A hybrid CNN-LSTM deep learning framework for enhanced crop yield prediction using spatial-temporal agricultural data

Introduction

The productivity of agriculture is a pivotal factor of food security, economic stability and sustainable rural development. As the world population is estimated to exceed nine billion by 2050, the need to have a sound and reliable crop yield prediction systems has been of high priority [1] . Conventional approaches to be used on the basis of statistical modelling and fieldbased measurements usually do not reflect the complexity and nonlinear interactions between climatic variables, soil attributes, crop phenology and crop management strategies. With the shift in the agricultural field into a data-driven field, there is potential in the merging of modern computational methods, particularly deep-learning, to improve the accuracy and quality of predictions and decisions in crop production systems [2] . Over the past several years, high-spatial and temporal agricultural sources of remote sensing systems, weather stations, drones, and IoT-enabled sensors of the field have become available, which has made it possible to develop high-performance prediction models [3,4] .

Nonetheless, engineering such datasets has been found to be quite challenging due to high dimensionality, noise, nonperiodic temporal variation and dynamic nature of natural environments. Convolutional Neural Networks (CNNs) have been shown to be particularly effective at learning spatial features in satellite images, vegetation indices, and spectral signatures and Long Short-Term Memory (LSTM) networks have been shown to learn long-range temporal dependencies, including rainfall patterns, temperature trends and crop growth cycles. Single model architectures might fail to effectively model the natural relation between spatial and time dimensions of agricultural information even though they have their respective advantages [5] . In order to overcome these constraints, hybrid deep learning systems have become more popular in the engineering and agricultural informatics community. Hybrid CNNLSTM framework is a strategic implementation to merge CNN and LSTM layers by providing spatial feature extraction and sequential learning of time dependencies respectively [6] . This interaction enables the model to learn intricate nonlinear associations more prosperously giving a complete picture of crop development due to environmental variability. Hybrid architectures can achieve high yields in forecasting yields when used with datasets like NDVI sequences, multi-band remote sensing images, soil parameter maps, and time-series weather data, and much higher than traditional machine learning techniques could. In addition, the engineering innovation in data pre-processing, feature fusion, and model optimization methods lead to enhancing the strength and generalization ability of hybrid models [7,8] . To take a few examples, spatial interpolation, normalization pipelines and dimensionality reduction can be used to clean up the quality of inputs, whereas model efficiency can be increased by hyperparameter tuning methods, including Bayesian optimization or grid search. This can be further enhanced by the combination of cloud computing and training enabled using a GPU to quickly and efficiently process vast amounts of agricultural data in realtime or near real-time, enabling scalable deployment to precision agriculture systems. The possible advantages of improved prediction of crop yield are not limited to scholarly studies and technology development. Proper forecasting is also used to make informed decisions, such as the type of crops to choose, when to irrigate the fields, how much to use in terms of fertilizer, pest management and planning the relevant market. As an engineer, creating an optimal hybrid deep learning system follows the larger objectives of creating resilient, intelligent, and sustainable agricultural solutions. Therefore, the proposed research explores a Hybrid CNN-LSTM deep learning model to leverage spatial-temporal agricultural datasets and deliver improved crop yield prediction accuracy. The study aims to bridge the gap between advanced computational intelligence methods and their practical applicability in modern agriculture.

