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

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.