Yield Prediction Method Research Articles

A precise grape yield prediction method based on a modified DCNN model

This paper presents an innovative method for predicting grape yield using a modified deep convolutional neural network (M−DCNN). This approach integrates advanced image processing techniques with deep learning algorithms to analyze high-resolution images of vineyards, enabling precise yield quantification. The M−DCNN model incorporates novel convolutional layers and activation functions to enhance accuracy and generalization capabilities. Extensive experiments were conducted using a varied dataset of grape varieties and vineyard conditions. The method involves image processing to identify grape clusters and monitor their growth at different stages. We evaluated the performance of several algorithms, including K-nearest neighbors (KNN), multinomial logistic regression (MLR), support vector machines (SVM), random forests (RF), deep convolutional neural network (DCNN), and M−DCNN, throughout the training and testing phases. The M−DCNN achieved the highest training accuracy at 97.35%, which represents a 0.85% improvement over the standard DCNN. Furthermore, in detecting grape berries, our method attained an average F1 score of 73.32% and coefficients of determination exceeding 0.98, showcasing high accuracy and precision. The results indicate that the M−DCNN model surpasses traditional yield estimation methods and existing machine learning models, affirming its potential to significantly enhance precision agriculture. This method offers a robust tool for accurate grape yield forecasting, facilitating informed decision-making in viticulture.

A Precise Plot-Level Rice Yield Prediction Method Based on Panicle Detection

Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to propose the YOLOv8s+LSKA+HorNet rice panicle detection and counting model, based on a drone remote sensing platform. Using the panicle count data collected by this model, along with the thousand-grain weight, number of grains per panicle, and actual yield data from a rice nitrogen gradient experimental field, various machine learning models were trained to ultimately propose a field-level rapid rice yield estimation model, RFYOLO. The experimental results show that the rice panicle detection and counting model can achieve an average precision (AP) of 98.0% and a detection speed of 20.3 milliseconds. The final yield estimation model achieved a prediction R2 value of 0.84. The detection and counting model significantly reduced missed and duplicate detections of rice panicles. Additionally, this study not only enhanced the model’s generalization ability and practicality through algorithmic innovation but also verified the impact of yield data range on the stability of the estimation model through the rice nitrogen gradient experiment. This is significant for early rice yield estimation and helping agricultural producers make more informed planting decisions.

An Effective Soil Analysis and Crop Yield Prediction Based on Optimised Light GBM in Smart Agriculture

ABSTRACTIn the agricultural sector, crop yield prediction plays an important role as it helps farmers make decisions about the growing season and type of crops to get better yield. The main goal in the agricultural sector is to reduce operating costs and pollution by improving crop yields and quality. This paper proposes an effective method for soil analysis and crop yield prediction for intelligent agriculture. The collected data are preprocessed using missing value interpolation and data normalisation techniques. Feature selection is performed on the preprocessed data using the Aquila‐based adaptive optimisation algorithm, which selects the best trait subset for yield prediction. An improved lightweight gradient‐boosting machine based on the Battle Royale Optimisation technique is used for classification. The performance of the proposed system is evaluated using mean absolute error, root mean square error, R‐squared, mean square error, mean square logarithmic error and mean absolute percentage error, and the proposed system achieved an accuracy of 97%. The proposed system accurately predicts crop yields, improving crop production and quality.

Prediction Models of Growth Characteristics and Yield for Chinese Winter Wheat Based on Machine Learning

In order to eliminate the limitations of traditional winter wheat yield prediction methods, the prediction models based on machine learning are used to improve the accuracy of winter wheat yield prediction. In this study, by collecting a large amount of domestic literature about wheat growth characteristics, the irrigation amount, fertilization amount, soil nutrient status, planting density, maximum leaf area index (LAImax), maximum aboveground dry matter accumulation (Dmax) and yield (Y) were chosen to develop the learning models. Using the data of the irrigation amount, fertilization amount, soil nutrient status and planting density as the training set, the regression prediction models (Gaussian process regression mode, linear regression model, regression tree mode and support vector machine model) were used to train and learn the data of the LAImax, Dmax and Y, respectively. The results show that the Gaussian regression model has the best precision compared to the other models. The coefficients of determination (R2) of the learning results of the Gaussian regression model for the LAImax, Dmax and Y are 0.9, 0.93 and 0.86, and the root mean square error (RMSE) is 0.57, 1125.1 and 640.41. Based on the data of the irrigation amount, nitrogen application amount, potassium application amount, phosphorus application amount, organic matter content, total nitrogen content, alkali-hydrolyzable nitrogen content, available phosphorus content, available potassium content and planting density, the method proposed in this paper can reliably predict the LAImax, the Dmax and Y of winter wheat. The results also have certain reference significance for the yield prediction of other crops.

