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Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.

With abundant available genomic data, genomic selection has become routine in many plant breeding programs. Multispectral data captured by UAVs showed potential for grain yield (GY) prediction in many plant species using machine learning; however, the possibilities of utilizing this data to augment genomic prediction models still need to be explored. We collected high-throughput phenotyping (HTP) multispectral data in a genotyped multi-environment large-scale field trial using two cost-effective cameras to fill this gap. We tested back to back the prediction ability of GY prediction models, including genomic (G matrix), multispectral-derived (M matrix), and environmental (E matrix) relationships using best linear unbiased predictor (BLUP) methodology in single and multi-environment scenarios. We discovered that M allows for GY prediction comparable to the G matrix and that models using both G and M matrices show superior accuracies and errors compared with G or M alone, both in single and multi-environment scenarios. We showed that the M matrix is not entirely environment-specific, and the genotypic relationships become more robust with more data capture sessions over the season. We discovered that the optimal time for data capture occurs during grain filling and that camera bands with the highest heritability are important for GY prediction using the M matrix. We showcased that GY prediction can be performed using only an RGB camera, and even a single data capture session can yield valuable data for GY prediction. This study contributes to a better understanding of multispectral data and its relationships. It provides a flexible framework for improving GS protocols without significant investments or software customization.

Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.

A primary selection target for wheat ( Triticum aestivum ) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from −0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.

The present study was conducted to establish prediction models for grain yield and nitrogen (N) uptake using normalized difference vegetation index (NDVI) measurements with the GreenSeeker optical sensor for different cultivar groups of basmati rice (Oryza sativa L.) and to define the optimum sensing timing. Sensor readings were collected at 21, 28, 35, 42, and 49 days after transplanting (DAT) from multi-cultivar and multi-rate N fertilization experiments conducted in 2016 and 2017. Prediction model established by regressing NDVI day−1 as the determinant of plant biomass with grain yield and N uptake at maturity following exponential functions revealed that sensing the crop before or after 35 DAT (panicle initiation stage) was not accurate and did not predict satisfactorily the yield or N uptake potential. Regression analysis generated two potential and viable yield or N uptake prediction models: one for the basmati rice cultivar CSR30 (tall cultivar), and the other for a PB-PUSA (group of semi-dwarf cultivars). Validation of the prediction models using an independent experiment conducted in 2018 revealed that sensing the crop at the panicle initiation stage provide grain yield and N uptake predictions close to the observed grain yield (R2 = 0.86, RMSE = 6.1%) and N uptake (R2 = 0.75, RMSE = 8.5%). This study showed that yield and N uptake potential in basmati rice can be predicted using in-season NDVI data measured with the GreenSeeker optical sensor.

Research into crop growth models at the spatial scale is of great significance for evaluating crop growth, predicting grain yield and studying global climate change. Coupling spatial remote sensing (RS) data can effectively promote the simulation of growth models at spatial scales. However, the integration of RS data and crop models to produce a coupled model based on pixel by pixel requires a large amount of calculations. Simulation zone partitioning is used to separate and cluster the large area into a few relatively uniform zones. Then, the growth model can run on the basis of these units. This method both reflects spatial heterogeneity and avoids repeated simulations of regions with similar attributes, improving the simulation efficiency. In this study, simulation partitioning was performed using soil nutrient indices (organic matter content, total nitrogen content and available potassium content) and corresponding spatial characteristics of wheat growth, as indicated by RS data. A coupled model, integrating RS information and the WheatGrow model, using vegetation indices as the coupling parameters (based on the Particle Swarm Optimization algorithm and PROSAIL model), was developed. The aim was to realize accurate prediction of wheat growth parameters and grain yield at the spatial scale. Good zone partitions were obtained by partitioning with the spatial combination of soil nutrient indices and the wheat canopy vegetation index, calculated during the main growth (jointing, heading and filling) stages. The variation coefficients of each index within individual simulation sub-zones were much smaller than those of the indices across the whole area. An analysis of variance showed that the indices were significantly different between the simulation sub-zones, which indicated that appropriate simulated sub-zones had been defined. The minimum root mean square error of the leaf area index, leaf nitrogen accumulation and yield between the predicted values and the values simulated by the coupled model were 0.92, 1.12 g m−2, and 409.70 kg ha−1, respectively, which were obtained when the soil-adjusted vegetation index was used as a partitioning zone and assimilating parameter. These results demonstrated that the coupled model of the crop model and RS data, based on the simulation sub-zones had a good prediction accuracy. The results provide important technical support for increasing model efficiency, when crop models need to be applied at the spatial scale.

