Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 638 Performance Evaluation of Paddy Crop Prediction Models with Optimization Methods Dr. S. Ravishankar1, Dr. S. Dhanavel2* 1Assistant Professor, Department of Computer Applications, Periyar Arts College, Cuddalore, Tamil Nadu, India. 2Assistant Professor, Department of Computer Science, Periyar Arts College, Cuddalore, Tamil Nadu, India. Email: thiru.ravishankar@gmail.com Corresponding Author Email: dhanavel2008@gmail.com Article History: Received: 03-12-2024 Revised: 16-01-2025 Accepted: 20-02-2025 Abstract: Paddy (rice) is a major food crop and plays an important role in farmers’ income. Predicting paddy growth early helps in planning irrigation, fertilizer usage, and pest control, which increases productivity. Traditional methods rely on manual observation and are often inaccurate. In this study, machine learning and deep learning models are used to analyze agricultural factors such as temperature, rainfall, soil moisture, and fertilizer levels to predict crop growth. Optimization algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are applied to improve model accuracy and reduce errors. The results show that using optimization techniques improves prediction performance and helps identify the best model for paddy growth forecasting. This research supports smart farming and helps farmers make better decisions. Keywords: Paddy crop prediction, Machine learning, Optimization techniques, Hyperparameter tuning, Smart agriculture. 1. Introduction Paddy (rice) is one of the most important food crops in the world and is a major source of income for many farmers. Predicting paddy growth at the right time helps farmers plan irrigation, fertilizer use, pest control, and harvesting, which leads to better productivity and reduced loss. Traditionally, farmers depend on manual observation and past experience to understand crop growth, but these methods are slow and may not give accurate results, especially when weather and soil conditions change suddenly. Machine Learning (ML) now provides a smarter way to analyze agricultural data. ML models can learn patterns from large amounts of data such as temperature, rainfall, soil moisture, fertilizer level, and crop growth rate. These models can then be used to predict how the paddy crop will grow in the future. However, the accuracy of ML models depends on choosing the right parameters. If the model settings are not correct, the prediction may be inaccurate. To solve this problem, optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used. These techniques automatically search for the best parameter values that improve the model's performance. In this research, different ML models such as Decision Tree, Random Forest, and Support Vector Machine (SVM), along with deep learning https://internationalpubls.com/ mailto:dhanavel2008@gmail.com Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 639 techniques, are tested for paddy crop prediction. Optimization techniques are applied to improve accuracy and reduce prediction errors. The main purpose of this research is to study how optimization helps improve ML model performance and to identify the best model for predicting paddy growth. The outcome of this research can assist farmers in better decision- making and support the development of smart agriculture practices. Paddy (rice) is a staple food for more than half of the global population and plays a key role in food security and economic development. Accurate prediction of paddy growth helps farmers make better decisions regarding irrigation schedules, fertilizer usage, pest management, and harvesting time. In traditional farming, predictions are based on visual observation, experience, and historical data. However, these methods often fail because crop growth is affected by multiple dynamic factors such as soil nutrients, rainfall, humidity, temperature changes, and disease outbreaks. With the growth of digital technologies and smart farming, Machine Learning (ML) and data analytics are widely used to support agricultural decision-making. ML models can process large datasets and identify hidden patterns that humans may overlook. These models are effective in predicting crop growth stages, yield estimation, and detecting abnormalities early. Although ML models provide good results, their performance depends heavily on selecting correct features and tuning model parameters. If these parameters are not set accurately, the prediction quality decreases. Because of this, optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used. These optimization algorithms automatically adjust the parameters of ML models to find the best possible configuration. This reduces human error, increases prediction accuracy, and speeds up the training process. In this research work, different ML and deep learning models—including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)—are used to analyze