Utilising Drone Imagery to Assess the Peanut Quantity and Quality with Machine Learning Approaches
Creators: name: Shahi, Tej
Publisher: name: CQUniversity
Year: 2025
Subjects: classificationCode: 460199
subject: Applied computing not elsewhere classified

description: Precision agriculture relies on the efficient measurement of temporal and spatial variability in crop information for a smarter farming management strategy. Variability in crops in terms of their growth rates, size, and yield potentials can exist even within a single field, whereas traditional in-field sampling techniques are unable to represent such crop variability, thereby making precise decisions on crop management more challenging. Hence, alternative non-destructive ways of gathering crop information have been explored, including field-based sensors, satellite imagery and unmanned aerial vehicles (UAV). Among these, UAVs have gained significant attention in precision agriculture due to their ability to provide high temporal and spatial crop information using various sensors such as visible (RGB), multispectral, and hyperspectral. However, the extraction of useful information from the vast amount of spatial data captured by UAVs poses a challenge. Machine learning methods have shown promise in analysing such spatial data, making them worthy of investigation for large-scale data analysis tasks in precision agriculture.

This thesis investigates the potential of UAV-based remote sensing and machine learning techniques for assessing crop yield and diseases, focusing on peanut crops cultivated in Bundaberg, Australia. Early crop disease detection is one of the crucial aspects of precision agriculture. Fast and accurate disease assessment methods using UAV-based remote sensing data have been developed, typically employing traditional vegetation indices and statistical measures. However, there is a need to explore the construction of new measurement indices or spectral information measures to improve crop disease estimation with UAV imagery. The two new methods, measurement index (MI) and coefficient of variation (CV), for late leaf spot (LLS) disease estimation on peanuts, are developed utilising the variability of spectral information within a peanut plot in a UAV multispectral imagery. The MI-based methods estimated the LLS disease with a coefficient of determination of 0.79 and errors of 8.32%. Furthermore, a cooperative scheme based on the three best-performing methods (CV, MI and mean) is established for accurate LLS estimation in peanuts.

Crop yield forecasting using UAV-based remote sensing has shown promising results. However, the non-linear relationship between UAV spectral data and crop yield requires the investigation of advanced machine learning methods to accurately measure this relationship and build a better crop yield estimation framework. By utilising the five bands (red, green, blue, near-infra-red and red-edge) multispectral images acquired at various growth stages of peanuts using UAV, the potential for peanut yield predictions using linear regression as well as seven non-linear machine learning models were implemented. Furthermore, these ML models’ hyperparameters were optimised using simulated annealing (SA). Additionally, the best three ML models, random forest (RF), support vector machine (SVM), and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme which provided the better yield prediction with an average error of 7.88%.

This body of work presents cooperative approaches for both peanut yield estimation and disease detection using UAV-multispectral data. In addition, a detailed review of recent progress in precision agriculture tasks such as yield prediction and disease detection with UAV and deep learning techniques, is provided which includes a detailed discussion on possible future research avenues of UAV-based precision agriculture.