Yield Prediction Research Articles

Rice yield predictions using remote sensing and machine learning algorithms: A review

Crop yield prediction is becoming increasingly crucial due to global food security concerns, as highlighted by recent reports from the World Health Organization. Accurate early predictions can mitigate famine risks by estimating food supply, which is essential for 820 million people facing hunger globally. Rice is the primary staple food consumed worldwide; therefore, global rice yield and rice area are monitored using emerging technologies such as remote sensing (RS) and machine learning (ML). These technologies provide valuable tools for enhancing rice yield predictions. RS includes critical information on crop health, soil conditions and weather patterns. In contrast, ML algorithms analyze these datasets to identify patterns and relationships that affect yield. Integrating these technologies offers promising improvements in yield forecasting accuracy, with applications showing successful yield predictions 1-3 months before harvest. Various ML techniques, including Random Forest, Support Vector Machines and deep learning models such as LSTM (Long-Short Term Memory), have been employed, often in combination with RS data. However, these models face challenges, such as data quality, managing high-dimensional RS data and accounting for spatial and temporal variability. Despite these challenges, integrating RS and ML has significant potential for advancing precision agriculture and achieving sustainable food production. This study explores the advancements, practical applications and challenges associated with using RS and ML for rice yield prediction, emphasizing the importance of these technologies in addressing global food security and promoting sustainable agricultural practices.

Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan

Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan

Predictive Farming: Harnessing Data and AI for Smarter, Sustainable Agriculture

Predictive Farming: Harnessing Data and AI for Smarter, Sustainable Agriculture leverages advanced technologies to enhance agricultural productivity, sustainability, and efficiency. This research focuses on developing a predictive farming system powered by machine learning models to optimize crop management and yield prediction. The project integrates a web-based interface using Flask- based backend for machine learning model deployment. The system analyzes agricultural data to provide insights on crop health, soil conditions, and optimal farming practices. The results demonstrate improved decision-making capabilities for farmers, leading to better resource utilization and increased crop yields. Keywords : Smart Farming, Machine Learning, Crop Management, Flask, Precision Agriculture, Agricultural Technology

AI enhanced Smart Crop Selection

This paper presents an innovative approach to smart crop selection by utilizing machine learning algorithms, specifically XGBoost, combined with AI-driven insights. The proposed system integrates soil characteristics, climatic conditions, and market trends to offer precise recommendations tailored to optimize agricultural productivity and sustainability. By leveraging predictive analytics and neural networks, this system provides real-time yield predictions, pest risk analysis, and economic feasibility reports. The integration of advanced technologies bridges the gap between traditional farming methods and modern agricultural advancements, ensuring a comprehensive and efficient decision-making process for farmers. Key Words: Smart agriculture, machine learning, XGBoost, AI, crop recommendation, sustainable farming, predictive analytics

Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models

Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models

Rice Quality and Yield Prediction Based on Multi-Source Indicators at Different Periods

This study aims to develop an effective and reliable method for estimating rice quality indices and yield, addressing the growing need for rapid, non-destructive, and accurate predictions in modern agriculture. Field experiments were conducted in 2018 at the Suiling Water Conservancy Comprehensive Experimental Station (47°27′ N, 127°06′ E), using Longqingdao 3 as the test variety. Measurements included the leaf area index (LAI), chlorophyll content (SPAD), leaf nitrogen content (LNC), and leaf spectral reflectance during the tillering, jointing, and maturity stages. Based on these parameters, spectral indicators were calculated, and univariate linear regression models were developed to predict key rice quality indices. The results demonstrated that the optimal R2 values for brown rice rate, moisture content, and taste value were 0.866, 0.913, and 0.651, with corresponding RMSE values of 0.122, 0.081, and 1.167. After optimizing the models, the R2 values for the brown rice rate and taste value improved significantly to 0.95 (RMSE: 0.075) and 0.992 (RMSE: 0.179), respectively. Notably, the spectral index GM2 during the jointing stage achieved the highest accuracy for yield prediction, with an R2 value of 0.822. These findings confirm that integrating multiple indicators across different growth periods enhances the accuracy of rice quality and yield predictions, offering a robust and intelligent solution for practical agricultural applications.

Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction

Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA).

Robust counting for multi-species plants based on Few-Shot learning

Accurate counting of plant organs at various growth stages is crucial for crop growth monitoring, phenotypic assessment, and yield prediction. Traditional plant counting models, typically designed for specific plant types, often exhibit poor generalization capabilities. In this research, an improved model, Field-CounTR, was proposed to accurately count multiple plant organs. The Field-Count dataset, utilizing a Few-Shot method, was introduced to enhance accuracy in plant counting. Additionally, a mixed salient module was developed to improve the feature fusion capability of sample images by exploiting their high similarity in Few-Shot counting tasks. During the downsampling process, Shuffle Attention was integrated to reduce redundant information in the feature fusion process. Furthermore, a hybrid convolution module combining Do-Conv and dilated convolutions was developed to increase the speed of convolutional inference and expand the receptive field through over-parameterized and dilation operations. To assess the effectiveness of the proposed approach, tests were conducted using the Field-Count dataset, which includes Sorghum, Wheat, Maize, and Rice. The results demonstrated a mean absolute error (MAE) of 14.49 and a root mean square error (RMSE) of 21.14. Compared with the CounTR model, the Field-CounTR model reduced the MAE and RMSE by 2.01 and 1.33, respectively. The enhanced Field-CounTR model exhibited superior feature extraction performance, high detection accuracy, and excellent generalization capabilities. This model can accurately count multiple plant types in complex field or orchard conditions and offers a wide range of applications.

Green utilization of corn straw: Prediction of bio-oil liquefaction yield and application of bio-oil in asphalt

Green utilization of corn straw: Prediction of bio-oil liquefaction yield and application of bio-oil in asphalt

State-enhanced attention network for optimisation of energy and yield in gas atomised metal powder production

Gas atomisation is a widely used technique for producing spherical metal powder feedstock for additive manufacturing. However, the process parameters suffer from variability and inefficiency in balancing powder yield, energy consumption, and particle size distribution. Optimising these complex, interdependent parameters pose a significant challenge. This work proposes a novel State-Enhanced Attention Network architecture in a framework that simultaneously optimises yield and energy consumption during nitrogen gas atomisation for sustainable metal powder production. The novelty lies in integrating processed long-term memory states with the attention mechanism, enabling nuanced attention weighting. This allows the model to leverage global sequence context and recent state information for improved yield and energy predictions. The proposed network is trained and integrated into a non-dominated sorting genetic algorithm to enable multi-objective optimisation. This framework evolves a set of Pareto-optimal solutions that balance trade-offs between maximising yield and minimising energy consumption. The approach is evaluated using augmented real-world data from an industrial gas atomisation plant. The proposed model demonstrates significantly improved predictive accuracy on real-world datasets, compared with baseline deep learning models. Results highlight the capabilities of the proposed technique for automated, data-driven optimisation of gas atomisation, simultaneously improving yield, energy efficiency, quality control, and sustainability. The integrated deep learning and evolutionary optimisation framework also provides an innovative solution for enhanced control of additive manufacturing powder production processes.

Yield predictions of ‘Del Cerro’ cotton (Gossypium hirsutum L.) germplasm by multispectral monitoring in the north coast of Peru

Yield predictions of ‘Del Cerro’ cotton (Gossypium hirsutum L.) germplasm by multispectral monitoring in the north coast of Peru

Transformative Approaches to Agricultural Sustainability: Automation, Smart Greenhouses, and AI

Agricultural sustainability is continually undermined by climate change, resource depletion, and the increasing worldwide need for food. Fundamental technologies, including automation, smart greenhouses, and artificial intelligence (AI), are changing modern agricultural methods by providing novel ways to improve sustainability in farming. This review study examines the significance of these technologies in advancing sustainable agricultural systems, particularly their effects on resource optimization, environmental conservation, and economic efficiency. Automation technologies, such as robots, drones, and autonomous vehicles, enhance farm management by enhancing efficiency and minimizing resource waste. Intelligent greenhouses, fitted with IoT sensors and temperature regulation systems, provide precise management of environmental conditions, therefore improving agricultural output while minimizing water and energy use. AI-driven technologies, including machine learning and predictive analytics, enhance crop health monitoring, pest management, and yield prediction, enabling data-informed decision-making. The research analyses the combination of various technologies, focusing their synergies in developing comprehensive smart agricultural systems that promote enduring sustainability. Despite the apparent promise, challenges like substantial initial investment, technological intricacy, and scalability persist. This review continues by addressing future directions, policy implications, and research requirements for promoting the use of these technologies to enhance global agricultural sustainability.