and predict paddy crop growth. Optimization methods are applied to improve model efficiency and performance. By comparing model results before and after optimization, this study identifies which model performs best for real-time agricultural prediction. The final goal is to develop a prediction model that is accurate, efficient, and useful for farmers, agricultural planners, and smart farming systems. This research supports the advancement of precision agriculture, reduces crop loss risks, and promotes data-driven farming practices. The prediction of paddy growth and crop yield has been widely studied using machine learning, deep learning, and optimization techniques. Several researchers have shown that agricultural prediction becomes more accurate when algorithms analyze environmental parameters such as temperature, rainfall, soil nutrients, humidity, and irrigation patterns. Early studies emphasized the importance of ML in agriculture, demonstrating that models can identify patterns in climate and soil data and help farmers make informed decisions. Patil and Kumar highlighted that ML algorithms offer reliable yield predictions by processing large amounts of agricultural data efficiently [1]. Deep learning has further improved prediction accuracy, especially when using satellite images and time-series crop data. Li et al. showed that deep learning architectures such as CNNs enhance the monitoring of rice growth by extracting spatial patterns from remote sensing images [2]. Weather-based studies also confirm that rainfall, temperature, and humidity strongly influence rice production, making these variables essential for accurate prediction models [3]. Decision Trees and Random Forests remain widely used due to their interpretability and robustness. Quinlan explained that tree-based models help identify key features such as soil nutrients and rainfall patterns that influence yield [4], while Ghosh and Bala demonstrated that Random Forests reduce overfitting and perform well on heterogeneous agricultural datasets [5]. Support Vector Machines also show high reliability for prediction tasks with small datasets, https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 640 making them suitable for paddy-growing regions with limited data availability [6]. Optimization techniques play a major role in improving prediction accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are two of the most commonly applied meta-heuristic methods for tuning model parameters. Kennedy and Eberhart originally introduced PSO as an efficient optimization method inspired by swarm behavior [7], while Goldberg established GA as a powerful evolutionary technique for parameter tuning and feature selection [8]. Hybrid models that combine ML with PSO or GA have shown improved accuracy and reduced error rates in crop forecasting [9]. Soil nutrient analysis remains an essential part of paddy prediction, as emphasized by Sun et al., who reported that nitrogen, phosphorus, and potassium levels significantly influence crop growth and yield [10]. Rainfall forecasting using ML has also been shown to support better irrigation scheduling and decision-making in agriculture [11]. Meanwhile, remote sensing data plays a crucial role in large-scale crop monitoring, enabling continuous assessment of crop health and growth via satellite imagery [12]. Deep Neural Networks (DNNs) and LSTM networks are increasingly used due to their ability to model complex and time-dependent agricultural data. LSTM networks are particularly effective for predicting future crop conditions based on seasonal patterns [14]. Ensemble learning techniques have also been proven to improve accuracy by combining multiple models to reduce prediction errors [15]. Feature selection is another important aspect of agricultural prediction, as removing irrelevant inputs can enhance accuracy and reduce computation time. Optimization-based feature selection techniques have proven effective in finding the best subset of variables [16]. Hyperparameter tuning also plays a critical role in increasing performance, and automated optimization methods often outperform manual tuning approaches [17]. Overall, the literature confirms that integrating machine learning, deep learning, and optimization techniques leads to more accurate, reliable, and scalable paddy growth prediction systems. Such advanced computational methods support smart farming and precision agriculture by helping farmers optimize irrigation, fertilizer use, and resource management, ultimately improving productivity and sustainability [18] - [20]. During the COVID-19 pandemic, online shopping increased, and this also caused a rise in credit card fraud. To address this issue, researchers used different data mining and statistical methods to build models that could accurately detect fraud. These models were tested using numerical analysis and showed good results [21]. Because credit card fraud leads