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A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R 2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%.

Rice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.

Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population and global warming, addressing food security has become a priority, so accurate yield forecasting is very important. Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model’s leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. The proposed methodology consists of different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector Machine (SVM), correlation with Normalized Google Distance (NGD), feature ranking with rising star. This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) and Time Series CNN to predict crop yield and then recommendation for further improvement. The proposed model showed very good results in all datasets and showed significant improvement compared to baseline models. The ECP-IEM achieved an accuracy 96.34%, precision 94.56% and recall 95.23% on different datasets. Moreover, the proposed model was also evaluated based on MAE, MSE, and RMSE, which produced values of 0.191, 0.0674, and 0.238, respectively. This will help in improving production of crops by giving an early look about the yield of crops which will than help the farmer in improving the crops yield.

Accurate crop yield forecasting prior to harvest plays a vital role in formulating, implementing, and optimizing policies concerning food safety, as well as in the efficient management of agro-product storage and marketing. The growth and development of crops are inherently influenced by weather conditions, making models that utilize weather variables indispensable for providing reliable predictions of crop yields. However, selecting the most suitable crop production forecasting model can pose a challenging task. Therefore, in this study, three multivariate models were developed to predict soybean yield in eight major districts of Uttarakhand: Udham Singh Nagar, Almora, Uttarkashi, Dehradun, Pauri-Garhwal, Tehri-Garhwal, Rudraprayag, and Pithoragarh. The models used were Artificial Neural Networks (ANN), Principal Component Analysis—Artificial Neural Networks (PCA-ANN), and Least Absolute Shrinkage and Selection Operator (LASSO). To build and assess the models, historical time series data of soybean yields and weather indices were utilized. The dataset was divided into calibration (80% of the data) and validation sets (remaining data) to evaluate the model predictions. The models were trained to predict soybean yield based on average values of phenological stages derived from daily weather data. Both weighted and unweighted weather indices were employed in the computation. After evaluating the models, it was observed that the PCA-ANN model outperformed all others in predicting soybean yield. The overall ranking of model performances for all locations was as follows: PCA-ANN > ANN > LASSO. It was also noted that the PCA-ANN hybrid model was the most effective for forecasting soybean yields in the examined districts of Uttarakhand, providing valuable insights for agricultural planning and decision-making.

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

Rice is one of the most important cereal foods not only for India but also for the world. The production of crop depends upon the favorable climatic conditions. Farmers’ access to more accurate data on crop yields in various climate conditions can aid in crucial agronomic and crop selection decisions. Taking this into account, the motive of the present research was to find the best method of predicting rice crop yield in seven important rice producing districts of Uttarakhand, namely Udham Singh Nagar, Nainital, Haridwar, Dehradun, Champawat, Tehri-Garhwal, and Pauri Garhwal. Data on the weather variables for the crop-growing season (27th to 44th SMW) for 19 years was gathered from the respective district and the NASA power website, while rice production data for the research period was gathered from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare. Stepwise multiple linear regression (SMLR), least absolute shrinkage and selection operator (LASSO), ridge regression, elastic net (ELNET), and artificial neural network (ANN) were employed for the model’s development. The 80% data of the total datasets was utilized to calibrate the models, while the remaining 20% data was allocated for the model validation. On examining these models, LASSO was found to be the finest performing model followed by ELNET, while SMLR was the worst performing model during calibration stage. During validation stage, ANN performed better for Champawat, Dehradun, Haridwar, Pauri Garhwal, and Udham Singh Nagar. The performance of ELENT and LASSO was found to be best for Nainital and Tehri Garhwal, respectively. The performance of ridge regression and SMLR were found to be poor as compared to the other models for the rice yield forecasting.

This study aimed to calibrate and compare the performances of the DSSAT-CERES and WOFOST models in predicting wheat phenology, growth, and grain yield in different sowing environments on the plains of northwest India. These models were calibrated and evaluated via independent field experiment datasets on wheat phenology, the leaf area index, and yield attributes recorded at Gurdaspur and Ludhiana in Punjab State, India, during the winter ( Rabi ) season of 2016–17. The factors of the experiments were two wheat cultivars (PBW 725 and PBW 677) and three seeding dates (5th November 20th November and 5th December). For the CERES model, the cultivar coefficient was derived via GENCALC along with a trial-and-error approach; for the WOFOST model, the coefficients were manually adjusted. The CERES and WOFOST models' performances were then assessed by computing several error indices that compare the observed and simulated crop parameters. The experimental values indicated that wheat cultivar, sowing time, and location significantly influenced wheat growth and yield. The results indicated that the CERES and WOFOST models could accurately predict wheat phenology, biomass, and harvest indices within 10% of the normalized root mean square error (NRMSE) while retaining the effects of different treatments. Furthermore, both models could estimate the grain yield with ≤ 6% NRMSE. According to the high correlation coefficient (r > 0.80; p < 0.001) and coefficient of determination (R 2  > 60%), the observed and simulated wheat growth and yield characteristics showed significant agreement under all testing conditions. Both models demonstrated acceptable accuracy in capturing wheat LAI temporal dynamics, although they exhibited notable variation in maximum leaf area index (LAI max ) estimation, with mean absolute percentage errors (MAPEs) ranging from 26 to 66%. Despite inherent model limitations, the demonstrated accuracy in simulating wheat phenological development and yield attributes supported the models' applicability for regional-scale wheat yield forecasting across diverse agronomic management practices and environmental conditions. A quantitative assessment of model performance metrics indicated that, compared with the CERES model, WOFOST demonstrated marginally superior accuracy in simulating wheat phenological development and yield parameters.