Harnessing Climate Data for Accurate Crop Yield Predictions

Harnessing Climate Data for Accurate Crop Yield Predictions

Optimizing crop yield forecasting with ensemble machine learning techniques

Accurate crop yield prediction is critical for ensuring food security and efficient agricultural management, particu- larly in the face of climate change and rising global populations. Current predictive models often fall short in generalizing across diverse agricultural contexts due to their inability to capture complex interactions between various climatic and soil variables effectively. This study addresses these gaps by proposing a com- prehensive machine-learning framework that integrates ensemble methods to enhance crop yield prediction accuracy. Using a dataset enriched with climatic and agricultural features, we evaluated multiple models, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Bagging Regressor, and K-nearest neighbors. The Random Forest model emerged as the top performer, achieving an accuracy of 0.985 and a Mean Squared Error (MSE) of 1.08e+08. At the same time, the Bagging Regressor closely followed with an accuracy of 0.984 and comparable MSE. Gradient Boosting and XGBoost models also demonstrated robust performance, with accuracies ranging from 0.865 to 0.974 and MSE values between 9.60e+08 and 1.89e+08. Our approach includes extensive hyperparameter tuning and k-fold cross-validation to ensure model generalizability and robustness across agricultural scenarios. These findings highlight the effectiveness of ensemble methods in capturing complex data relationships and their superiority over traditional models in predicting crop yields. Our work sets the stage for future research into integrating real-time data and advanced hybrid models, aiming to refine predictive accuracy further and support sustainable agricultural practices.

Linking Soil Fertility and Production Constraints with Local Knowledge and Practices for Two Different Mangrove Swamp Rice Agroecologies, Guinea-Bissau, West Africa

Mangrove swamp rice (MSR) production is critical for the diet of small farmers of coastal Guinea-Bissau. In mangrove swamp agroecosystems, rice is grown during the rainy season when freshwater and nutrients are abundant. However, small-scale farmers face challenges like unpredictable rainfall and rising sea levels, which increase soil salinity and acidity. This study aims to assess soil physical–chemical properties, paired with farmers’ local practices, to evaluate fertility constraints, and to support sustainable soil–plant management practices. This co-designed research contributes to filling a gap concerning the adoption of sustainable agricultural practices adapted to specific contexts in West Africa. In two regions, Oio (center) and Tombali (south), rice yields were measured in semi-controlled trials both in two agroecological settings: Tidal Mangrove (TM) and Associated Mangrove (AM) fields. 380 soil samples were collected, and rice growing parameters were assessed during the 2021 and 2022 rice sowing, transplanting, and flowering periods. Principal Component Analyses (PCA) and Multivariate Regression Analysis (MRA) were applied to understand trends and build fertility proxies in predicting yields. Significant spatial and temporal variability in the soil properties between agroecologies was found. Salinity constraints in Oio TMs limit production to an average of 110 g/m2, compared to 250 g/m2 in Tombali. Yield predictions account for 81% and 56.9% of the variance in TMs and AMs, respectively. Variables such as organic matter (OM), nitrogen (N), potassium (K), and precipitation positively influence yields, whereas sand content, pH, and iron oxides show a negative effect. This study advances the understanding of MSR production in Guinea-Bissau and underscores the importance of incorporating farmers’ knowledge of their diverse and complex production systems to effectively address these challenges.