Related work

The prediction of crop yield has emerged as a critical research area due to the rapid advancements in data-driven agriculture, sensor technology, and computational modelling. The literature on crop yield prediction widely shows the path of progression of use of traditional machine learning models to more advanced deep learning models that effectively represent spatial-temporal agricultural trends. The early research mostly centered on the models of traditional machine learning and this model formed the basis of predicting yields on the basis of soil, weather, and crop-related variables. The literature on random forest models is quite widespread due to their strength and capability of dealing with nonlinear relationships. As an example, Curtis J Ransom [2] compared different machine learning models to soil and climate characteristics in the U.S. Midwest and discovered that random forest was more successful when compared to stepwise and ridge regression. In the same manner, M. Kuradusenge et al. [3] applied random forest and support vegetable regression to accurately predict Irish potato and corn yields in Rwanda through the incorporation of the weather history data. Nikhil [4] used classical machine learning models to estimate the yields of rice, sorghum, cotton, and sugarcane on Indian states and indicated that Extra Trees Regressor yielded better outcomes. All these works made the ensemble-based learners to be relevant in the agricultural prediction tasks. The K-Nearest Neighbour (KNN) technique is another very common traditional method. In the study of Suresh et al. [5] , the K-means clustering algorithm was used with a variation of KNN prediction to forecast the crop yields in Tamil Nadu, India. Karn [6] used a novel crop recommendation system based on KNN algorithm on a Kaggle dataset (22 variables and 2200 records). The application of KNN to crop recommendation was also done by Kumar et al. [7] , who showed that simple distance-based classifiers might work effectively in case data sets are of moderately large size, and characteristics are organized in a reasonably structured way. These approaches, though successful in some situations, usually did not have the ability to deal with complex and high dimensional data, and the transition to more sophisticated neural-based models was initiated. The neural network methods were an extension of the previous models in terms of predictive power because they allowed learning nonlinear features. The yield of maize, sorghum, millet, and peanuts were predicted in Senegal in reference to neural networks, SVM, and random forest models by Sarr et al. [8] , but they did not include soil-related variables in their dataset. Das [9] has further enhanced yield prediction by using a hybrid approach, which involved the use of MARS-based feature selection and neural network or SVR, which was used on lentil crop data. In the eastern region of Kazakhstan, Sadenova et al. [10] used the features derived by remote sensing to construct several prediction models and the neural networks performed better. These experiments indicated that neural network-based models were capable of deriving patterns that were more meaningful than shallow learners, although it nonetheless necessitated a structured engineering of features. Researchers have also actively pursued gradient boosting methods because it is ensemble-based and it generally performs better in most tabular prediction problems. Shahhosseini et al. [11] discovered that XGBoost was the most precise model in predicting the yield of maize and estimating the loss of nitrate through a factorial simulation dataset. Elsewhere, P. Mishra et al. [12] were using gradient boosting regression to forecast crop yields in France, and Pradeep et al. [13] used the same methods to predict their yields across boards in India. Abdel-Salam et al. [14] used feature selection and optimized SVR to improve yield prediction using agricultural datasets sources by the government. Collectively, these classic machine learning researches highlight the switch to more complicated deep learning structures that can utilize big, unstructured data. As the fields of remote sensing, internet of things sensor, and big agricultural databases advanced, deep learning-based techniques became more popular in the crop yield modelling field. In contrast to traditional models, deep learning architectures automatically learn hierarchical features, hence being adapted to large-scale data in space and time. CNNs were popularized and used in extracting spatial patterns. A deep neural network model was used by Khaki et al. [15] to predict corn yield in 2247 locations in the United States, based on the environmental variables. In Kim et al. [16] , the authors applied CNN-based models, which integrated both satellite products and meteorological data to forecast crop yield in the U.S. Midwest 2006 2015. The researchers by Kumar et al. [17] suggested a hybrid deep capsule autoencoder (DCAS) to predict yields, based on data of the Agra district of India. The weight-tuned deep convolutional neural network (WTDCNN) and dimensionality reduction methodology was proposed by Subramaniam et al. [18] to predict regional yields in crops in India. The CNN-based works show that spatial correlations like soil distribution, changes in vegetation indices, and climatic patterns can be obtained. The CNN models treat the spatial aspect, whereas the Long Short-Term Memory (LSTM) networks handle the time-based aspects like the rainfall patterns, temperature cycles and crop maturity cycles. In predicting the yield of wheat in China, Henan Province, Shen et al. [19] utilized multispectral and thermal remote sensing data combined with random forest (LSTM-RF). Bhimavarapu et al. [20] proposed a better LSTM optimization operational to minimize training and testing errors on the Indian crops datasets. Wang et al. [21] applied the LSTMs to predict the wheat yields through remote sensing time-series to predict the Henan Province, whereas Di et al. [22] applied Bayesian optimized LSTM (BO-LSTM) to combine multi-source meteorological and satellite data in Hebei Province. DeepCropNet (DCN) is a model that predicts the yield of corn in the county level in the U.S. and allows prediction of hierarchical time-dependent features, created by Lin et al. [23] . Haider et al. [24] applied the LSTM models to predict the wheat yields in Pakistan using the Federal Bureau of Statistics data. These articles are good evidence of the ability of LSTM to model time-related attributes of agricultural systems. Later with the advancement of research, there was an increased significance of hybrid models that combine CNN and LSTM since the two learn together spatial and temporal information. Khaki et al. [25] have suggested that a hybrid CNN-LSTM model would be useful in the accurate prediction of yields in the U.S. Corn Belt, which is superior to standalone CNN models, LASSO, and random forest. Saini et al. [26] developed a CNN-BI-LSTM-CYP model to predict the sugarcane yield based on the data of the key Indian states. Boppudi et al. [27] applied a better feature ranking fusion methodology based on a hybrid LSTM-DBN to predict the yield in India. The hybrid studies show that integration of spatial capabilities of CNNs and temporal abilities of LSTMs can be used to achieve much higher prediction accuracy particularly when large-scale spatial-temporal agricultural data are used. In addition to CNN-LSTM models, newer models investigated graph-based deep learning. Fan et al. [28] proposed a hybrid architecture of GNN-RNN to consider geospatial relationships, which was a significant weakness of the previous models, which considered geographical units as independent samples. By integrating Graph Neural Networks (GNNs) with LSTMs, their approach operationalized spatial adjacency information and delivered superior results compared to existing deep learning methods. This evolution toward spatial-temporal-geographical models reflect the growing emphasis on developing more context-aware and holistic crop yield prediction frameworks.