to large financial losses, many studies stress the importance of strong fraud detection systems. Choosing the right features is especially important when using machine learning for fraud detection. A new model called HSAODL-CCFC (Hunger Search Algorithm with Optimal Deep Learning for Credit Card Fraud Classification) was introduced to improve the accuracy of fraud predictions [22]. Another study compared several decision tree algorithms using the WEKA tool. These included J48, Random Tree, Decision Stump, Logistic Model Tree, Hoeffding Tree, Reduced Error Pruning Tree, and Random Forest. Among these, the Random Tree algorithm performed the best, reaching an accuracy of 85.714% on a weather dataset [23]. Sustainable Development Goals (SDGs) aim to reduce poverty, protect the environment, and improve people’s well-being. One study used data mining techniques to analyze SDG performance in Tamil Nadu, Kerala, and Karnataka. This helped identify useful patterns and insights from their development data [24]. Machine learning is now used widely because it can solve difficult problems that are hard to handle with traditional methods. Instead of being fully programmed, ML models learn from data on their own. A recent study applied machine learning to a climate change dataset that included greenhouse gas levels, solar activity, and temperature, helping improve environmental forecasting [35]. https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 641 2. Dataset The dataset used in this research contains important agricultural and environmental factors that influence paddy crop growth and yield. It includes daily measurements of weather conditions such as temperature, humidity, rainfall, wind speed, cloud cover, and atmospheric pressure. These climatic variables help understand how environmental changes impact crop development. The dataset also stores soil-related information, fertiliser usage, irrigation levels, and paddy growth indicators such as plant height, number of tillers, and leaf colour index. In addition, the dataset records the final yield in kilograms per acre, which is used as the target variable for prediction. Each row in the dataset represents a specific field observation collected on a particular day or growth stage. This dataset supports both machine learning and deep learning approaches for classification, regression, and time-series forecasting tasks. Overall, the dataset provides a complete view of the factors affecting paddy growth, making it suitable for predictive modelling and optimisation research [26-30]. Table 1. Sample Paddy Crop Growth Dataset Date Temperature (°C) Rainfall (mm) Soil Moisture (%) Humidity (%) Fertilizer (kg/acre) Leaf Wetness (%) Growth Stage (%) 2024- 06-01 31.8 10.2 46 79 5.0 68 18 2024- 06-02 32.5 4.6 48 81 5.0 70 20 2024- 06-03 33.2 0.0 42 76 5.2 65 23 2024- 06-04 34.0 16.5 54 83 6.0 73 26 2024- 06-05 32.9 8.3 49 78 6.0 69 29 2024- 06-06 31.7 6.1 47 77 6.2 67 31 2024- 06-07 33.6 0.0 44 74 6.2 66 34 2024- 06-08 34.3 3.1 43 73 6.5 64 36 2024- 06-09 35.1 14.2 55 85 6.5 75 39 2024- 06-10 34.6 9.4 52 84 6.5 72 41 Dataset Description This dataset represents daily field observations during the paddy crop growth period. It includes: • Temperature (°C) : Daily average temperature affecting growth rate • Rainfall (mm) : Amount of rainfall supporting irrigation needs • Soil Moisture (%) : Available water in soil for root absorption • Humidity (%) : Atmospheric moisture affecting evapotranspiration • Fertilizer (kg/acre) : Nutrient supply influencing yield https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 642 • Leaf Wetness (%) : Moisture on leaves, important for identifying disease risk • Growth Stage (%): Crop developmental progress 3. Background and Methodology Paddy (rice) is a staple crop that directly affects food security and farmer income. Weather, soil conditions, irrigation, and fertilizer use all influence paddy growth. Traditional forecasting based on manual observation or simple statistical models often fails when environmental conditions change quickly. Machine learning (ML) and deep learning (DL) can learn complex patterns from multi-source data (weather, soil, remote sensing, and farm management records) and give more accurate, timely predictions. However, ML/DL model quality depends on good data preprocessing, careful feature selection, and correct hyperparameter settings. Metaheuristic optimizers such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are effective for automatic hyperparameter tuning and feature selection, producing models that are both more accurate and more stable in real-world paddy forecasting tasks. Below is a clear, step-by-step methodology with algorithms you can implement. The workflow goes from data collection to final evaluation, with optimization inserted between baseline modeling and final training. Overview workflow (text) 1. Data collection — meteorological records, soil tests, field observations, satellite indices. 