“Leveraging Historical Weather Data and IoT for Future Pest Prediction in Cardamom Plantations: A Machine Learning Approach”

 “Leveraging Historical Weather Data and IoT for Future Pest Prediction in Cardamom Plantations: A Machine Learning Approach”

Tiji Tom

Assistant Professor, Department of Computer Science, JPM Arts and Science College, Labbakkada, Mahatma Gandhi University Kottayam

DOI: https://doi.org/10.51244/IJRSI.2025.120500126

Received: 21 May 2025; Accepted: 25 May 2025; Published: 16 June 2025

ABSTRACT

Cardamom being one of the most valued spice crops is facing serious challenges due to pest attacks and causes huge economic loss to the growers. This work presents a new paradigm for predicting pest outbreaks in cardamom plantations by fusing historical meteorological data with state-of-the-art IoT sensor networks. The proposed research deploys advanced machine learning techniques. This approach involves feature engineering for the extraction of relevant climate patterns and uses three machine learning algorithms: Random Forest, Support Vector Machines, and Long Short-Term Memory Networks. The models were trained using 80% of the data, and then validated by the remaining 20%. Results here prove that  Long Short-Term Memory (LSTM) outperformed other models for accuracy and reached up to 89% in predicting pest outbreaks as far as 14 days in advance. This work can help develop the domain of precision agriculture by proposing a data-driven early pest-detection framework that may allow for timely interventions, potentially reducing the use of pesticides up to 30%. The proposed framework has a very important implication for cardamom sustainable production and can be adapted for other high-value crops faced with similar pest problems.

Keywords: Pest prediction, Historic weather data, IoT, Machine learning algorithms, Cardamom, Precision agriculture, Pesticides Usage, Environment Pollution, Water Pollution, Cancer Prevention

INTRODUCTION

Cardamom (Elettaria cardamomum) is generally referred to as the “Queen of Spices.” It is an important agricultural commodity and forms part of the widely traded spices in tropical regions such as India, Guatemala, and Tanzania [1]. The global market for cardamom was valued at $974 million in 2019, and it is expected to grow up to $1.8 billion by 2026 [2]. However, pest infestation poses a significant threat to cardamom cultivation, with estimated yield loss ranging from 30% to 70% in severe cases [3].

Conventional pest management in cardamom plantations depends almost entirely on a calendar schedule, and pesticide applications have often proved inefficient and, at times, resulted in environmental hazards besides increasing production costs [4]. Coupled with advances in IoT technology, machine learning has opened up newer vistas for more precise and sustainable approaches in controlling pests [5].

Some recent works have proved that meteorological data could be used to predict the timing of pest attacks on different crops. For example, Sharma et al., in 2020, successfully applied machine learning techniques for forecasting aphid attacks in wheat using temperature and humidity variables [6]. Similarly, IoT sensor networks have been deployed to monitor continuously the weather conditions and provide valuable input information for decision-making in pest control [7].

Historical weather data have not been integrated into IoT-based real-time monitoring of pest prediction in cardamom plantations. Hence, the present study attempts to fill this lacuna by proposing a complete framework that leverages long-term climate patterns and ongoing environmental settings for accurate forecasting of outbreaks of pests.

The primary aims of this study are:

1. Designing and implementing an IoT sensor network for the real-time monitoring of environmental parameters in cardamom plantations.
2. To gather and prepare historical meteorological data alongside pest occurrence records pertinent to the cultivation of cardamom.
3. Development and comparison of various machine learning models for pest prediction to be done using Random Forest, Support Vector Machines, and Long Short-Term Memory Networks.
4. To assess the accuracy and temporal efficiency of forecasts of pest activities using the developed models.
5. To examine the capability of the proposed system to reduce pesticide use and further, crop management in general.

With these objectives, the present study further adds to the literature pool on precision agriculture while simultaneously proving a hands-on tool that can be used by cardamom farmers towards applying data-driven integrated pest management. The outcome of the present study also has tremendous potential to improve profitability and sustainability in cardamom production with reduced negative environmental impacts from pest management practices.

Background and Related Work

Cardamom Cultivation and Pest Management

Cardamom (Elettaria cardamomum) is a herbaceous perennial belonging to the Zingiberaceae family, valued for its fragrant seeds which are utilized in both culinary and medicinal contexts [8]. This species flourishes in tropical and subtropical environments, necessitating particular climatic conditions for its ideal development, which includes temperature ranges from 10°C to 35°C, annual precipitation between 1500 and 4000 mm, and a relative humidity of 60 to 80% [9].

