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High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m -2 in the first year and 64.4 g m -2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m -2 and 60.6 g m -2 , respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m -2 and 66.3 g m -2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.

There are certain growth stages and spectral regions that are optimal for obtaining a high accuracy in rice yield prediction by remote sensing. However, there is insufficient knowledge to establish a yield prediction model widely applicable for growth environments with different meteorological factors. In this study, high temporal resolution remote sensing using unmanned aerial vehicle-based hyperspectral imaging was performed to improve the yield prediction accuracy of paddy rice cultivated in different environments. The normalized difference spectral index, an index derived from canopy reflectance at any two spectral bands, was used for a simple linear regression analysis to estimate the optimum stage and spectral region for yield prediction. Although the highest prediction accuracy was obtained from the red-edge and near-infrared regions at the booting stage, the generalization performance for different growth environments was slightly higher at the heading stage than at the booting stage. The coefficient of determination and the root mean squared percentage error for the heading stage were R2 = 0.858 and RMSPE = 7.52%, and they were R2 = 0.853 and RMSPE = 9.22% for the booting stage, respectively. In addition, a correction by solar radiation was ineffective at improving the prediction accuracy. The results demonstrate the possibility of establishing regression models with a high prediction accuracy from a single remote sensing measurement at the heading stage without using meteorological data correction.

Rice grain yield prediction with UAV-driven multispectral images are re-emerging interests in precision agriculture, and an optimal sensing time is an important factor. The aims of this study were to (1) predict rice grain yield by using the estimated aboveground biomass (AGB) and leaf area index (LAI) from vegetation indices (VIs) and (2) determine the optimal sensing time in estimating AGB and LAI using VIs for grain yield prediction. An experimental trial was conducted in 2020 and 2021, involving two fertility conditions and five japonica rice cultivars (Aichinokaori, Asahi, Hatsushimo, Nakate Shinsenbon, and Nikomaru). Multi-temporal VIs were used to estimate AGB and LAI throughout the growth period with the extreme gradient boosting model and Gompertz model. The optimum time windows for predicting yield for each cultivar were determined using a single-day linear regression model. The results show that AGB and LAI could be estimated from VIs (R2: 0.56–0.83 and 0.57–0.73), and the optimum time window for UAV flights differed between cultivars, ranging from 4 to 31 days between the tillering stage and the initial heading stage. These findings help researchers to save resources and time for numerous UAV flights to predict rice grain yield.

In this study assimilation of MODIS LAI (MOD15A2) into DSSAT-CERES-rice crop simulation model was used to develop advance yield estimates of rice crop during pre-harvest stage (F3) in Palakkad district of Kerala during Mundakan (September- January) season 2022-23 and 2023-24. The free parameters identified as inputs for the DSSAT-CERES-rice crop simulation model were adjusted and optimized sequentially during assimilation process until a minimum value of cost function is reached. This helped to minimize the deviation between MODIS- LAI and model generated LAI and the yield predicted by the model consequently is taken as the predicted yield. The average predicted yield during 2022-23 and 2023-24 was 5590 kgha-1 and 5124 kgha-1 respectively. The yield prediction by simulation model integrated with remote sensing products had higher accuracy than using simulation model alone during both the years with number of panchayats having the BIAS above ± 10 per cent reduced from 20 to 12 and 23 to 11 during 2022-23 and 2023-24 respectively. The findings clearly show that incorporating satellite data into crop simulation models can produce more accurate rice production forecasts than crop simulation techniques used alone.

Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.

Prediction of crop yields is very helpful in ensuring food security, planning harvest management (storage, transport, and labor), and performing market planning. However, in Tanzania, where a majority of the population depends on crop farming as a primary economic activity, the digital tools for predicting crop yields are not yet available, especially at the grass-roots level. In this study, we developed and evaluated Maize Yield Prediction System (MYPS) that uses a short message service (SMS) and the Web to allow rural farmers (via SMS on mobile phones) and government officials (via Web browsers) to predict district-level end-of-season maize yields in Tanzania. The system uses LSTM (Long Short-Term Memory) deep learning models to forecast district-level season-end maize yields from remote sensing data (NDVI on the Terra MODIS satellite) and climate data [maximum temperature, minimum temperature, soil moisture, and precipitation (rainfall)]. The key findings reveal that our unimodal and bimodal deep learning models are very effective in predicting crop yields, achieving mean absolute percentage error (MAPE) scores of 3.656% and 6.648%, respectively, on test (unseen) data. This system will help rural farmers and the government in Tanzania make critical decisions to prevent hunger and plan better harvesting and marketing of crops.

Denitrification is one of the key processes of the global nitrogen (N) cycle driven by bacteria. It has been widely known for more than one hundred years as a process by which the biogeochemical N-cycle is balanced. To study this process, we develop an individual-based model called INDISIM-Denitrification. The model embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM and is designed to simulate in aerobic and anaerobic conditions the cell growth kinetics of denitrifying bacteria

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops.

Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.