Research Methodology 3.1 Study Design Overview

This study adopted an integrated spatial-temporal modelling framework that leveraged multisource remote-sensing data, sequential climatic variables, soil properties, and ground-truth crop yield information to construct a hybrid deep learning model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The study design was conceptualized to capture both spatial variability in vegetation cover and landscape dynamics, as well as temporal fluctuations in climate parameters that influence crop growth across different stages of the crop season. The workflow involved several stages, beginning with data acquisition from satellite archives, weather monitoring systems, and soil databases, followed by pre-processing processes including cleaning, normalization, and alignment of multi-frequency datasets. Spatial features were derived from satellite products through image processing and vegetation index computation, while temporal features were extracted from sequential climatic and crop calendar data. The hybrid CNN-LSTM architecture was then designed to simultaneously process spatial image tensors and temporal sequences. The final stage of the study included model training, crossvalidation, performance comparison with baseline machine learning models, and evaluation of prediction accuracy using multiple statistical metrics. This design enabled the model to simulate complex interactions between vegetation patterns and environmental drivers, ultimately improving the accuracy of crop yield prediction at a regional scale.

Data Collection 3.2.1 Satellite Imagery

The spatial data of this research was the satellites that served as the main source of information, and two significant sources of this information were used: the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2. MODIS also offered high 250meter resolution images in 16 day composites which was appropriate in monitoring long term vegetation of large agricultural areas. Sentinel-2, a satellite run by the European Space Agency, provided more detailed (10 m) images at 5 days revisit rates, which facilitated a more detailed analysis of the field-level dynamics of vegetation. Key vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) were obtained based on both satellites to determine the health and development of crops and canopies. The satellite data was 10 years based on large agricultural districts in order to capture interannual deviation in climatic conditions, cropping and management practices. These indices could be used in determining growth phases, identifying stress regimes, and estimating biomass gain, which are important attributes used in predicting yield.