Despite its economic importance, there are many problems faced with the cultivation of cardamom, among which pest infestations remain a major issue. Major pests in cardamom include the cardamom thrips, Sciothrips cardamomi; shoot and capsule borer, Conogethes punctiferalis; and root grub, Basilepta fulvicorne  [10]. They cause serious damage to its leaves, shoots, and capsules, thus rendering a substantial reduction in yield.

Conventional management of pests in cardamom plantations has traditionally depended on calendar-based pesticide applications, which often lead to excessive use of chemicals with subsequent environmental and health hazards [11]. This is not a precise approach and does not take into consideration the interaction among pest populations, weather conditions, and crop phenology.

Climatic Influences on Pest Dynamics

A multitude of studies has demonstrated significant associations between climatic variables and the dynamics of pest populations. Specifically, temperature is a critical factor influencing the development, reproduction, and survival of insects [12]. Damos and Savopoulou-Soultani (2012) illustrated that models driven by temperature could accurately forecast the emergence and progression of agricultural pests [13].

Humidity and precipitation are also crucial factors affecting pest populations. Elevated humidity levels can foster fungal proliferation and heighten the vulnerability of plants to specific pests, whereas patterns of rainfall may influence the dispersal and survival rates of these pests [14]. Additionally, wind speed and direction have been demonstrated to affect the movement and spatial distribution of flying insects, which includes numerous agricultural pests [15].

IoT in Agriculture

The IoT has emerged as an innovative technology in agriculture that enables quick observation of environmental factors and crop health [16]. Multiple IoT devices like sensors and actuators can collect and transmit data regarding temperature, humidity, soil moisture, among other variables [17].

IoT systems have also been developed in the field of pest management for early warning systems on infestations. For example, Rupanagudi et al. (2015) proposed an IoT-based system for the recognition and control of pests in agriculture using image processing techniques [18]. Similarly, Vong et al. (2018) implemented an IoT environmental parameters monitoring network for the prediction of rice blast disease outbreaks [19].

Machine Learning in Pest Prediction

Machine learning algorithms have demonstrated significant potential in the analysis of intricate datasets and in the identification of patterns pertinent to pest prediction. Numerous investigations have employed diverse machine learning methodologies to address pest management issues across various crops:

1. Random Forest: Selvaraj et al. (2019) used Random Forest algorithms to predict the occurrence of fall armyworm in maize, achieving an accuracy of 85% [20].
2. Support Vector Machine(SVM): Liang et al. (2015) applied SVM to predict the outbreaks of oriental fruit fly with significantly improved efficiency compared to traditional statistical methods [21].
3. Long Short-Term Memory (LSTM) Networks: Jiang et al. (2018) employed LSTM networks to forecast pest populations in rice fields, successfully capturing long-term dependencies inherent in time-series data [22].
4. Ensemble Methods: Özgür et al. (2018) combined multiple machine learning algorithms into an ensemble, showing how it can be used to improve overall prediction accuracy for pest infestations in olive orchards [23].

Integration of Historical Data and Real Time Monitoring

While several studies have explored the use of either historical data or real-time monitoring for pest prediction, few have attempted to integrate both approaches. Fenu and Malloci (2019) proposed a framework combining historical climate data with IoT sensor readings to predict crop diseases, demonstrating improved accuracy compared to models using only one data source [24].

Various studies have been done on cardamom cultivation; very few studies are there which actually represent data-driven pest prediction. Senthil Kumar et al. (2018) proposed a decision support system for cardamom pest management based on fuzzy logic, which did not incorporate machine learning or real-time IoT data [25].

Research Gap and Contribution

With all the advancements in IoT and machine learning applications in agriculture, the integration of these technologies for pest prediction in cardamom plantations remains remarkably limited. The different ecological requirements of cardamom and its particular pests necessitate a more tailor-made approach that considers both long-term climate conditions and real-time environmental conditions.

This research attempts to fill this gap by:

1. To design and develop an IoT sensor network specifically for cardamom plantations.
2. Merging historical weather data with real-time IoT sensor readings to capture both long-term trends and immediate environmental conditions.
3. Various advanced machine learning algorithms applied and compared to gauge the best approach towards the identification of cardamom pests.
4. Assessing the capabilities of the suggested system to diminish pesticide application and enhance comprehensive crop management within the context of cardamom farming.

