Hybrid prediction strategy to predict agricultural information

Crop Yield Prediction (CYP) is critical to world food production. Food safety is a top priority for policymakers. They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business. Crop Yield (CY) is a complex variable influenced by multiple factors, including genotype, environment, and their interactions. CYP is a significant agrarian issue. However, CYP is the main task due to many composite factors, such as climatic conditions and soil characteristics. Machine Learning (ML) is a powerful tool for supporting CYP decisions, including decision support on which crops to grow in a specific season. Generally, Artificial Neural Networks (ANN) are usually used to predict the behaviour of complex non-linear models. As a result, this research paper attempts to determine the correlations between climatic variables, soil nutrients, and CY with the available data. In ANN, three methods, Levenberg-Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG), are used to train the neural network (NN) model and then compared to determine prediction accuracy. The performance measures of the training, as declared above, such as Mean Squared Error (MSE) and correlation coefficient (R), were determined to assess the ANN models that had been built. The experimental study proves that LM training algorithms are better, while BR and SCG have minimal performance.

Agriculture is the fundamental source of food, income, and livelihood for rural communities in India. Numerous crops are affected due to the lack of technical decision-making support and variations in weather patterns, temperature, rainfall, and atmosphere factors, which play a critical role in defining crop yield. Hence, choosing the appropriate crop to maximize yield is key to enhancing real-time farming practices. This study proposes an Auto-Regressive Integrated Moving Average (ARIMA) and Multi-Scale Gated Recurrent Unit (MSGRU) model for effective crop yield prediction and crop recommendation. Initially, label encoding and min-max normalization techniques are applied during the pre-processing phase to transform categorical values into uniform, numerical format for data scaling. Then, ARIMA is employed for crop yield prediction, followed by the MSGRU network deployed to extract both short-term and long-term dependencies, enabling accurate crop recommendation. The proposed ARIMA–MSGRU model achieves a superior accuracy of 99.73% at a reduced RMSE of 1.568, outperforming existing algorithms, demonstrating greater effectiveness.

Agriculture plays a crucial role in the economic development of many countries, especially where farming is the primary source of livelihood. Farmers often face difficulties in selecting suitable crops, predicting crop yield, identifying plant diseases at an early stage, and estimating financial outcomes such as profit or loss. To address these challenges, this study presents Sheti Mitra, an interactive machine learning–based system designed to assist farmers in making informed agricultural decisions. The proposed system integrates crop recommendation, crop yield prediction, and plant disease detection into a single platform. The system utilizes image processing and deep learning techniques to detect diseases from leaf images using Convolutional Neural Networks (CNN), while crop recommendation and yield prediction are performed using machine learning algorithms such as Random Forest. Environmental parameters including soil nutrients, temperature, humidity, and rainfall are analyzed to determine the most suitable crop and estimate expected yield. Additionally, the system provides an estimation of profit or loss based on predicted yield and crop market factors, helping farmers plan their agricultural activities effectively. A web-based interface built using HTML, CSS, and JavaScript interacts with a Flask-based backend that processes user inputs and generates predictions using trained models. By combining disease detection with crop and yield prediction, the proposed system offers a comprehensive decision-support tool for farmers. The implementation demonstrates that machine learning techniques can significantly improve agricultural productivity and reduce risks associated with crop selection and disease management. This system aims to support smart farming practices and promote sustainable agricultural development.

This article explores the integration of advanced Artificial Intelligence (AI) enabled deep learning methods with accurate crop yield prediction. The objective of the work is to enhance the accuracy, sensitivity, and specificity of crop yield prediction. Also, false positive and false negative cases are minimized in crop yield prediction. AI-enabled Barilai–Blinder–Oaxaca–Bernoulli Deep Classifier (BBO-BDC) is proposed including preprocessing, feature selection, and crop yield prediction. First, the raw samples were collected from the crop yield prediction dataset. Barilai–Borwein Gradient Min–max Normalization-based preprocessing is applied to eliminate all missing values. Second, to provide fine-grained feature subsets, the Blinder–Oaxaca Statistical Decomposition-based feature selection method is used. Finally, an AI-enabled Bernoulli Deep Belief Network is designed to predict the crop yield. The empirical results demonstrate that the BBO-BDC technique significantly improves the accuracy up to 12%, specificity up to 15%, and sensitivity up to 3% with feature selection. Furthermore, the BBO-BDC technique realizes a substantial reduction in convergence speed by 29% and 51% reduction in overhead compared to conventional methods with feature selection. The study of innovative AI method integrated with Min–max Normalization, Barilai–Borwein gradient, Blinder–Oaxaca decomposition function, Deep Belief Network, Xavier Initialization function, Bernoulli distribution function, and Principal Components for achieving better performance in crop yield prediction.

