On the Feasibility of Estimating Fruits Weights Using Depth Sensors

Automatic fruit weight estimation is an essential in today’s automated fruit and agriculture industry. To increase the weighing efficiency of fruits, and to decrease the usage various tools that require dedicated human efforts, robust and easy to use automatic machine vision-based systems are required. Such systems can further reduce human errors, willful manipulation at point of sale counters in traditional shops in weighing fruits as well as decrease the overall costs of manual systems. Despite a wealth works in automatic fruit inspection systems with image processing techniques, there are not many works that adopt consumer depth cameras like Microsoft Kinect. In this work, we study the feasibility of an automatic fruit weight estimation method for Sweet Lemons (Citrus limetta), Sweet Peppers (Capsicum annuum), and Tomatoes (Solanum lycopersicum) based on their color (RGB) and depth images. Given the lack of available public datasets for this research direction, we create a dedicated database that consist of color plus depth information using data from Microsoft Kinect V.2 sensors with 50 samples in each of the three fruits. Our novel method is evaluated using the quality metrics such as mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE), which are calculated on three different distances, namely 0.8, 1.0, and 1.3 meters, from the sensor. One of the main goals of this paper is to study whether such depth sensor-based weighing method can remove the need for cumbersome industrial machines. Our proposed method is a non-learning-based system; hence it is fast and potentially could be used on real time automatic inspection systems. Our feasibility study indicates we obtain satisfactory accuracy in weighing these fruits and provides a potential for other fruits.

Keywords:

Automatic fruit weight estimation , Image processing , Depth data , RGB-D , Automatic inspection , Low cost , Microsoft Kinect