Investigation of machine learning based techniques for crop yield estimation of corn and cotton using multi-sensor data fusion

Accurate crop yield estimation is crucial for decision-making and planning in modern agriculture with increasing challenges with food security. Yield predictions provide farmers with insights into expected production, facilitating optimized resource allocation, improved agricultural management strategies, and enhanced profitability. This study investigates the application of machine learning (ML) techniques, including Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Random Forest (RF) models, for predicting crop yields using multi-sensory time-series data that has been collected on two fields over a four-year timeframe. The focus is on corn (Zea mays) and cotton (Gossypium hirsutum) yield, two of the top critical crops in Mississippi region. A multi-sensory dataset was collected using multispectral cameras and LiDAR sensors mounted on unmanned aircraft systems (UAS), along with soil moisture and temperature data from volumetric probes and environmental data from a nearby weather station. Over four years, more than 30 features were extracted weekly from five major categories, with 235 ground truth yield records from plots in the field. The study outlines the methodology for feature selection and examines its impact on yield prediction accuracy. Using percentile root mean square error (RRMSE) and mean absolute percentage error (MAPE) as performance metrics, the study found that the proposed LSTM model produced lower field-wise errors (9 − 21 % MAPE) compared to other models and validation, indicating superior performance in predicting yields across selected weeks. The proposed ML-based approach, validated through year-based and field-wise cross-validation methods, demonstrates the effectiveness of using UAS-collected multi-sensor data for accurate yield estimation in corn and cotton.