Crop yield prediction provides information to policymakers in the agricultural production system. This study used leaf reflectance from a spectroradiometer to model grain yield (GY) and aboveground biomass yield (ABY) of maize (Zea mays L.) at Aba Gerima catchment, Ethiopia. A FieldSpec IV (350-2,500 nm wavelengths) spectroradiometer was used to estimate the spectral reflectance of crop leaves during the grain-filling phase. The spectral vegetation indices, such as enhanced vegetation index (EVI), normalized difference VI (NDVI), green NDVI (GNDVI), soil adjusted VI, red NDVI, and simple ratio were deduced from the spectral reflectance. We used regression analyses to identify and predict GY and ABY at the catchment level. The coefficient of determination (R2), the root mean square error (RMSE), and relative importance (RI) were used for evaluating model performance. The findings revealed that the best-fitting curve was obtained between GY and NDVI (R2 = 0.70; RMSE = 0.065; P < 0.0001; RI = 0.19), followed by EVI (R2 = 0.65; RMSE = 0.024; RI = 0.61; P < 0.0001). While the best-fitting curve was obtained between ABY and GNDVI (R2 = 0.71; RI = 0.24; P < 0.0001), followed by NDVI (R2 = 0.77; RI = 0.17; P < 0.0001). The highest GY (7.18 ton/ha) and ABY (18.71 ton/ha) of maize were recorded at a soil bunded plot on a gentle slope. Combined spectral indices were also employed to predict GY with R2 (0.83) and RMSE (0.24) and ABY with R2 (0.78) and RMSE (0.12). Thus, the maize's GY and ABY can be predicted with acceptable accuracy using spectral reflectance indices derived from spectroradiometer in an area like the Aba Gerima catchment. An estimation model of crop yields could help policy-makers in identifying yield-limiting factors and achieve decisive actions to get better crop yields and food security for Ethiopia.

The agricultural sector is indispensable to feeding the growing global population, making efficient crop management and yield prediction imperative. Traditional farming practices often rely on subjective decision-making and generalized fertilizer application methods, leading to suboptimal resource utilization and yield outcomes. In this research, we introduce an innovative method Utilizing the capability the bunch of algorithms introduced for machine learning tasks to precise fertilizer recommendation and crop yield prediction. The developed system provides farmers with personalized fertilizer recommendations tailored to their specific soil and crop requirements, thereby minimizing waste and maximizing yield potential. Additionally, real-time monitoring and feedback mechanisms enable adaptive adjustments throughout the growing season, ensuring timely interventions to mitigate adverse outcomes and optimize productivity

Agriculture is paramount to global food security, and predicting crop yields is crucial for policy and planning. However, predicting these yields is challenging due to the myriad of influencing factors, from soil quality to climate conditions. While traditional methods relied on historical data and farmer experience, recent advancements have witnessed a shift towards machine learning (ML) for improved accuracy. This study explored the application of machine learning (ML) techniques in predicting crop yields using data from Nigeria. Previous efforts lacked transferability across crops and localities; this research aimed to devise modular and reusable workflows. Using data from the Agricultural Performance Survey of Nigeria, this study evaluated the performance of different machine learning algorithms, including Linear Regression, Support Vector Regressor, K-Nearest neighbor, and Decision Tree Regressor. Results revealed the Decision Tree Regressor as the superior model for crop yield prediction, achieving a prediction accuracy of 72%. The findings underscore the potential of integrating ML in agricultural planning in Nigeria where agriculture significantly impacts the economy. Further research is encouraged to refine these models for broader application across varying agroecological zones.

The prediction of crop yield is of significant interest for growers utilising biochar on land, while managers prioritise the enhancement of soil organic carbon (SOC). However, it remains unclear to what extent machine learning can accurately predict crop yield or SOC when biochar is applied to soil. In this study, Random Forest (RF) and Multilayer Perceptron Neural Network (MLP‐NN) models were employed to predict crop yield and SOC with 297 paired data from field trials. The results indicated that the RF model (test R 2 = 0.83) did not differ significantly from the MLP‐NN model (test R 2 = 0.84) in predicting crop yield. However, the RF model (test R 2 = 0.87) performs significantly better than the MLP‐NN model (test R 2 = 0.53) in predicting SOC. The most influential features for crop yield were found to be the biochar application rate (15%), initial SOC (13%), biochar pH (10%), and biochar TP (10%). In contrast, the variation of SOC was primarily influenced by latitude (26%), biochar application rate (22%), initial SOC (15%), and biochar pH (13%). Furthermore, both crop yield and SOC variation were influenced by multiple factors, not solely one, and their impacts were not necessarily linear. This study suggests that the optimization of biochar pH and phosphorus content, along with the regulation of its application rate in sandy or clay‐rich soils, can simultaneously enhance both crop yield and SOC. In the future, we hope to develop a decision support system with prediction, different scenarios, and consultation capabilities based on geospatial location.

The unpredictability of crop yield due to severe weather events such as drought and extreme heat continues to be a key worry. The present study evaluated six meteorological and three Landsat satellite-based vegetation drought indices from 1986 to 2019 in the drought-prone-semi-arid Saurashtra region of Gujarat (India). Cotton and groundnut crop yield prediction models were developed using multiple linear regression (MLR), artificial neural network with MLP, and random forest (RF). The models performed crop yield estimation at two timescales, i.e., 75 days after sowing and 105 days after sowing. The standardized precipitation evapotranspiration index/reconnaissance drought index among meteorological drought indices, normalized difference vegetation anomaly index/vegetation condition index, and normalized difference water index anomaly were chosen as best highest correlations with crop yields. The RF-based models were found most efficient in predicting the cotton and groundnut yield of Saurashtra with R2 ranging from 0.77 to 0.92, Nash–Sutcliffe efficiency ranging from 71 to 90%, and root-mean-square error ranging from 80 to 133 kg/ha for cotton and 299 to 453 kg/ha for groundnut. This study demonstrated the method for making several decisions based on early crop yield prediction including timely drought mitigation measures.