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.

Crop yield prediction by Mestrial Environ Netsual Network (MENN)

Crop yield prediction methods can roughly predict actual yield, although better yield prediction performance is still sought. In the existing methodologies the crop yield prediction outcomes are based on the past experience data and failed to predict the exact outcomes of the crop yield. Hence, a hybrid approach namely Crop yield prediction by Mestrial Environ Netsual Network (MENN) has been proposed to overcome the challenges in the existing approaches and to predict the crop yield with impeccable manner. In previous techniques, the change in phenotype as well as genes in the seed and the plant pathology are not combined as a new model. Hence, Mestrial Neural Network (MNN) has been proposed which consist of Task allocation layer, Subset-net layer and Integrated yield estimation layer to predict the sowing seed gene along with the phenotype and pathology. Also, incorporated pathology module examines the phenotype of respected sowing seed selected for the prediction of yield value. Moreover, while combining the statistical data and image data for the prediction, the generalization ability of prediction model was affected by reason of the images that shared the same timestamp as the statistical data were eliminated as part of the procedure for creating the dataset utilized in the existing approaches. Hence, a novel, Yield Environ Netsual Network (YENN) has been proposed which is consists of two deep networks; (i) Deep Q network (DQN) and (ii) VGG16 for the generalization ability as well as the elimination of data caused by the same timestamp is rectified. Here, VGG-16 is utilized for processing the given input images. As a result, the proposed model well examine the potential disease based on the gene and environment conditions and effectively predict the yield value of crops.

Crop Monitoring System Using MODIS Time-Series Data for Within-Season Prediction of Yield and Production of US Corn and Soybeans

In terms of contribution to global food security, this study aimed to build a crop monitoring system for within-season yield prediction of US corn and soybeans by using the Moderate Resolution Imaging Spectroradiometer (time-series data, which consists of three essential core algorithms (crop phenology detection, early crop classification, and crop yield prediction methods)). Within-season predictions for 2018–2022 were then made to evaluate the perfor- mance of the proposed system by comparing it with the United States Department of Agriculture's (USDA's) monthly forecasts and the fixed statistical data. The absolute percentage errors of the proposed system for predicting national-level yield and production were less than 5% for all simulation years as of day of year (DOY) 279. The prediction accuracy as of DOY 247 and DOY 279 were comparable to the USDA's forecasts. The proposed system would enable us to make a comprehensive understanding about overview of US corn and soybean crop condition by visualizing detail spatial pattern of good- or poor harvest regions on a within-season basis.

Using the Test Guardband Estimation Method to Forecast Future Semiconductor Yield Trends

Semiconductor manufacturing and technology testing are developing at different speeds, and yield loss due to inaccuracies in the testing process has become an important issue. The integrated circuit test model is applied in this study using an iterative yield prediction method (IYP) to estimate the fabrication yield of future wafers based on their process parameters and electrical characteristics. Additionally, test guardband estimation (TGE) method is proposed to adjust the test guardband to maintain the desired product quality after testing. The proposed iterative yield prediction method is demonstrated using a set of parameters from the IEEE International Roadmap for Devices and Systems (IRDS) 2021. Evaluation of the specifications in the IRDS table reveal shows that IYP can demonstrate the appropriate yield curve as predicted.

A fast and efficient approach to estimate wild blueberry yield using machine learning with drone photography: Flight altitude, sampling method and model effects

Precise yield estimation is important for phenotyping and crop management but is challenging for large-scale and high-density crops, such as wild blueberries. Machine learning (ML) technology and unmanned aerial vehicle (UAV) platforms created opportunities for more accurate and efficient yield estimation. RGB cameras are widely used in image processing due to their low cost, easy operation, and data analysis advantages. Here, we aimed to identify an effective, accurate, low-cost, and practical yield prediction method that can be applied to wild blueberry fields and potentially other high-density crops. Deep learning (DL) was overlooked due to its data-intensive training requirements, and the acquisition of image and yield data for wild blueberries is time-consuming and difficult. We evaluated the feasibility of using RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models based on color and texture feature data obtained from UAV RGB images to predict wild blueberry yield, and tested the effect of flight altitude, sampling method, and ML model. We found that the XGBoost model trained on a systematically sampled dataset built from high-altitude color and texture features achieved the best prediction performance (R2 = 0.89, RMSE = 542.01, and MAE = 379.94). Interestingly, images captured at a higher altitude (30 m) performed better compared to that at lower altitudes (5 and 15 m), which provides clear evidence supporting higher and more efficient UAV cruise operations. Our study provides a non-destructive, efficient, fast, and easy-to-use yield prediction method for large-scale and high-density crops, confirming its feasibility with wild blueberries.