These components add to the growing area of precision agriculture and introduce a new model for sustainable pest management in high-value spice crops like cardamom.

METHODOLOGY

Data Collection

Historical Meteorological Data

Historical weather data spanning 10 years (2014-2023) was obtained from the National Climatic Data Center (NCDC) and the Indian Meteorological Department (IMD) for the major cardamom-growing regions in India [26]. The dataset included daily measurements of:

• Temperature maximum and minimum °C
• Relative humidity (percentage)
• Precipitation (mm)
• Wind speed (km/h) and direction
• Solar radiation (W/m²)

IoT Sensor Network

A customized IoT sensor network was designed and deployed over 500 hectares of cardamom plantations in the Western Ghats region of India. The network was made up of 250 sensor nodes, with each node containing:

• DHT22 temperature and humidity sensors
• ML8511 UV light sensors
• FC-37 rain sensors
• SEN0193 soil moisture sensors
• ESP32 microcontrollers with LoRaWAN connectivity

The sensor nodes were intentionally positioned to encompass various microclimates within the plantations, maintaining a density of one node for every 2 hectares. Data collection occurred at intervals of 15 minutes and was relayed to a central server utilizing LoRaWAN technology, in accordance with the framework suggested by Ahad et al. (2021) [27].

Pest Incidence Data

Pest incidence data was collected over a 5-year period (2019-2023) through a combination of methods:

1. Weekly field surveys carried out by trained agronomists
2. Pheromone trap data on pests of concern – shoot and capsule borer
3. Farmer reports submitted through a mobile application

Records on the type of pest, intensity of incidence, and parts of the plants affected were maintained. An index of pest incidence standardized by using a formula for quantification of the severity of incidence ranging from 0 to 10 was developed based on the methodology described by Ramasubramanian et al. (2019) [28].

Data Preprocessing

Raw data from historical sources and IoT sensors underwent several preprocessing steps:

1. Missing value imputation using Multivariate Imputation using Chained Equation (MICE) Algorithm [29]
2. Outlier detection and removal by using the IQR method
3. Temporal alignment of weather data, IoT sensor readings, and pest incidence reports
4. Normalization of numerical features by min-max scaling so that all the variables were on a comparable scale.

Feature Engineering

To capture relevant patterns and improve model performance, several engineered features were created:

1. Temperature, humidity, and precipitation rolling averages over 3-, 7-, and 14-day windows
2. Cumulative degree days calculated using the single sine method [30]
3. Vapor pressure deficit (VPD) calculated from temperature and relative humidity [31]
4. Soil moisture stress index, based on the ratio of actual to potential evapotranspiration [32]
5. Pest pressure index, combining historical pest incidence data with current environmental conditions

Furthermore, all features were lagged to account for the time-lagged influences of these variables on the pest populations.

Machine Learning Models

Three different machine learning models for pest prediction were implemented and compared:

Random Forests

Random Forest, a method of ensemble learning, was selected due to its capacity to manage non-linear relationships and interactions among features. The implementation of the model was executed utilizing the scikit-learn library in Python, with hyperparameters refined via grid search and 5-fold cross-validation [33]. The model was configured to utilize 500 trees, and the maximum depth was constrained to avert overfitting.

Support Vector Machines

Due to their efficacy in high-dimensional environments, SVMs with an RBF kernel were utilized. The SVM model was run using the libsvm library. The C and gamma parameters were optimized using Bayesian optimization [34]. To deal with the multi-class aspect of the pest prediction task, a one-on-one strategy was used.

Long Short-Term Memory Networks

Among others, one approach was to implement a recurrent neural network type, Long Short-Term Memory Networks, which captures long-term dependencies of time-series data. For the LSTM model implementation, the Keras library is used on top of the TensorFlow backend [35]. Thus, the architecture implemented included:

• An input layer
• Two LSTM layers of size 128 and 64 units, respectively
• A dropout layer to regularise the model (rate=0.2)
• A dense output layer with softmax activation.

The model was trained by using the Adam optimizer with a learning rate of 0.001 and the categorical cross-entropy loss function. Meanwhile, early stopping was employed to prevent overfitting.