Semantic-Guided Transformer Network for Crop Classification in Hyperspectral Images

The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitations when processing hyperspectral remote sensing images, primarily in the following aspects. First, the complex background in the images. Various elements in the background may have similar spectral characteristics to the crops, and this spectral similarity makes the classification model susceptible to background interference, thus reducing classification accuracy. Second, the differences in crop scales increase the difficulty of feature extraction. In different image regions, the scale of crops can vary significantly, and traditional classification methods often struggle to effectively capture this information. Additionally, due to the limitations of spectral information, especially under multi-scale variation backgrounds, the extraction of crop information becomes even more challenging, leading to instability in the classification results. To address these issues, a semantic-guided transformer network (SGTN) is proposed, which aims to effectively overcome the limitations of these deep learning methods and improve crop classification accuracy and robustness. First, a multi-scale spatial–spectral information extraction (MSIE) module is designed that effectively handle the variations of crops at different scales in the image, thereby extracting richer and more accurate features, and reducing the impact of scale changes. Second, a semantic-guided attention (SGA) module is proposed, which enhances the model’s sensitivity to crop semantic information, further reducing background interference and improving the accuracy of crop area recognition. By combining the MSIE and SGA modules, the SGTN can focus on the semantic features of crops at multiple scales, thus generating more accurate classification results. Finally, a two-stage feature extraction structure is employed to further optimize the extraction of crop semantic features and enhance classification accuracy. The results show that on the Indian Pines, Pavia University, and Salinas benchmark datasets, the overall accuracies of the proposed model are 98.24%, 98.34%, and 97.89%, respectively. Compared with other methods, the model achieves better classification accuracy and generalization performance. In the future, the SGTN is expected to be applied to more agricultural remote sensing tasks, such as crop disease detection and yield prediction, providing more reliable technical support for precision agriculture and agricultural monitoring.

Evaluation of Statistical Models of NDVI and Agronomic Variables in a Protected Agriculture System

Vegetable production in intensive protected agriculture systems has evolved due to its intensity and economic importance. Sensors are increasingly common for decision-making in crop management and control of environmental variables, obtaining optimal yields, such as estimating vegetation indices. Innovation and technological advances in unmanned vehicle platforms have improved spatial, spectral, and temporal resolution. However, in protected agriculture systems, the use is limited due to the assumption of having controlled environmental conditions for indeterminate vegetable production. Therefore, sequential monitoring of NDVI is proposed during the 2022 and 2023 agricultural cycles using the Green Seeker® sensor and agronomic variables. This has created a database to generate predictive models of development and yield as a function of nutrient status. The results obtained indicate high significance levels for the development and NDVI curves in all phenological stages; in contrast to the yield predictive models, this is due to the maximum values (close to one) recorded for NDVI inside the greenhouse in comparison to the yield prediction obtained from the 18th week of harvest. Evaluating the models between NDVI and agronomic variables is not an index that offers certainty in predicting yield in indeterminate crops in protected agriculture production systems. This is due to the constant optimal development in response to controlled environmental conditions, nutrient status, and water supply inside the greenhouse, without the sustainability of yield, which decreases in the final stages of production until production becomes economically unprofitable.

Innovative Approaches to Bengal gram Yield Mapping: Integration of Sentinel-1 SAR and Crop Simulation Models for Precision Agriculture

Accurate spatial yield estimation is crucial for optimizing agricultural management and ensuring food security. This study integrates Sentinel-1A SAR remote sensing data and the DSSAT crop simulation model to predict Bengal gram (chickpea) yield in Nagaur district, Rajasthan, India. Sentinel-1A backscatter data were processed for crop area mapping, achieving an overall classification accuracy of 85.1% and a kappa index of 0.70, demonstrating the reliability of SAR for agricultural monitoring under diverse weather conditions. Leaf Area Index (LAI) was derived from SAR backscatter values and linked to DSSAT-simulated yields, generating spatial yield predictions. Validation using Crop Cutting Experiment (CCE) data showed a high agreement of 91.3% between predicted and observed yields, with low root mean square error (RMSE), confirming model accuracy. This research highlights the synergistic potential of SAR-based remote sensing and simulation models for large-scale yield forecasting, advancing precision agriculture. Future efforts may incorporate additional sensors and machine learning to further enhance prediction accuracy and adaptability to climate variability.

Yield Prediction of Supercritical Fluid Extraction of Nigella Sativa using Neutral Networks

Yield Prediction of Supercritical Fluid Extraction of Nigella Sativa using Neutral Networks

A Python Framework for Pest Management and Crop Yield prediction

A Python Framework for Pest Management and Crop Yield prediction