Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection.

Elie Zemmour,Polina Kurtser,Yael Edan

doi:10.3390/s19092130

Elie Zemmour, Polina Kurtser + Show 1 more

Open Access

https://doi.org/10.3390/s19092130

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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.

Highlights

Fruit detection is important in many agricultural tasks such as yield monitoring [1,2,3,4,5,6,7,8], phenotyping [9,10,11], precision agriculture operations, and robotic harvesting [16,17,18]
A direct decrease in very large images is not noted, we still can conclude that splitting a large image to 300 × 300 or lower will decrease the average Splitting Condition (STD)
We present the tuning procedure results, including thresholds derived to categorize the sub-images into light level groups, as well as the recursive stop condition that achieved the best result for each database

Summary

Introduction

Fruit detection is important in many agricultural tasks such as yield monitoring [1,2,3,4,5,6,7,8], phenotyping [9,10,11], precision agriculture operations (e.g., spraying [12] and thinning [13,14,15]), and robotic harvesting [16,17,18]. Texture, and location are subject to high variability in the agricultural domain [18]. Fruits grow in unstructured environments with highly-variable lighting conditions [19,20] and obstructions [21] that influence detection performance. Color and texture are fundamental characteristics of natural images and play an important role in visual perception [22].

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