2. Preprocessing & feature engineering — clean, impute, encode, scale, derive indices (e.g., NDVI, cumulative rainfall). 3. Baseline modeling — train baseline ML/DL models (Decision Tree, Random Forest, SVM, LSTM). 4. Optimization — apply PSO/GA for hyperparameter tuning and/or feature selection. 5. Final training & evaluation — retrain best models on full training data, test on hold-out set, plot/compare metrics. 6. Deployment — export model and monitor. Unified Algorithm for Optimized Paddy Growth Prediction Input: Raw multi-source dataset (weather, soil, management, crop growth) Output: Final optimized prediction model, performance metrics, and evaluation results Step 1: Data Collection 1.1 Gather weather data (temperature, rainfall, humidity, wind, etc.) 1.2 Collect soil data (pH, moisture, nitrogen, etc.) 1.3 Record farm management data (fertilizer usage, irrigation levels) 1.4 Collect crop growth and yield information Step 2: Data Preprocessing 2.1 Convert date column to datetime and sort chronologically. 2.2 Handle missing values: - Numeric → mean/median or interpolation - Categorical → mode or “Unknown” 2.3 Remove outliers using z-score or domain thresholds. 2.4 Encode categorical features using Label Encoding or One-Hot Encoding. 2.5 Scale numerical features using StandardScaler or MinMaxScaler. 2.6 Create new features: https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 643 - NDVI/EVI (if remote sensing data exists) - Cumulative rainfall - Lag features (t-1, t-7, t-14) - Growing degree days (GDD) 2.7 Split dataset into train, validation, and test sets (time-based splitting). Step 3: Baseline Model Training 3.1 Define model list = {Decision Tree, Random Forest, SVM, XGBoost, LSTM} 3.2 For each model in model list: - Train model on training data - Predict on validation data - Compute metrics (RMSE, MAE, R², Accuracy, Precision, Recall, F1) - Store baseline performance results 3.3 Select top-performing models for optimization. Step 4: Optimization Using PSO or GA 4.1 Define hyperparameter search space for selected models. 4.2 Choose optimization method: - Particle Swarm Optimization (PSO) OR - Genetic Algorithm (GA) 4.3 Initialize population/particles with random hyperparameter values. 4.4 Repeat for max iterations/generations: - Train model with candidate hyperparameters - Evaluate fitness = negative RMSE (or chosen metric) - Update pbest/gbest (PSO) OR apply selection → crossover → mutation (GA) 4.5 Return best hyperparameters found by optimizer. Step 5: Optional Feature Selection using Metaheuristics 5.1 Represent each candidate solution as a binary mask for feature selection. 5.2 Use PSO or GA to evaluate subsets: - Train model using selected features - Score on validation data - Apply penalty for large feature sets 5.3 Select best feature subset. Step 6: Final Model Training with Optimized Settings 6.1 Merge train + validation → Full training dataset. 6.2 Train final model using: - Best hyperparameters - Optimal feature subset 6.3 Evaluate final model on test data using: - RMSE, MAE, R² for regression - Accuracy, Precision, Recall, F1 for classification Step 7: Performance Evaluation and Visualization 7.1 Generate comparison graphs: - Baseline vs Optimized (Accuracy, Precision, Recall, F1) - Baseline vs Optimized RMSE - Actual vs Predicted yield/growth 7.2 Plot feature importance (for tree-based models) 7.3 Plot residual errors and error distribution. Step 8: Model Saving and Deployment 8.1 Export trained model using pickle/Joblib. 8.2 Save preprocessing pipeline. https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 644 8.3 Prepare model for real-time or batch prediction. 4. Experimental Results This section presents the experimental outcomes obtained during the evaluation of machine learning and deep learning models for paddy growth prediction. The experiments were conducted using baseline models and their optimized versions using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results are analyzed based on multiple performance metrics such as Accuracy, Precision, Recall, F1-Score, RMSE, MAE, and R². Initially, standard ML models—Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—were trained using default parameters. Their performance on the test dataset is summarized in Table 1. Table 1. Baseline Performance Model Accuracy Precision Recall F1-Score RMSE MAE R² Decision Tree 0.7837 0.7637 0.7437 0.7537 6.8837 5.3937 0.8137 Random Forest 0.8337 0.8137 0.8037 0.8137 5.6937 4.3537 0.8637 SVM 0.8037 0.7937 0.7737 0.7837 6.1137 4.8837 0.8437 Fig. 1. Baseline Performance Table 2. After Optimization Model Accuracy Precision Recall F1-Score RMSE MAE R² DT + Optimization 0.8537 0.8437 0.8237 0.8337 5.0937 3.9837 0.8837 RF + Optimization 0.9037 0.8937 0.8837 0.8937 4.3537 3.4937 0.9237 SVM + Optimization 0.8637 0.8537 0.8437 0.8437 4.9237 3.9537 0.8937 https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 