This research presents a bibliometric analysis of articles predicting crop yield using machine learning methods. While several systematic review articles exist, a comprehensive bibliometric analysis illustrating the knowledge structure and research trends, along with collaboration networks among authors, institutions, and countries in this field, has not been conducted. The study focused on 826 articles published over a 32-year period (1992 to 2023) and revealed a significant increase in publications, particularly in recent years. Zhang Zhao from China authored the majority of articles, while the highest number of citations was associated with articles by Zhu Yan and Lobell. Leading countries in article publications are the USA, China, India, Germany, Australia, and Canada, showing strong interconnections in citing each other's research. The Chinese Academy of Sciences and the US Department of Agriculture are the institutions with the highest number of articles and citations in this domain. The journals Agricultural and Forest Meteorology and Remote Sensing are recognized as top ranking journals in this field (Q1). Based on co-occurrence analysis, three main thematic domains were identified: weather and crop yield prediction, plant growth simulation models, and crop yield prediction using remote sensing data. The research suggests a focus on variables such as disease, pests, insects, and soil salinity when predicting yield. Additionally, greater attention should be given to discussions on food security and crop yield, especially in developing countries.

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

Objective: To propose Multi-parametric Deep Neural Network (MDNN) for modeling the impact of climate changes, multiple parameters related to the weather and soil for accurate crop yield prediction. Methods: In MDNN, a measure called Growing-Degree Day (GDD) is introduced for measuring the overall effect of weather conditions related to the crop yield. One of the key elements in MDNN is the neuron’s layer-wise activation function. In order to enhance the crop yield predictive performance, a leaky rectified linear unit is used in the activation units of MDNN. For the analysis of performance of DNN and MDNN, data about weather, crop and soil are collected from http://www.ccafs-climate.org/climatewizard/, https://data.world/thatzprem/agriculture-india and https://data.gov.in/search/site?query=soil respectively. From the collected data, 60000 records are used for training and 40,000 records are used for testing. Findings: By considering multiple parameters of climate and the effect of weather on crop yield, the accuracy of MDNN is improved for predicting the crop yield. The effectiveness of MDNN is tested and compared with DNN for different types of crops. The MDNN achieves 91.84% of mean accuracy for five different crops compared to the DNN classification. Novelty: This proposed work tries to predict the crop yield more accurately by analyzing the climate, weather and soil parameters. The MDNN considerably improves statistical efficiency over typical DNN by using previous knowledge about important phenomena and functional forms relating them to the crop yield. Keywords: Crop yield prediction; machine learning; DNN; climatic changes; soil parameters; growing degree-day

Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia

Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the yield prediction problem there exist the following inadequacies: Unable to create a direct non-linear or linear mapping between the raw data and crop yield values; and the performance of those models highly relies on the quality of the extracted features. Deep reinforcement learning provides direction and motivation for the aforementioned shortcomings. Combining the intelligence of reinforcement learning and deep learning, deep reinforcement learning builds a complete crop yield prediction framework that can map the raw data to the crop prediction values. The proposed work constructs a Deep Recurrent Q-Network model which is a Recurrent Neural Network deep learning algorithm over the Q-Learning reinforcement learning algorithm to forecast the crop yield. The sequentially stacked layers of Recurrent Neural network is fed by the data parameters. The Q- learning network constructs a crop yield prediction environment based on the input parameters. A linear layer maps the Recurrent Neural Network output values to the Q-values. The reinforcement learning agent incorporates a combination of parametric features with the threshold that assist in predicting crop yield. Finally, the agent receives an aggregate score for the actions performed by minimizing the error and maximizing the forecast accuracy. The proposed model efficiently predicts the crop yield outperforming existing models by preserving the original data distribution with an accuracy of 93.7%.

Crop yield prediction is vital for global foodstuffs and intensely challenging due to its multitudinous factors such as soil, weather, rainfall, temperature, and their interactions. The influence of weather changes in India impacted in terms of the agricultural crop outcomes over the last decades. The proposed research focuses on the weather condition and assists the farmers to comprehend the crop yields before growing onto farming. This research illustrates a deep learning framework using artificial neural networks (ANN). The offered ANN model, along with further widespread approaches such as bagging classifier, extra tees classifier, linear discriminant analysis (LDA) classifier, quadratic discriminant analysis (QDA) classifier and stochastic gradient boosting (SGB) classifier are used for India's downloaded crop dataset. These proposed six models achieved quite good accuracies. The numerous substantial contributions of the prediction models are the ability to produce the exact predictions. These were succeeded by training the algorithms and naturally to the verify data. Eventually, we tend to compare our study with the previous study's research paper.