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.

Enhancing co-gasification gas yield prediction in downdraft gasifiers through statistical correction strategy

Compared to typical gasification methods, the co-gasification method can change syngas composition by adjusting feedstocks and mixing ratio, but predicting gas yield in co-gasification is often more difficult. As a typical gasification equipment, the downdraft gasifier is commonly used to conduct experimental studies on co-gasification. For this reason, a novel co-gasification gas yield prediction method combined with the statistical correction strategy is proposed utilizing a downdraft gasifier as the gasification equipment. The statistical correction strategy is implemented by the statistically based error correction method and uncertainty classification of gasification conditions. The co-gasification dataset of the downdraft gasifier was built to test the proposed method. The results demonstrate that the statistical correction strategy is effective, and the proposed method can forecast co-gasification gas yields accurately, with prediction models achieving R2 of 0.9418, 0.9842, 0.8952, and 0.9822 for H2, CH4, CO, and CO2 yields, respectively.

Method to predict alloy yield based on multiple raw material conditions and a PSO-LSTM network

The production of ferroalloys accounts for a large proportion of the total energy consumption of the steelmaking industry. Accurately predicting alloy element yields is the key to reducing alloy waste, but there are significant differences in alloy yield under different conditions using ferroalloy raw materials during steelmaking. Therefore, this paper proposes a multi-model alloy element yield prediction method based on a particle swarm optimization (PSO) hyperparameter-optimized long short-term memory (LSTM) network and raw material condition classification. The accuracy of the PSO-LSTM prediction model was verified through simulations and was significantly higher than that of other network models when using the same raw material conditions. The average absolute error of predictions using raw materials with a low drum index was 0.4485, and it was 0.6162 under a high drum index, which was significantly lower than that (0.7077) under the condition without classification. This demonstrates the rationality of classifying working conditions according to the raw material conditions of ferroalloys. In addition, this paper combines the prediction model with a linear programming algorithm to develop a ferroalloy operating system and uses it in a steel plant to guide workers to complete an alloying operation. After four months of industrial testing, the internal control rate of finished steel composition increased from 91–94 % to 95–98 %. According to statistical analysis, the optimized HRB400E threaded steel consumed 1.23 kg less silicon-manganese per ton of steel, reduced the cost of steel alloy per ton by 8.6 yuan, and significantly reduced the waste of ferroalloys during steelmaking.

An Intelligent Decision Support System for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms

Agriculture is crucial to economic growth and development. Crop yield forecasting is critical for food production which includes vegetables, fruits, flowers, and cattle. Artificial Intelligence (AI) is rising in agriculture, providing farmers with real-time or long-term insights about their fields. It allows us to identify the areas that require irrigation, fertilization, or pesticide treatment. Statistical models struggle to track complex relationships in crop yields due to numerous factors. Machine Learning (ML) and Deep Learning (DL) algorithms can solve this problem by training themselves in these relationships, enabling accurate predictions in agricultural yield prediction methods. Predicting product performance in agriculture is challenging due to various factors, but profit forecasting improves decision-making, production, economics, and food safety. The present study focuses on the use of ML and DL algorithms to suggest a novel decision support system for crop yield prediction with the objectives to develop a robust, accurate model, investigate algorithm effectiveness, and create a user-friendly system for informed crop production decisions. According to the results, the developed system is capable of making precise predictions, which can support farmers in making better decisions about how to manage their crops. The simulation results demonstrate that the intelligent decision support system proposed for crop yield prediction using ML and DL algorithms is capable of achieving high accuracy and precision. The system can be used to help farmers make better decisions about crop planting and management, which can lead to increased crop yields and profits. The results of our experiment show that our model is better than the others and it achieves an accuracy of 99.82 %. Additionally, we utilized ML to condense the input space while preserving high accuracy.