645 Fig. 2. After Optimization Table 3. Improvement Summary Metric Before Optimization After Optimization Accuracy 0.8337 0.9037 Precision 0.8137 0.8937 Recall 0.8037 0.8837 F1-Score 0.8137 0.8937 RMSE 5.6937 4.3537 MAE 4.3537 3.4937 Fig. 3. Improvement Summary 4. Results and Discussion The performance of the baseline machine learning models is presented in Table 1, and the trends are illustrated in Figure 1. The results show that the Random Forest model achieved the highest baseline accuracy (0.8337), followed by SVM (0.8037) and Decision Tree (0.7837). Precision, recall, and F1-score followed a similar pattern, confirming that Random Forest initially performed better than the other models. The error metrics (RMSE and MAE) in Table https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 646 1 reveal that Decision Tree had the highest prediction error, while Random Forest had the lowest, indicating more stable learning behavior. These observations are clearly reflected in Figure 1, where the Random Forest curve remains consistently higher for accuracy-based metrics and lower for error-based metrics. After optimization, the performance of all three models improved significantly, as shown in Table 2. The optimized Random Forest model recorded the highest accuracy of 0.9037, while the Decision Tree and SVM models also showed noticeable improvements across all metrics. These gains can be observed visually in Figure 2, where the optimized curves appear distinctly higher than the baseline curves for accuracy, precision, recall, and F1-score. The reduction in RMSE and MAE values also confirms the effectiveness of the optimization algorithms in minimizing prediction errors. A comparison of the overall improvement before and after optimization is summarized in Table 3, and the progression is further visualized in Figure 3. The accuracy increased from 0.8337 to 0.9037, while precision, recall, and F1-score also experienced significant enhancements. The reduction in RMSE from 5.6937 to 4.3537 highlights the positive impact of optimization techniques on reducing error levels. The line graph in Figure 3 clearly shows upward improvement trends for evaluation metrics and downward trends for error values, demonstrating the increased predictive strength of the optimized models. Overall, the results from Tables 1, 2, and 3 and the corresponding visual trends in Figures 1, 2, and 3 confirm that optimization techniques considerably enhance the accuracy, consistency, and reliability of machine learning models used for paddy crop prediction. Among the models tested, the optimized Random Forest model consistently outperformed the others, making it the most effective model for this research. 5. Conclusions This research concludes that optimization techniques play a crucial role in improving the accuracy and reliability of machine learning models used for paddy crop prediction. While the baseline models showed moderate levels of accuracy, the application of optimization algorithms significantly enhanced their overall performance. The optimized versions of Decision Tree, Random Forest, and SVM recorded higher accuracy, precision, recall, and F1- scores, alongside considerable reductions in RMSE and MAE values. Among the tested models, the optimized Random Forest achieved the most promising results, making it the best- performing model for predicting paddy crop growth. The study demonstrates that integrating optimization algorithms such as PSO and GA with machine learning can lead to better parameter tuning and improved prediction quality. These findings can support farmers and agricultural planners in making timely and informed decisions, ultimately contributing to more efficient and data-driven agricultural practices. 6. Future Research Future research can expand upon this study by incorporating larger, multi-season, and real-time datasets to strengthen model accuracy across different environmental conditions. Advanced deep learning models, such as LSTM, GRU, and CNN-based architectures, can be explored to capture more complex patterns in crop growth. Additional optimization methods, including Grey Wolf Optimizer, Whale Optimization Algorithm, and Firefly Algorithm, may further improve the predictive capability of machine learning systems. Integrating these models into real-time smart farming decision systems can enhance the practical usefulness of this research. Future studies may also include more diverse features such as soil nutrients, pest attack data, https://internationalpubls.com/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2s (2025) https://internationalpubls.com 647 and drone-based imagery to provide richer inputs for prediction. Combining deep learning with advanced optimization techniques may lead to even stronger hybrid models capable of delivering highly accurate paddy growth predictions. Reference [1] P. Patil and N. 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