Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture

Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.