Weather and Climate Data

NASA POWER and NOAA meteorological data were accessed on a daily basis as part of the collection of information about the weather and the climate. These sources gave vital parameters of minimum and maximum temperature, rainfall, relative humidity, speed of wind and solar radiations, which are crucial in determining the physiological processes of plants. The temperature data played a significant role in determining the growing degree days (GDD) and water availability and water evapotranspiration dynamics was determined by rainfall and humidity. The Solar radiation was used to define the activity of photosynthesis and was critical in simulating the energy balance and growth rates. The satellite data was gathered over the 10 years period to ensure that the weather data were also taken during that period. Through the combination of the high resolution temporal data and the satellite determined measures of the vegetation, this research attempted to build a model that was able to reflect the intricate interplay between climatic variants and crop productivity.

Soil and Crop Data

The ISRIC SoilGrids and the USDA SSURGO database were used to obtain soil characteristics, such as soils geospatial layers, with the details of soil-related characteristics, such as pH, the percentage of nitrogen or organic carbon, and the texture of the soil. These parameters were needed to know the nutrient availability, ability to retain moisture and generally the fertility of soil and all these directly influence crop productivity. The one-hot encoding of soil texture was done to enable the use of soil texture in conjunction with numerical features. Crop specific variables such as variety, date of sowing, irrigation techniques, and practices were sourced in the Kaggle Crop Yield Dataset and confirmed in state agricultural department records. These variables of crops were used as contextual inputs which were useful in narrowing the interpretation of both the satellite-derived and temporally changing features.

Ground Truth Yield Data

The ground truth data concerning the yield of crop was taken with respect to the government agricultural statistics of the district and remote-sensing yield estimation reports. Fieldlevel yields were also added where possible to enhance granularity and modeling quality. To achieve consistency in the comparison between locations and years, crop yields were normalized in kg /ha. The target variable used to train the deep learning model was yield data and thus allowed the deep learning model to learn supervised and assess the performance of prediction. The combination of ground truth, satellite and climatic data made the model results valid.

Data Pre-processing 3.3.1 Data Cleaning

Cleaning of data was done through missing values, elimination of inconsistencies, and low-quality observations. Loss of climatic data especially rainfall and humidity gap due to the failure of equipment or due to the cloud cover was filled in K-Nearest Neighbour (KNN) imputation. This procedure saved natural correlation between climate variables and little distortion of times was achieved. The quality assurance (QA) bands of MODIS and Sentinel-2 were used to filter the cloud-contaminated pixels in remote-sensing imagery, which refer to cloud cover, haze, shadow, and sensor noise. Clear-sky pixels were only used to calculate the vegetation index so that there was no uncertainty in the spatial feature.

Data Normalization

All the numerical variables such as NDVI, rainfall, temperature, soil nutrients, and values of radiation were standardized to a 0-1 scale using Min-Max scaling. It was necessary to normalize the variables with large magnitudes to avoid domination of the learning process by them, particularly in neural networks where the activation functions are responsive to the scale of inputs. This move contributed to stability in training and a faster conclusion as well.

Temporal Alignment

Because datasets had different sources with varying temporal frequencies, i.e. daily weather data, weekly soil moisture estimates and biweekly satellite composites, the records were harmonized to weekly intervals. All data sets were synchronized with the stages of crop growth, beginning with sowing to harvest. Temporal grids were built so as to create sequences that can be processed by LSTM. This alignment made the input features count on same time periods in the crop cycle thus enhancing the time pattern recognition capability of the model.

Image Processing for CNN

Satellite images have been transformed to a standardized dimension of 128 x 128 pixels so that they can be compatible with CNN input requirements. The artificially increasing the dataset and preventing overfitting was done in the form of augmentation techniques including rotation, horizontal and vertical flipping, brightness and contrast changes. There were tensors of images that were normalized and stacked in time sequence so that the CNN could get spatial patterns of the structure of crop canopies at a given time.

Feature Engineering 3.4.1 Climatic Features

Climatic features were engineered to capture long-term and short-term fluctuations affecting crop growth. These included cumulative rainfall, weekly mean temperature, temperature extremes (maximum and minimum), and vapor pressure deficit derived from humidity data. These features were crucial for representing water stress, heat stress, and energy availability during the crop season.