Establishment of an NPK nutrient monitor system in yield-graded cotton petioles under drip irrigation

BackgroundThe determination of nutrient content in the petiole is one of the important methods for achieving cotton fertilization management. The establishment of a monitoring system for the nutrient content of cotton petioles during important growth periods under drip irrigation is of great significance for achieving precise fertilization and environmental protection.MethodsA total of 100 cotton fields with an annual yield of 4500–7500 kg/ha were selected among the main cotton-growing areas of Northern Xinjiang. The nitrate nitrogen (NO3−–N), inorganic phosphorus (PO43−–P) and inorganic potassium (K+–K) content and yield of cotton petioles were recorded. Based on a yield of 6000 kg/ha as the dividing line, a two-level and yield-graded monitoring system for NO3−–N, PO43−–P and K+–K in cotton petioles during important growth periods was established, and predictive yield models for NO3−–N, PO43−–P and K+–K in petioles during important growth periods were established.ResultsThe results showed found that the yields of the 100 cotton fields surveyed were normally distributed. Therefore, two yield grades were classified using 6000 kg/ha as a criterion. Under different yield-graded, the NO3−–N, PO43−–P and K+–K content of petiole at important growth stages was significantly positively correlated with yield. Further, the variation range of NO3−–N, PO43−–P and K+–K content in petioles could be used as a standard for yield-graded. In addition, a yield prediction model for the NO3−–N, PO43−–P and K+–K content of petioles was developed. The SSO-BP validation model performed the best (R2 = 0.96, RMSE = 0.06 t/ha, MAE = 0.05 t/ha) in the full bud stage, which was 12.9% higher than the BP validation model. However, the RMSE and MAE were decreased by 86.7% and 88.1%, respectively.ConclusionThe establishment of NPK nutrition monitor system of cotton petioles under drip irrigation based on yield-graded provides an important basis for nutrition monitor of cotton petiole under drip irrigation in Xinjiang. It also provides a new method for cotton yield prediction.

YOLO-C: An Efficient and Robust Detection Algorithm for Mature Long Staple Cotton Targets with High-Resolution RGB Images

Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination (R2) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity.

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

CROP CULTIVATION FORECASTING USING MACHINE LEARNING

The primary sources of income in rural India are unquestionably agriculture and its linked sectors. Another important contribution to the nation's Gross Domestic Product (GDP) is the agricultural industry. The enormous scale of the agriculture industry is a blessing for the nation. In contrast to worldwide norms, the crop output per acre is unsatisfactory. This is one of the likely reasons why India's marginal farmers commit suicide at a higher rate than other farmers. This initiative offers farmers a practical and approachable yield prediction method. Through a web application, the suggested system connects farmers. The area, the type of soil, and a few environmental characteristics are supplied by the user. The Random Forest provided the most accurate results.

Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API (VIs & Tex + API) improved the accuracy of yield prediction than single-phase UAV imagery-based feature, with the panicle initiation stage being the best period for yield prediction (R2 as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi- temporal method (multi-temporal model VIs & Tex: R2 as 0.795, RMSE as 0.298, RRMSE as 0.072) can increase R2 by 0.020–0.111 and decrease RMSE by 0.020–0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.

Phenological analysis and yield estimation of rice based on multi-spectral and SAR data in Maha Sarakham, Thailand

ABSTRACT Rice is one of the most essential food crops in the world and accurate estimation of rice yield is a major content of agricultural research. Recently, scholars have used machine learning algorithms for rice yield estimation. However, there are few studies on rice yield prediction based on rice phenological stages. In this study, a method for rice yield prediction on the basis of rice phenology analysis was proposed. For this study, the cumulative NDVI and EVI based Logistic regression curves were carried out to determine the phenological period. Comparing several regression models, the results of the random forest regression model developed using phenology-based regression analysis performed better. The R2 of training and validation samples were 0.96 and 0.95, respectively, with RMSE of 0.06 ton/ha. This method is feasible for governments to predict rice yield and make farm risk management decisions.

A Yield Prediction Method Based on Nonparametric Statistical and Comparison With a Networks Method

In this paper, we propose a circuit yield prediction method to improve the efficiency of circuit yield prediction. Both traditional Monte Carlo simulation and the current popular machine learning for yield prediction require a large number of circuit simulation results, which increases production costs. This method finds the data boundary points through high-dimensional kernel density estimation, and performs simulation calculations on these boundary values instead of the overall simulation. Experiments show that, compared with machine learning and Monte Carlo methods, the proposed algorithm can efficiently predict the yield of circuits.