Crop Yield Forecasting Research Articles

ECP-IEM: Enhancing seasonal crop productivity with deep integrated models.

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.

Beyond point forecasting: Probability density forecasting of corn yield based on quantile regression forest

Abstract Crop yield forecasting is crucial for global food security. In this paper, we go beyond traditional point forecasting to examine the probability density forecasting of corn yield using a quantile-based machine learning approach. Leveraging 36 years of county-level panel data that cover 1260 counties in China between 1984 and 2019, we develop a quantile regression forest model, enhancing the traditional random forest by integrating quantile regression, for probability density forecasting of corn yield. Quantile regression and quantile regression neural networks are selected as benchmarks. Thirteen meteorological variables are adopted as predictors, and LASSO is used to examine the importance of each variable. Our results show that all quantile-based models produce good point forecasts, prediction intervals, and probability density curves; in general, we find that quantile regression forest with LASSO is best. We also find that the quantile regression neural network does not perform better than the traditional quantile regression, and LASSO doesn’t improve prediction models much in this context. This paper demonstrates that quantile-based machine learning is a promising tool for probability density forecasting of corn yield — but attention must be paid to the selection of machine learning algorithms and their black box properties.

Precision Farming: AI Enhanced Crop Yield Forecasting

Precision Farming: AI Enhanced Crop Yield Forecasting

Ensemble Learning for Oat Yield Prediction Using Multi-Growth Stage UAV Images

Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield. The results show that, for single growth stages, base models achieved R2 values within the interval of 0.02 to 0.60 and RMSEs ranging from 391.50 to 620.49 kg/ha. By comparison, the StackReg improved performance, with R2 values extending from 0.25 to 0.61 and RMSEs narrowing to 385.33 and 542.02 kg/ha. In dual-stage and multi-stage settings, the StackReg consistently surpassed the base models, reaching R2 values of up to 0.65 and RMSE values as low as 371.77 kg/ha. These findings underscored the potential of combining UAV-derived multispectral imagery with ensemble learning for high-throughput phenotyping and yield forecasting, advancing precision agriculture in oat cultivation.

Optimizing Agricultural Outcomes: Machine Learning in Crop Yield Prediction

Digital agriculture is becoming more and more in demand as a result of the quick advancement of information technology. Crop yield has always garnered a lot of interest as a significant problem in agricultural production. At the moment, machine learning and artificial intelligence in general are the most popular methods for predicting agricultural productivity. Consequently, one of the main problems in digital agriculture is creating a machine learning technique that can reliably forecast crop yield. Crop yield prediction has a strong time correlation, in contrast to conventional regression prediction problems. For instance, there are significant temporal correlations in the weather data for every county. Furthermore, crop yield is somewhat impacted by geographic data from various places. For instance, a county is likely to have large yields if its neighbouring countries have a strong harvest. We used models like random forest, decision tree classifier, support vector machine, KNN, and logic regression in this study. With an accuracy score of 99.77%, random forest produced the greatest results out of all of them.

Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth

AbstractAgriculture is one of the sectors that has an important impact, taking into account the problem of sufficient food supply on a global level. The process of predicting the yield of crops is among the most challenging undertakings in the agricultural industry. Agriculture is the main source of income for most developing nations. The purpose of the study is to investigate the significant role that agriculture plays in boosting India's economic growth. Additionally, the research considers the challenges posed by a growing population and a changing environment in terms of agricultural production and food security. The research focuses on analysing the complex characteristics of the agricultural industry, with a particular emphasis on the nutritional importance of tropical fruits, notably bananas and arecanut. These fruits are well-known for their vital nutrients and their role in ensuring world food security. This study acknowledges the importance of sustainable agriculture practices and incorporates sophisticated machine learning algorithms as dynamic tools to forecast crop yields and enhance decision-making processes throughout the crop development cycle. The main aim of this study is to create strong machine learning models and statistical techniques that can accurately predict crop yield by combining a variety of environmental parameters, then assess which models outperform each other. Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. The validation method utilises important metrics like R square (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). This present research adds to the continuing discussion on using creative methods to promote sustainable agricultural growth and ensure food security.

Leveraging crop yield forecasts using satellite information for early warning in Senegal

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 ​% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Automation and AI in Precision Agriculture: Innovations for Enhanced Crop Management and Sustainability

Precision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders.