Vegetation Indices

Vegetation indices such as NDVI, EVI, Leaf Area Index (LAI), and Canopy Water Content were extracted from processed satellite imagery. NDVI time-series were particularly important as they reflect chlorophyll activity, canopy vigour, and biomass accumulation. LAI served as a proxy for leaf density, whereas canopy water content provided insights into drought stress and transpiration rates.

Soil Features

Soil pH, nitrogen content, organic carbon, and soil texture were incorporated as static features. These properties influence nutrient availability and water retention, which directly affect yield potential. Soil texture classes were converted to categorical encodings to facilitate integration with the model.

Temporal Indicators

Temporal indicators included week of year, days after sowing (DAS), and growing degree days (GDD). GDD captured heat accumulation across the growing season and served as an important predictor for phenological progression in crops.

Dimensionality Reduction

Principal Component Analysis (PCA) was applied to highdimensional soil and climatic features to reduce redundancy and noise. Additionally, autoencoders were used to compress NDVI time-series into low-dimensional latent vectors, enhancing model efficiency and reducing computation time while retaining crucial temporal patterns.

Model Architecture 3.5.1 CNN Component (Spatial Learning)

The CNN module consisted of three convolutional layers with 3 × 3 kernels and ReLU activation to extract spatial features representing canopy structure and vegetation distribution. Each convolutional layer was followed by a max-pooling layer to reduce spatial dimensions while retaining dominant features. The final feature maps were flattened and passed through a dense layer to generate spatial embeddings.

LSTM Component (Temporal Learning)

The temporal module included two stacked LSTM layers, each with 128 units, designed to learn sequential dependencies in weather, soil, and vegetation index timeseries. A dropout layer with a rate of 0.3 was applied to prevent overfitting by randomly deactivating neurons during training. The output from the LSTM was passed through a dense layer to produce temporal embeddings.

Hybrid CNN-LSTM Fusion

Spatial and temporal embeddings were concatenated to create a comprehensive representation of crop growth conditions. This merged vector was processed by fully connected layers that learned complex interactions between spatial and temporal information. The output layer used linear activation to generate yield predictions in kg/ha.

Model Hyperparameters

The model was optimized using the Adam optimizer with a learning rate of 0.001. Mean Squared Error (MSE) served as the loss function due to its sensitivity to larger errors. Training was carried out with a batch size of 32 over 100 epochs, with early stopping applied when validation loss plateaued to prevent overfitting.

Model Training and Validation 3.6.1 Dataset Partitioning

The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. The training dataset was used for model learning, while the validation set helped in fine-tuning hyperparameters. The testing subset provided an unbiased evaluation of model performance.

Evaluation Metrics

Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R² score, and mean prediction deviation (%). RMSE and MAE quantified absolute deviations between predicted and observed yields, while R² assessed the proportion of variance explained by the model.

Cross-Validation

Five-fold cross-validation was performed to assess model generalizability across different regions and cropping seasons. This process ensured robustness, reduced bias, and the model's stability under varying environmental conditions.

Results and Analysis 4.1 Overview of Model Evaluation

The findings of this paper were made upon a thorough analysis of the hybrid CNN-LSTM deep learning system on integrated spatial-temporal agricultural data sets. The datasets covered field-level vegetation indices (NDVI), climatic variables (rainfall and temperature) and soil parameters (pH and nitrogen content) and weekly phenological indicators. The dataset had five types of crops, and these were wheat, maize, rice, soybean, and another wheat field, which gave a good balance in the validation of the models. All the field records had essential predictors affecting yield outcomes including NDVI values averaging between 0.60 and 0.85, rainfall of 430 mm to 940 mm, temperature of 20.8 0 C to 26.7 0 C, soil pH of 6.2 to 6.9, and soil nitrogen of 0.28-0.40 per cent. These characteristics allowed a multidimensional analysis of the variability of crop yields in fields and seasons. These input features were used to train the hybrid model which was then tested on unknown data to predict accuracy. Measures of RMSE, MAE, R 2 value, as well as percentage error deviation were calculated, and they provided a solid test of the magnitude of error and the variance explained. The CNN-LSTM model was better than all baselines, showing a higher capacity to identify spatial differences based on satellite-based NDVI and temporal variations based on climatic and phenological data. These performance trends are indicative of the effectiveness of spatial temporal fusion strategies in modelling agronomic processes since these processes are dynamic in terms of their time and space variability.