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average R2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

Grain yield is affected by a variety of complex factors, with large volatility and randomness. In order to improve the accuracy of grain yield prediction, this paper proposes a grain yield prediction method with improved unbiased grey Markov model. In the unbiased grey Markov model, after the average division of states is performed, the values within each state are nonlinear. This paper proposes to replace the method of taking the median of the first and last transitions in the unbiased Markov chain with the average for the calculation of state division and to correct the residual values of the prediction results by using the improved unbiased grey Markov model, in order to improve the accuracy of the predicted value of grain yield. Simulation experiments were conducted to compare the grey GM (1, 1) model, the unbiased grey GM (1, 1) model, the unbiased grey Markov model and the improved unbiased grey Markov model. The original grain output data for Chongqing from 2000 to 2022 were used for the validation analysis to compare the prediction accuracies of the four models. The results show that the prediction accuracy of the grey GM (1, 1) model and the unbiased grey GM (1, 1) model is basically the same, with an average error of 3.213%. The prediction accuracy of the unbiased grey Markov model is better, with an average error of 2.039%. The unbiased grey Markov model has the smallest prediction average error of 1.367%. Compared with the previous three models, the improved unbiased grey Markov model can further improve the prediction accuracy, which is suitable for medium- and long-term prediction and predicts the grain production data of Chongqing in the next eight years.

Many broadacre farmers have a time series of crop yield monitor data for their fields which are often augmented with additional data, such as soil apparent electrical conductivity surveys and soil test results. In addition there are now readily available national and global datasets, such as rainfall and MODIS, which can be used to represent the crop-growing environment. Rather than analysing one field at a time as is typical in precision agriculture research, there is an opportunity to explore the value of combining data over multiple fields/farms and years into one dataset. Using these datasets in conjunction with machine learning approaches allows predictive models of crop yield to be built. In this study, several large farms in Western Australia were used as a case study, and yield monitor data from wheat, barley and canola crops from three different seasons (2013, 2014 and 2015) that covered ~ 11 000 to ~ 17 000 hectares in each year were used. The yield data were processed to a 10 m grid, and for each observation point associated predictor variables in space and time were collated. The data were then aggregated to a 100 m spatial resolution for modelling yield. Random forest models were used to predict crop yield of wheat, barley and canola using this dataset. Three separate models were created based on pre-sowing, mid-season and late-season conditions to explore the changes in the predictive ability of the model as more within-season information became available. These time points also coincide with points in the season when a management decision is made, such as the application of fertiliser. The models were evaluated with cross-validation using both fields and years for data splitting, and this was assessed at the field spatial resolution. Cross-validated results showed the models predicted yield relatively accurately, with a root mean square error of 0.36 to 0.42 t ha−1, and a Lin’s concordance correlation coefficient of 0.89 to 0.92 at the field resolution. The models performed better as the season progressed, largely because more information about within-season data became available (e.g. rainfall). The more years of yield data that were available for a field, the better the predictions were, and future work should use a longer time-series of yield data. The generic nature of this method makes it possible to apply to other agricultural systems where yield monitor data is available. Future work should also explore the integration of more data sources into the models, focus on predicting at finer spatial resolutions within fields, and the possibility of using the yield forecasts to guide management decisions.