Crop yield forecasting is the art of predicting yield before harvest and is crucial for sound planning and policy making at various levels. Rapeseed and mustard (R&M) is the predominant oilseed crop in Assam and is very much sensitive to climatic factors compared to other crops grown during rabi season. In the context of recent climate variability observed in the Brahmaputra valley, development of efficient yield forecasting models for R&M based on weather parameters would certainly help the policy makers in formulating appropriate policies and decision-making. The present study was undertaken to develop R&M pre-harvest yield forecast models at F1 (flowering), F2 (siliqua formation) and F3 (siliqua development & maturity) stages for 15 districts of Assam using stepwise multiple linear regression (SMLR) and artificial neural network (ANN) techniques by analysing the yield and weather data for 27 years and to investigate the impact of weather factors in determining the yield of R&M. The results were enumerated based on coefficient of determination (R2), root mean square error (RMSE) and normalized root mean square error (RMSEn) values. ANN models were found to have greater R2 values over SMLR at all the three stages of forecast except in 2–3 districts. Similarly, profound improvement was observed in RMSE and RMSEn values of the forecast models using ANN technique. Analysing the percent error between observed and forecasted yield, ANN weather-based models were found to give more accurate pre-harvest yield prediction of R&M in the Brahmaputra valley of Assam. Temperature and relative humidity were found to be the most significant parameters in affecting R&M yield in most of the districts during all the three stages of forecast.

Pre-harvest yield forecasting is important for the sustainability of agricultural companies, enabling more sustainable economic decision-making. In the present study, we propose an approach based on the sum of degree days of the corn crop related to the dates of satellite images to organize the data of two crops generating predictive models with the k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost). The field study was carried out in a commercial area during the 2017/18 and 2018/19 harvests. Spectral data were obtained from Sentinel-2 satellite images. After the correction and processing of the images, the values ​​of the spectral bands and the vegetation indices were obtained. For the development of the models, the images obtained throughout the cycle were divided into three classes of the mean weeks before harvest (WBH) from different degree-days (GD) during the cycle, in this study we adopted 12 combinations of data inputs to develop the models. In yield forecasting, we were able to forecast approximately 30 to 70 days before harvest (500 to 900 degree-days before harvest), in addition, the most accurate models were when the data used as driven variables were the spectral bands of the red, blue, green and nir collected from 800 to 1200 degree-days of the culture (WBH4). For the models developed, combined with WBH for yield forecast, it was possible to forecast yield with an average error of 0.503 t ha-1, and the greatest precision and accuracy occurred with the use of all variables RGB e Near-infrared.

Bajra (pearl millet) is a very important crop in Rajasthan, India, since it is drought-resistant, nutritious, and culturally important. But its productivity is becoming vulnerable to changes in climate, such as erratic rain and temperature changes, and thus precise estimation of yield is vital. Crop Yield Prediction (CYP) indicators like soil decomposition, rainfall and meteorological patterns are slowly evolving, exhibiting long-term temporal dependency and propagating over time. Conventional cropping prediction algorithms based on artificial intelligence process the historical data and these indicators in a unidirectional manner. While mapping the temporal dependencies, these algorithms consider each year independently and do not capture the delayed effect, like salt degradation. To address this issue, the study proposes a region-based spatiotemporal model with an attention-guided Bidirectional LSTM (Long-Short Term Memory) framework for CYP, termed as G-BiLSTM. The proposed model reproduces the spatial relationships between districts via GCN (Graph Convolution Network) -based immediate neighbour extraction. Further, a Bidirectional LSTM is used to model multi-year CYP temporal features, allowing each annual observation to be encoded using both past and future temporal context. A variance-reduced and comprehensible representation is produced by integrating an attention mechanism to adaptively highlight the most informative years within a temporal window. Using 15 agroenvironmental characteristics, including understudied elements like saline and alkaline soil composition, the framework is assessed on a dataset that includes 32 districts in Rajasthan over 13 years (2007–2019). The suggested attention-enhanced BiLSTM consistently outperforms traditional temporal models, achieving lower prediction error and better generalisation, according to experimental results analysis using a three-year sliding temporal window. For regional crop yield forecasting, the suggested method offers a scalable solution.