Performance of the CNN-LSTM Model

The CNN-LSTM hybrid model had the best prediction power of all the tested structures. The model had an error of 186.5 kg/ha, which is remarkably low when it is applied to predicting the yield in various crop types and environmental conditions. The size of the prediction error of most of the few individual observations was also supported by the MAE of 122.3 kg/ha, which indicated the small size of the prediction error. Most significantly, the R 2 value of 092 indicated that 92 percent of the variable of crop yields could be accounted by the model, meaning that there was a strong linear relationship between the predicted and the actual value of yield. The predicted values were always consistent with observed yields when applied on test fields namely, wheat field F101 (3700 kg/ha), F105 (3850 kg/ha), maize field F102 (4200 kg/ha), rice field F103 (4900 kg/ha) and soybean field F104 (3400 kg/ha). Indicatively, the model was able to project the high yields in field F103, with NDVI of 0.85 and rainfall of 940 mm indicating the good vigor of the vegetation and the optimum water condition. Equally, the reduced yields of soybean in F104 were rightfully recorded because of the low NDVI (0.60) and average rainfall (480 mm). These findings proved the sensitivity of the model to changes in vegetation and meteorological variation. Both figures visually demonstrated that yield values increased with higher NDVI levels, a relationship the CNN-LSTM model captured with minimal error. The regression line showed a tight clustering of prediction points, reinforcing the strength of the predicted-actual alignment.

Comparison with Other Architectures

To better understand the relative performance of the hybrid model, additional models including LSTM-only, CNN-only, Random Forest (RF), and Support Vector Regression (SVR) were trained and evaluated using identical datasets and preprocessing pipelines. The LSTM-only model managed to capture temporal dependencies in climatic variables, producing an R² of 0.88 with an RMSE of 241.0 kg/ha, reflecting moderate accuracy but limited spatial feature understanding. The CNN-only model performed less effectively, achieving an R² of 0.82 and RMSE of 310.4 kg/ha, owing to its inability to integrate multi-week climatic and phenological sequences crucial for crop development. The machine learning baselines (RF and SVR) underperformed relative to deep learning architectures. The SVR model's R² settled around 0.79, and the RF model's around 0.84, affirming that while tree-based models capture nonlinearities, they lack the capacity to generalize across complex, multivariate agricultural environments. The CNN-LSTM's integration of spatial-temporal data resulted in far superior performance, with notable reductions in both RMSE and MAE.

Feature Importance and Explainability

Feature importance analysis was performed using SHAP values derived from the Random Forest model, serving as a baseline interpretability reference. NDVI emerged as the most influential predictor, followed by rainfall and temperature, confirming the dominant role of vegetation health and climatic conditions in determining yield outcomes. Soil nitrogen ranked fourth, underscoring its importance for biomass production but reflecting lower overall variability compared to NDVI or rainfall. The influence of sowing week was also significant. Fields sown earlier in weeks with higher rainfall availability-such as wheat fields sown in week 11 and 12-displayed stronger yield outcomes. This interaction effect illustrates the importance of phenological timing, where climatic favorability during early growth phases produces long-term yield benefits. Feature ranking summary: The heatmap visually emphasized correlations between yield and climate-vegetation features. NDVI showed the strongest linear correlation with yield, while rainfall displayed moderate but positive correlation. Soil pH had minimal correlation, consistent with its relatively stable values across fields.

Statistical Significance Testing

To establish the predictive reliability, paired t-tests were carried out on the predicted yield and actual observed yield values in each of the models. The CNN-LSTM achieved a p value of above 0.05 which holds no statistically significant difference between predictions and real world yields. This confirms the accuracy of the model in decision-making and to be used in reality. Contrary to this, both RF and SVR models yielded p<0.01, which is the indication that they have significant deviations between the projected and actual yields and that they cannot predict the yields with high accuracy in complex environments. These findings statistically support the effectiveness of the hybrid strategy of deep learning.