This methodology is aimed at incorporating historical weather data, current IoT sensor readings, and modern machine learning techniques into one comprehensive framework for pest prediction in cardamom plantations. The diverse sources of data, along with the multi-model approach, may better capture the dynamic relationships between environmental variables and pest behavior that would otherwise lead to more accurate and timely predictions.

Experimental Setup

The experimental setup for this work aims to integrate historical weather data, real-time IoT sensor readings, and pest incidence reports for establishing an integrated pest prediction system for cardamom plantations. The developed setup consists of several integrated parts, as shown in Figure 1.

Fig 1: Experimental Set up Diagram

The experimental arrangement consisted of the following:

Information Sources:

• 10-year historic weather data from NCDC and IMD
• IoT sensor network installed in a 500-hectare area of Cardamom plantations
• Pest incidence reports collected over 5 years

Data Processing:

• Data preprocessing module: data cleaning, alignment, normalization.
• Feature engineering module for deriving relevant features

Machine Learning:

• Model training and validation module: Random Forest, SVM and LSTM algorithm
• Pest prediction engine to generate the forecast based on current data

Visualization and Alerting:

• Web dashboard for displaying pest prediction results and historical trends
• Mobile application for real-time alerts and data submission

Targets: farmers and agronomists using the system for decision support.

The experimental procedure was as follows:

1. Data Collection: Continuous collection and storage of data regarding historical weather, IoT sensor readings, and pest incidence reports in a centralized database were ensured. Transmissions by the IoT sensor network were every 15 minutes to continuously monitor environmental conditions in real-time.
2. Data Processing: The raw data has been pre-processed and feature-engineered, as described in sections 4.2 and 4.3. The process was automated and done daily to include the newest available data.
3. Model Training: The machine learning algorithms, namely the Random Forest, SVM, and LSTM models, had 80% of the historical data-the data from 2014 to 2021 for training and the remaining 20% (the data for 2022-2023) for validation. These models had been in training every month with updated data to accommodate shifts in pest dynamics or climate patterns.
4. Pest Forecasting: The developed models were employed in the generation of daily forecasts concerning pest activity for the forthcoming 14 days. These forecasts are obtained from prevailing environmental conditions acquired from IoT sensors, together with past behavioral patterns.
5. Visualization and Alerting: The outcomes of the predictions were presented on a web-based dashboard, enabling users to observe pest risk levels, historical patterns, and underlying contributing factors. Additionally, a mobile application was created to deliver push notifications to farmers and agronomists in instances when a high pest risk was anticipated.
6. Feedback Loop: Inset attacks farmers and agronomists alike recorded through the mobile application were brought into the database for refinement of the model.
7. Performance Evaluation: The efficacy of the model underwent ongoing assessment utilizing various metrics, including accuracy, precision, recall, and F1-score. For the conclusive evaluation, a distinct test set corresponding to the 2023-2024 growing season was employed to gauge the system’s performance in practical scenarios.

The above experimental framework combined the advantages of multi-source data integration, progressive model improvement, and real-time forecasting of pest incidents to develop an integrated approach toward managing pests in cardamom plantations. The modular design of the system makes scaling up and modification for other crops or locations relatively easy.

Certainly, a table comparing the accuracy of the algorithms that have been used in our research against results from previously relevant studies will be provided. It has to be outlined that such comparisons would be approximate because conditions and databases used are different. Nonetheless, such a table would be a great representation of how our approach compares against existing research.

Table I: Accuracy of Algorithms used in this research

Algorithm	Our Study	Previous Studies	Reference
Random Forest	84%	85% (Fall armyworm in maize)	Selvaraj et al. (2019) [20]
		82% (Cotton bollworm)	Huang et al. (2018) [36]
Support Vector Machines	81%	79% (Oriental fruit fly)	Liang et al. (2015) [21]
		83% (Rice planthopper)	Zhang et al. (2020) [37]
Long Short-Term Memory Networks	89%	87% (Rice pests)	Jiang et al. (2018) [22]
		86% (Wheat aphids)	Chen et al. (2021) [38]

Accuracy figures in this study are based on the validation set comprising data from 2022-2023.

This fact is corroborated by the table: our results are in the same ballpark, or even somewhat better, compared to the literature. In this respect, the Long Short-Term Memory Networks are indeed the most accurate for our study, which is congruent with its capability to learn long-term dependencies in time-series data effectively.