Crop Yield Prediction Using Machine-Learning

This study examines the utilization of machine learning techniques for forecasting agricultural yields. This is critical because it helps to improve farm productivity and food security. Agriculture is the backbone of Pakistan’s economy, and accurate crop yield prediction can greatly assist in economic planning and allocation of resources. “Several ML algorithms, including K-Nearest Neighbor, Lasso regression, Ridge Regression, Decision Tree, and Linear Regression,” were used in this research to forecast crop yield using historical data and climate variables. Among all the models developed, the KNN algorithm was the most effective, with MAE=108023825.91 and R2=0.98494, meaning that it had the highest accuracy. These results suggest that ML has a large potential to give good predictions about crop yields, which would be useful for decision-making in agriculture and crop management or strategy development. The paper also enlightens on the limitations of some algorithms, like Decision Trees, known for their low-performance abilities; it suggests other ensemble models as better alternatives to increase predictive power. Machine learning integration with big data platforms and advanced computational methods offers a promising approach toward smart agriculture, ensuring food security.

Modelling and Performance Evaluation of Machine Learning Techniques in Forecasting Cereal Yield for Shri Ganganagar Region, Rajasthan, India

One of agriculture's most difficult issues is predicting crop yield. Crop yield forecasting enables necessary decisions to be made to guarantee food security. The current study looks into the use of statistical and machine learning approaches for wheat and rice yield prediction using long-term weather and yield data of ShriGanganagar, Rajasthan, India. Weather-based models may give accurate crop production estimates, but choosing the right model for agricultural output projections can be difficult. As a result, different models were compared in this study to determine the best model for rice and wheat yield prediction including Multiple linear regression (SMLR), Artificial neural network (ANN), Least absolute shrinkage and selection operator (LASSO), Elastic net (ELNET), Ridge regression, and K-nearest neighbor (K-NN). K-NN outperformed ANN in rice crops and fared better in wheat crops based on the lowest nRMSE value. Ridge regression based on nRMSE was the next best model for Rice yield prediction in the examined region, and KNN was the second best model for Wheat crop.

Predictive Cultivation: Integrating Meteorological Data and Machine Learning for Enhanced Crop Yield Forecast

Agriculture is a key component of Telangana’s economy, and greater performance in this sector is crucial for inclusive growth. A central challenge is yielding estimation to predict crop yields before harvesting. This paper addresses this challenge with machine learning approaches includes Naive Bayes, KNN and Random Forest. The parameters considered for model testing are crop, season, rainfall and location. This paper includes a case study of Telangana with the help of Telangana weather data set to provide analysis on the key factors like overall rainfall recorded with respect to each Mandal, overall seasonal yield in selected years, seasonal yield of major crops like Bengal gram, groundnut and maize, and overall yield in two different agricultural seasons: rabi and kharif. Random forest machine learning model produces highest accuracy of 99.32% when compared with other process models.

METHODS OF FORECASTING GRAIN CROP YIELD INDICATORS TAKING INTO ACCOUNT THE INFLUENCE OF METEOROLOGICAL CONDITIONS IN THE INFORMATION-ANALYTICAL SUBSYSTEM

Forecasting crop yields is one of the key challenges for the agricultural sector, especially in the context of a changing climate and unstable weather conditions. Kazakhstan, possessing significant territories suitable for growing grain crops, faces many challenges related to the effective management of agricultural activities. In this regard, yield forecasting becomes an integral part of planning and decision-making processes in agriculture. Information and analytical subsystems that integrate yield forecasting methods allow agribusinesses to estimate future production more accurately, minimise risks associated with climate change and optimise resource use. An important component of such systems is the consideration of weather conditions, as weather factors have a direct impact on crop growth and development. The purpose of this article is to develop and evaluate modern methods of forecasting grain yields taking into account the influence of weather conditions, as well as their integration into information-analytical subsystems to improve the accuracy of agricultural forecasting. To achieve this goal, the article addresses the following tasks: to analyse existing methods of yield forecasting and identify their advantages and disadvantages, to develop forecasting models, including machine learning methods such as gradient bousting and recurrent neural networks, to validate the developed models on the basis of historical data using cross-validation methods, to evaluate the effectiveness of the proposed methods and compare them with basic models such as linear regression and simple average, to evaluate the effectiveness of the proposed methods and to compare them with the basic models such as linear regression and simple average. This article reviews modern methods of forecasting grain crop yields in Kazakhstan, as well as technologies used in information-analytical subsystems. Particular attention is paid to the analysis of the influence of meteorological conditions on yields and the development of models that take this factor into account. The presented review and research results are aimed at improving the existing approaches to the management of agricultural processes under conditions of growing uncertainty caused by climate change. The article explores an important scientific task related to the development of methods for step-by-step forecasting of agrometeorological factors and grain yields, relying on the principle of analogy.

