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

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.