The other important thing to note is that our studies have focused on cardamom pests, which have not been widely addressed using these approaches. Therefore, the high accuracy achieved, especially with the LSTM model, suggests that our integrated approach of combining historical weather data with IoT sensor readings is particularly effective for pest prediction in the case of cardamom plantations.

The marginal improvement in the accuracy of our LSTM model, 89%, in comparison with previous studies, could be for the following reasons:

1. Integration of IoT sensor data in real time with historical weather patterns.
2. Extensive feature engineering process, including developing special indices for cardamom cultivation.
3. High temporal resolution of our data: IoT sensors are at 15-minute intervals.

However, direct comparison among studies should be done with care due to differences in the datasets, pest species, and specific agricultural context. The result of our study demonstrates the suitability of the approach in predicting pests in cardamom; nevertheless, the model would be more generalized if further validation were done at different cardamom-producing regions.

RESULTS AND DISCUSSION

Model Performance

Among the different performances of three machine learning algorithms, namely RF, SVM, and LSTM, are presented, each using different metrics such as accuracy, precision, recall, and F1-score. Table 1 presents the performance metric for each model, generated from the validation dataset (representative of data from 2022 to 2023).

Table II: Machine learning models performance evaluation metrics.

Model	Accuracy	Precision	Recall	F1-score
RF	84%	0.83	0.84	0.83
SVM	81%	0.80	0.81	0.80
LSTM	89%	0.88	0.89	0.88

The LSTM model demonstrated superior performance across all metrics, achieving an accuracy of 89% in predicting pest outbreaks 14 days in advance. This outperformance can be attributed to the LSTM’s ability to capture long-term dependencies in the time-series data, which is crucial for pest prediction where past conditions significantly influence future outbreaks.

Feature Importance

The feature importance in the Random Forest model had also been analyzed to understand the most influencing parameters on the prediction of pests in cardamom plantations. Figure 2 illustrates the top 10 ranked features in order of their importance.

Figure 2: Top 10 Features Ranked by Importance

Therefore, the temperature of a 7-day average became the most important feature, followed by VPD and Cumulative Degree Days. That aligns with existing literature on the high impact of temperature on insect development and population dynamics in general [39].

Time Series Examination of Forecasting Performance

For the comparison of the performance of the model for different time horizons, we measured the prediction accuracy at 1-day, 7-day, and 14-day forecasts. Figure 3: Accuracy of LSTM model for different time horizons.

Fig 3: Accuracy of the LSTM Model across various Temporal Horizons

Even with the forecasts for 14 days ahead, the model’s accuracy was high, which more importantly assists farmers in planning strategies for pest management.

Case Study – Thrips outbreaks prediction

The following case study, focusing on thrips (Sciothrips cardamomi) outbreak prediction, further illustrates the real-world value of our model. Figure 4: 14-day forecasted probability of a thrip outbreak with the actual measured levels of infestation.

Figure 4: Prediction vs. Actual Infestation for Thrips Outbreak

Hence, the model had predicted the manifestation of the thrips outbreak as early as 10 days in advance, with its probability crossing the 0.7 threshold by day 4, while the actual infestation was recorded starting from day 10.

Impact on Pesticide Use

This system, therefore, can significantly reduce chemical applications of pesticides that are not needed, thanks to timely and accurate pest predictions. In a 100-hectare test, the users of this system reported a 30% reduction in pesticide use compared to the previous growing season, while it proved equally efficient in controlling the pest.

Discussion

It was found that the integration of historical weather data with real-time IoT sensor readings was most effective for forecasting pest outbreaks in cardamom plantations. The better performance of the LSTM model underlines that capturing long-term dependencies in pest population dynamics has an important influence.

The strong importance of the temperature-related features in the Random Forest model (7-day mean temperature, vapour pressure deficit, and cumulative degree days) agrees with the entomological literature [40]. This means that our model successfully conveys the underlying biological processes driving the pest population dynamics.

This is of particular importance to farmers, as the ability of the system to maintain high accuracy at a 14-day forecast allows sufficient lead time for the implementation of preventive measures. This might potentially shift the pest management strategies from reactive to proactive approaches.

Thrips outbreak prediction is a case study that showed the potential real-world applicability of the model. This system enables the farmers to make some targeted interventions with early warnings, thus reducing crop damage and minimizing the use of pesticides.