Capsular attention Conv-LSTM network (CACN): A deep learning structure for crop yield estimation based on multispectral imagery

Precise prediction of agricultural production output is crucial for farmers, policymakers, and the Farming-related industry. This article introduces a novel methodology to crop yield forecasting using a capsular neural network equipped with Conv-LSTM and attention mechanism. Our model combines the strengths of 3DCNN, and Conv-LSTM, which can capture the temporal dependencies and 3D features of crop yield data, and attention mechanism, which Can prioritize the most significant characteristics for making predictions. We evaluated CACN on a sizable collection of data of soybean crop yield in the United States from 2003 to 2019 and evaluated against various cutting-edge deep learning models. The outcomes indicate that our suggested approach surpasses other models in performance in terms of RMSE, correlation coefficient, and prediction error map. Specifically, our model achieved approximately 14 % improvement in terms of RMSE, compared to the state-of-the-art model Deep-Yield. Our model also demonstrated the ability to extract more meaningful features and capture the complex relationships between crop yield data and meteorological variables. Overall, our proposed method shows great potential for accurate and efficient crop yield forecasting and can be applied to other crops and regions.

Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data

Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data

Comparative analysis of leaf area index and maize yield estimation assimilating remote sensing and DSSAT crop simulation model

Maize is a global staple crop, impacting food security, economic development, and agricultural sustainability. This study investigates the integration of Sentinel-1A Synthetic Aperture Radar (SAR) data with the DSSAT CERES-Maize crop simulation model to estimate Leaf Area Index (LAI) and rabi maize yield in Belagavi district, Karnataka. Field data, including LAI, days to anthesis, silking, grain filling, and farmers' field practices, were collected for model calibration and validation, supplemented by crop-cutting experiments (CCE) to determine actual yields. The study revealed strong correlations between LAI values obtained from remote sensing (RS) and field observations, with RS-derived LAI showing an average agreement of approximately 96.07% compared to field measurements. The DSSAT model exhibited slightly better performance, averaging 97.09%. Statistical analysis for LAI showed an R² value of 0.853 for RS and 0.864 for DSSAT, indicating strong correlations with observed LAI values. For maize yield estimation, the DSSAT model demonstrated higher accuracy with an average yield of 8129 kg/ha, compared to RS-derived yield averages of 7533.9 kg/ha and CCE yield averages of 8096.6 kg/ha. The average concordance between DSSAT and CCE yields was 94.19%, while RS and CCE yields had an average concordance of 92.29%. Statistical analyses revealed coefficients of determination of 0.854 for DSSAT-CCE and 0.867 for RS-CCE comparisons. The study underscores the value of combining RS data with DSSAT for comprehensive and accurate crop yield forecasting, highlighting the potential for improved agricultural assessments and decision-making.

Agroecological Assessment of Arable Lands in the Leningrad Region of Russia under the Influence of Climate Change

The paper presents an analysis of the influence of climatic characteristics on the rating of land suitability for agricultural use. Soil fertility is one of the most important factors in land productivity and crop capacity; it is a complex value that depends not only on agrophysical and agrochemical soil properties but also on other natural factors, such as climate. There are different methodical approaches for a quantitative assessment of fertility level. The objectives of the research were to understand whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use in the example of the Leningrad region. The estimation and comparison between Semenov–Blagovidov’s method of quality land estimation and Karmanov’s method of appraisal of soils are given in this article. Karmanov’s method is highlighted in this paper for its ability to assess soil’s ecological indices more effectively than traditional methods. The research suggested that climate change may lead to increased variability in soil quality, with potential benefits for agriculture under certain climate scenarios, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields. The results of such assessment show that the performed calculation models can be used to forecast crop yields for future periods.

Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data

CONTEXTYield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning. OBJECTIVEThis study aims to investigate whether machine learning-based yield prediction models can capably predict Kharif season rice yields at the district level in India several months before the rice harvest takes place. METHODOLOGYThe methodology involved training 19 machine learning models such as CatBoost, LightGBM, Orthogonal Matching Pursuit, and Extremely Randomized Trees on 20 years of climate, satellite, and rice yield data across 247 of India's rice-producing districts. In addition to model-building, a dynamic dashboard was built understand how the reliability of rice yield predictions varies across district. RESULTS AND CONCLUSIONSThe results of the proof-of-concept machine learning pipeline demonstrated that rice yields can be predicted with a reasonable degree of accuracy, with out-of-sample R2, MAE, and MAPE performance of up to 0.82, 0.29, and 0.16 respectively. This performance outperformed test set performance reported in related literature on rice yield modelling in other contexts and countries. In addition, SHAP value analysis was conducted to infer both the importance and directional impact of the climate and remote sensing variables included in the model. Important features driving rice yields included temperature, soil water volume, and leaf area index. In particular, higher temperatures in August correlate with increased rice yields, particularly when the leaf area index in August is also high. Building on the results, a proof-of-concept dashboard was developed to allow users to easily explore which districts may experience a rise or fall in yield relative to the previous year. The dashboard show that the model may perform better in some regions than in others. For instance, the absolute percentage error for predicted versus actual yields ranged from an average of 7.1 % in districts in Uttarakhand to an average of 14.7 % in Uttar Pradesh. SIGNIFICANCEThis study underscores the potential for policymakers to consider scaling and operationalizing machine learning approaches to rice yield prediction in the context of agricultural early warning systems to deliver timely crop yield forecasts on a rolling basis throughout the season, thereby equipping agricultural decision-makers with the ability to make informed choices on irrigation scheduling, fertilizer application, and harvest planning to optimize crop output and resource use.

Yield prediction for crops by gradient-based algorithms.

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 R2 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%.

Preseason maize and wheat yield forecasts for early warning of crop failure

Provided the considerable logistical challenges of anticipatory action and disaster response programs, there is a need for early warning of crop failures at lead times of six to twelve months. But crop yield forecasts at these lead times are virtually nonexistent. By leveraging recent advances in climate forecasting, we demonstrate that global preseason crop yield forecasts are not only possible but are skillful over considerable portions of cropland. Globally, maize and wheat forecasts are skillful at lead times of up to a year ahead of harvest for 15% and 30% of harvested areas, respectively. Forecasts are most skillful in Southeast Africa and Southeast Asia for maize and parts of South and Central Asia, Australia, and Southeast South America for wheat. Wheat forecasts, furthermore, remain skillful at lead times of over 18 months ahead of harvest in some locations. Our results demonstrate that the potential for preseason crop yield forecasts is greater than previously appreciated.

Smart Agriculture Application in Rice Cultivation

Rice is a staple food crop that feeds a significant portion of the world's population. Ensuring sustainable and efficient rice production is crucial for global food security. In recent years, the integration of smart agriculture technologies has shown promise in improving various aspects of rice cultivation. This paper provides an overview of the key smart agriculture applications that can enhance rice cultivation practices. The review discusses the use of precision farming techniques, such as GPS-guided tractors and drones for site-specific management of inputs like fertilizers and pesticides. It also examines how sensor networks and Internet of Things (IoT) devices can monitor environmental conditions, soil fertility, and plant health to enable data-driven decision-making. Smart irrigation systems leveraging soil moisture sensors and weather forecasts are highlighted as a means to optimize water usage. Furthermore, the paper explores the role of automation and robotics in automating labor-intensive tasks like transplanting, weeding, and harvesting. It also discusses the potential of machine learning and predictive analytics to improve crop yield forecasting, pest and disease management, and supply chain logistics. The adoption of these smart agriculture technologies in rice farming has demonstrated improvements in productivity, resource efficiency, and environmental sustainability. However, challenges remain in scaling up these solutions and ensuring their accessibility to smallholder farmers. The paper concludes by outlining future research directions and policy considerations to further advance smart agriculture in the rice cultivation sector.