The adoption of precision agriculture in pineapple farming has a significant impact by increasing the yield and reducing the input resources while improving the management of pineapple crops. The intersection of advanced drone technology and cutting-edge artificial intelligence has reformed fruit crop management through revolutionary levels of automation, precision fruit detection, yield estimation, and crop health detection. However, the capability for obscuring the detection of subtle features to better manage occlusions and complex environments in images captured by drones at certain heights with drones is challenging to distinguish, thus hindering an accurate object analysis for fruit-environment differentiation. The proposed work uses Deep Learning (DL) techniques to classify pineapple fruit images captured ten meters above the ground. This is achieved specifically through the use of pretrained models and Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) due to their ability to learn robust interpretations from images for object classification tasks. This paper evaluates the capabilities and accuracies of four pretrained models, namely ResNet-101, ResNet-50, Inception-ResNet-v2, and VGG-19, to detect and classify the pineapple fruit amidst the complex background and varying lighting conditions. By evaluating the pretrained models for pineapple fruit classification using comprehensive metrics (True Positive Rate (TPR), False Positive Rate (FPR), Accuracy (ACC), Recall (REC), Precision (PRE), F1-score), the results reveal that the Faster R-CNN architecture with the VGG-19 pretrained model outperformed the other architectures, demonstrating the best performance in pineapple fruit detection with an ACC of 0.7924 (79.24%), a PRE of 0.9990 (99.90%), a REC of 0.7930 (79.30%), and an F1-score of 0.8839 (88.39%). The effectiveness of this model in overseeing complex scenarios suggests potential improvements in classification accuracy compared to other pretrained models, while acknowledging performance variability across various architectures.