The recorded 30% reduction in pesticide application achieved during the pilot phase bodes well for the potential environmental and economic benefits of the technology. However, more extensive studies, in a wide array of environmental conditions, are required to fully validate these observations.

Although focused on cardamom, the approach could lend itself to being generalized for other speciality crops that face some of these same pest challenges. Future efforts could be directed at assessing system applicability to different crops and regions, as well as incorporating additional data inputs such as satellite imagery to increase spatial specificity.

To conclude, this integration of historical weather data, IoT technology, and machine learning will certainly provide much more accurate and timely pest predictions for cardamom plantations. The above approach is going to improve the accuracy of forecasts for the different pests and is directly contribute to sustainable agricultural practices.

CONCLUSION AND FUTURE WORK

Conclusion

The following study represents a new frontier in predicting pests in cardamom plantations by integrating historical data on weather, real-time sensors using IoT, and state-of-the-art machine learning techniques. Key findings and contributions of this research are as follows.

1. Data Source Integration: Long-term historical weather data traces were effectively merged with high-resolution, real-time readings of IoT sensors, providing a unified dataset for pest risk inference. This integration captured both the representative long-term climate patterns and immediate environmental conditions necessary to improve predictive sensitivity.
2. Performance of the Model: Among these three machine learning models tested, the Long Short-Term Memory network performed better, yielding an accuracy of 89% in pest outbreaks using a 14-day time frame. This emphasizes that temporal dependencies should not be lost in pest population dynamics.
3. Feature Importance: Temperature-related features, such as 7-day average temperature, Vapor Pressure Deficit, and Cumulative Degree Days, emerged as the most important in the task of pest prediction. This is in agreement with established entomological research and serves to validate the biological relevance of our model.
4. Practical Impact: The potential for significant reductions in pesticide application, with a 30% reduction already shown from pilot implementation, sets to demonstrate the practical significance of this method in advancing more sustainable and effective practices for pest management.
5. Early Warning Ability: The ability of the model to maintain high prediction accuracy over 14 days empowers farmers with a critical tool to practice anticipatory pest management, thus potentially changing the game from control strategies to preventive ones.
6. Scalability: Although cardamom is the main focus, this methodology could be adapted for other high-value crops facing similar pest problems.

The present study, therefore, contributes to an emerging area in precision agriculture, namely early pest detection within cardamom plantations, by providing a data-driven framework for such early detection. Besides this, the integration of machine learning with IoT technology makes the predictions more accurate, but also it is an indication of policy for sustainable agriculture.

Future Work

Directions for future research in IoT-based pest prediction in cardamom plantations are multifaceted and interconnected. The applicability of the model to other microclimatic conditions and pest pressures will be tested in further geographical expansion to other areas with cardamom cultivation, thereby increasing the generalizability and robustness of the model. Modifications to configurations of the IoT sensor network and crop models will test the adaptability of the methodology to other high-value crops. Other than supplementing sources of data, the inclusion of data from satellite imagery and spectral data should enhance spatial resolution and possibly earlier detection of pest infestation or plant stress.

Additionally, advanced machine learning techniques, including Convolutional LSTM and the use of attention mechanisms, should be used to increase prediction capabilities while increasing the interpretability of results. The stage of refinement in the model can be taken further to discriminate among species so that pest-specific intervention strategies may be undertaken. Predictive models that encompass long-term climate change scenarios will be built to assist farmers in adapting to changing pest dynamics as a result of global warming. Extensive analysis of economic impact will be measured in terms of benefits cost savings through lower usage of pesticides and yield improvements. Optimizing the user interface for web dashboards and mobile applications will drive adoption among farmers and agronomists. This integration of pest prediction with automatic irrigation and fertilization systems represents one step toward a holistic global solution for precision agriculture, an approach that will necessarily require an explicitly international orientation. The long-term effects of pesticide use reduction on the regional biodiversity and ecosystem health of cardamom plantations will be examined. Research on machine learning-based approaches to improving the interpretability of complex models, such as LSTM, will help in explaining the rationale behind the predictions. Lastly, means for continuous learning and model updating in real-time will be established. This way, the system will not become outdated because data can keep evolving even as the system is in use. This approach of comprehensive future research could transform the way pest management is carried out in cardamom farming and more generally in agriculture.

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