Interpretation of the Hypothetical Dataset

The simulated data set also gave us some idea of variation of crop and field yield. The crop with the best yield (4900 kg/ha) was rice (Field F103) because it had the best NDVI (0.85), maximum rainfall (940 mm), and optimum temperature (26.7O C). The yields of wheat F101 and F105 were in the middle range of 37003850 kg/ha, which was in line with the mid-range NDVI and rainfall values. Soybean (F104) had the lowest yield (3400 kg/ha), which is associated with the minimum NDVI (0.60). These distributions established the fact that the direct measurable influences of NDVI, rainfall, and temperature on yield outcomes-relationships that were highly replicated by CNN-LSTM model.

Discussion

The results of the present research also eloquently reveal the high promise of incorporating both spatial and time-related information into a hybrid CNN-LSTM deep learning system as a sure way of making credible predictions of crop-yield outcomes. The capability of the model to take into account vegetation indices, climatic variables, soil properties, and phenological information enabled the model to reflect complex, multi-dimensional relationships that conventional machine learning models are unable to reflect. The large RS of 0.92 and the low RMSE of 186.5 kg/ha indicate the effectiveness of the model in identifying patterns of yield determinants in various crop types and environmental conditions. This high quality suggests that deep learning systems with the capability of processing sequential and image-convolved features at the same time are better adapted to agronomic data, which is seasonal and spatially heterogeneous.

One of the most important conclusions of the analysis was that the correlation between NDVI and crop yield was strong, which is visually represented by heatmaps and scatter plots. Fields that had high NDVI were always more productive, particularly in rice where the NDVI had a maximum of 0.85 and the yield of rice had an optimum of 4900 kg/ha. This implies that satellite-based vegetation indices are still one of the best predictors of crop vigor and biomass gain. The second predictor with the strongest correlation was rainfall, and that high precipitation areas were associated with highyield clusters in the heatmaps. The fact that the model has been able to estimate the yield decline in the low-NDVI and medium-rainfall areas, including field F104 (soybean), further supports its ability to measure the impacts of environmental stressors.

The comparison outcomes also emphasized the power of the hybrid model. Although LSTM-only and CNN-only models learned the temporal and spatial dynamics respectively, they did not have the ability to have a single unified featurelearning like the hybrid architecture. The fact that the RF (0.84) and SVR (0.79) models have lower values of R 2 indicates that classical machine learning approaches are not so effective when it comes to generalizing to complex interactions among features that occur in agricultural data. This was statistically significant in terms of testing the hypothesis, where the CNN-LSTM model did not indicate any significant differences between the predicted and actual yield (p>0.05), which was not the case with RF and SVR models that had significant deviations. Furthermore, the analysis of feature importance showed that NDVI, rainfall, and temperature were the most powerful predictors in the results of the yield, and the main role was played by the status of vegetation and climatic factors. Nitrogen content of soil and sowing week also influenced yields, and therefore the interaction of nutrient content and planting time with weather conditions determined the productivity. In general, the research shows that hybrid deeplearning solutions offer a strong and empirically grounded avenue of actionable agricultural prognostication and precision-farming decision support

Conclusion

The paper validates the fact that the hybrid CNN-LSTM architecture is an effective and reliable method of crop-yield prediction through the use of spatial and time-based agricultural information. The fact that it has better results compared to traditional machine learning model and single stream deep learning models shows the relevance of integrating vegetation indices, climatic variables, soil variables, and phenological variables into a coherent predictive model. The high accuracy rate, low error rates and statistical confirmability of the model to the real yield value renders the model to be applicable in real life practices of precision agriculture, resource planning and climate smart farming. The CNN-LSTM framework will offer an effective decision support system that can be used to inform farmers, policymakers, and agricultural managers through more informed and sustainable crop-management practices because of the accurate determination of key yield determinants, especially NDVI, rainfall, and temperature.

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