Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection

Abstract

This paper presents an automatic parameter tuning procedure specially developed for a dynamic adaptive thresholding algorithm for fruit detection. One of the major algorithm strengths is its high detection performances using a small set of training images. The algorithm enables robust detection in highly-variable lighting conditions. The image is dynamically split into variably-sized regions, where each region has approximately homogeneous lighting conditions. Nine thresholds were selected to accommodate three different illumination levels for three different dimensions in four color spaces: RGB, HSI, LAB, and NDI. Each color space uses a different method to represent a pixel in an image: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), LAB (Lightness, Green to Red and Blue to Yellow) and NDI (Normalized Difference Index, which represents the normal difference between the RGB color dimensions). The thresholds were selected by quantifying the required relation between the true positive rate and false positive rate. A tuning process was developed to determine the best fit values of the algorithm parameters to enable easy adaption to different kinds of fruits (shapes, colors) and environments (illumination conditions). Extensive analyses were conducted on three different databases acquired in natural growing conditions: red apples (nine images with 113 apples), green grape clusters (129 images with 1078 grape clusters), and yellow peppers (30 images with 73 peppers). These databases are provided as part of this paper for future developments. The algorithm was evaluated using cross-validation with 70% images for training and 30% images for testing. The algorithm successfully detected apples and peppers in variable lighting conditions resulting with an F-score of 93.17% and 99.31% respectively. Results show the importance of the tuning process for the generalization of the algorithm to different kinds of fruits and environments. In addition, this research revealed the importance of evaluating different color spaces since for each kind of fruit, a different color space might be superior over the others. The LAB color space is most robust to noise. The algorithm is robust to changes in the threshold learned by the training process and to noise effects in images.

Development of a sweet pepper harvesting robot

“…Due to the complex problem, most R&D on robotic harvesting focuses on a single aspect of the robotic system, for example, detection (Halstead, McCool, Denman, Perez, & Fookes, 2018;Kamilaris & Prenafeta-Boldú, 2018;Kapach, Barnea, Mairon, Edan, & Ben-Shahar, 2012;Vitzrabin & Edan, 2016a, 2016bZemmour, Kurtser, & Edan, 2019;Zhao, Gong, Huang, & Liu, 2016), manipulation and gripping (Bulanon & Kataoka, 2010;Eizicovits & Berman, 2014;Eizicovits, van Tuijl, Berman, & Edan, 2016;Rodríguez, Moreno, Sánchez, & Berenguel, 2013;Tian, Zhou, & Gu, 2018), and motion/task planning (Barth, IJsselmuiden, Hemming, & Van Henten, 2016; Korthals et al, 2018;Li & Qi, 2018;Liu, ElGeneidy, Pearson, Huda, & Neumann, 2018;.…”

An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor

“…Currently, some work has been done to detect the peduncle based on the color information using RGB cameras [6,7,29,30]; however, such cameras are not capable of discriminating between peduncles, leaves, and crops if they are in the same color [7]. The work in [31] proposed a dynamic thresholding to detect apples in viable lighting conditions, and they further used a dynamic adaptive thresholding algorithm [32] for fruit detection using a small set of training images. The work in [27] facilitates detection of peduncles of sweet peppers using multi-spectral imagery; however, the accuracy is too low to be of practical use.…”

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

“…Furthermore, architectures and algorithms of the proposed one-dimensional CNN, and the partition rules of the dataset used for method verification were discussed. Finally, the performance of the proposed approach was compared with the results of other pattern recognition methods, all of which were conducted under the ten-fold cross-validation [39].…”