This paper presents an advanced image processing technique for detection, characterization, and localization of orange fruits and their stems, using an RGB-D camera, addressing a critical challenge in agricultural automation. Additionally, this work investigates the performance of RGB-D camera for orange fruit and its stem characterizations, alongside with the Super-Resolution Convolutional Neural Network (SRCNN) techniques to enhance image quality for improved detection accuracy. The proposed method captures the images of orange fruits, converts them into HSV color space, isolates the orange fruit using binary masking, identifies the largest visible fruit, detects its stem, characterizes both the fruits and their stems, and precisely localizes both the fruit and stem features using pinhole camera modeling. The effectiveness of the super-resolution approach was evaluated by comparing the results of a higher resolution camera, RGB-D camera, and RGB-D camera with super-resolution enhancement. The super resolution approach was concluded to significantly improve stem detection and localization, with the proposed image processing method achieving localization results within a maximum absolute error of 4.5° and 1.7% deviation from measured values. These findings demonstrate promising potential for achieving high-accuracy localization of orange fruit and stems, facilitating precise gripping and cutting action in automated harvesting systems.