catchment (center of Morocco). As a preliminary step, both ERA-Interim and predicted LE at the time of the satellite overpass are evaluated in comparison to a large database of in situ meteorological measurements and eddy covariance (EC) observations, respectively. ERA-Interim compared reasonably well to in situ measurements, but a positive bias on air temperature was highlighted because meteorological stations used for the evaluation were mainly installed on irrigated fields while the grid point of ERA-Interim is representative of larger areas including bare (and hot) soil. Likewise, the predicted LE was in good agreement with the EC measurements gathered on the main crops of the region during 15 agricultural seasons with a correlation coefficient r = 0.70 and a reasonable bias of 30 W/m2. After extrapolating the instantaneous LE estimates to ET daily values, monthly ET was then assessed in comparison to monthly irrigation water amounts provided by the local agricultural office added to CRU precipitation dataset with a reasonable agreement; the relative error was more than 89% but the correlation coefficient r reached 0.80. Seasonal and interannual evapotranspiration was analyzed in relation to local climate and land use. Lastly, the potential use for improving the early prediction of grain yield, as well as detecting newly irrigated areas for arboriculture, is also discussed. The proposed method provides a relatively simple way for obtaining spatially distributed daily estimates of ET at the watershed scale, especially for not ungauged catchments.

UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat.

Early prediction of grain yield helps scientists to make better breeding decisions for wheat. Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based multi-sensor data can improve the prediction accuracy of crop yield. For this, five ML algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat. A set of thirty wheat cultivars and breeding lines were grown under three irrigation treatments i.e., light, moderate and high irrigation treatments to evaluate the yield prediction capabilities of a low-cost multi-sensor (RGB, multi-spectral and thermal infrared) UAV platform. Multi-sensor data fusion-based yield prediction showed higher accuracy compared to individual-sensor data in each ML model. The coefficient of determination (R2) values for Cubist, SVM, DNN and RR models regarding grain yield prediction were observed from 0.527 to 0.670. Moreover, the results of ensemble learning through integrating the above models illustrated further increase in accuracy. The predictions of ensemble learning showed high R2 values up to 0.692, which was higher as compared to individual ML models across the multi-sensor data. Root mean square error (RMSE), residual prediction deviation (RPD) and ratio of prediction performance to inter-quartile range (RPIQ) were calculated to be 0.916 t ha−1, 1.771 and 2.602, respectively. The results proved that low altitude UAV-based multi-sensor data can be used for early grain yield prediction using data fusion and an ensemble learning framework with high accuracy. This high-throughput phenotyping approach is valuable for improving the efficiency of selection in large breeding activities.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11119-022-09938-8.

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G×E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.

Prediction of grain yield and nitrogen uptake by basmati rice through in-season proximal sensing with a canopy reflectance sensor

Prediction of grain yield and nitrogen uptake by basmati rice through in-season proximal sensing with a canopy reflectance sensor

Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model

BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.

Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression

Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

Evaluation of DSSAT model (CERES rice) on rice production: A review

Rice is the staple food for over half of the world population. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice model in different regions of the world in relation to their potential application towards increasing resource use efficiency and yield of rice. In this article, the CERES-Rice model evaluation by using the simulated and observed values on crop phenology (anthesis, physiological maturity) and final grain yield mainly over Asian countries by different authors has been compiled and described. Mainly the model was evaluated based on different statistical measures such as RMSE and D-index. Several datasets for the prediction of grain yield and phenological period across different parts of Asia were examined. This particular model predicted those with high-accuracy (nRMSE1-5% for anthesis and 1-4% for physiological maturity days). For various data sets for grain yield, the nRMSE varied between 0.05–5.00 percent with error percentage of 2-5%. The model sometimes over-estimated or under-estimated the values of grain yield, especially under water stress conditions.

Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery

Throughout the plant development, the physiological processes of growth, as well as the handling of water and fertilizers represent sources of variation in the spectral response of the crops. Thus, in the context of precision agriculture, information on the phenological stage of cultivation can be decisive in accurately estimating agronomic variables through the use of aerial images obtained by RPAS (Remotely Piloted Aircraft System) platforms. Therefore, the aim of this study was: i) to investigate the potential of using RPAS embedded with multispectral sensors in order to obtain data on a soybean irrigated field with a central pivot system ii) to identify the best stage of development of irrigated soybean in order to obtain multispectral images aiming to the yield prediction; iii) to test different vegetation indices (VIs) for estimating the yield of soybean crop using artificial neural networks. The treatments were established using different metric water potentials in the soil and the analysis was comprised by 30 sample units. Multispectral images were acquired through a Sequoia® camera aboard the Phantom 4® Pro platform, during seven phenological crop stages. In addition to the spectral bands, nine VIs were taken as predictors based on the response variables derived from the site survey. The selection of the best growth stage was based on the highest level of association, represented by the Spearman correlation coefficient between predictors and each response variable. The Multi-Layer Perceptron (MLP) algorithm was used to adjust the predictive model, whose performance was assessed in terms of training and testing. To have independent measurements of the performance of the proposed algorithm was used by k-fold cross-validation. The results obtained from the correlation between the image-derived data and the soybean yield indicate that the ideal monitoring window for irrigated soybean can be found at the end of the vegetative stage (V6). The selection of the phenological crop stage had a positive impact on the predictions. The MLP models showed a good adjustment and good generalization capability. In turn, grain yield was the most complex agronomic parameter for modeling, with correlations of 0.70 and 0.92 for training/testing, and excellent results in other statistical tests, reinforce the great combining capacity between remote sensing via RPAS and machine learning (ML) in applications aimed at precision agriculture. This approach offers a useful tool for evaluating grain yield in center-pivot-irrigated soybean crops and emphasizes the need of observation regarding the phenological crop stage as a factor of analysis in the prediction of grain yield via remote sensing.