Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

Sajad Sabzi,Razieh Pourdarbani,Davood Kalantari,Thomas Panagopoulos

doi:10.3390/app10010383

Abstract

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

Highlights

Nowadays, agricultural automation is inevitable to reduce costs, minimize labor difficulty, decrease environmental impact, increase timely farming and crop quality, and brink transparency in the supply chain [1,2]
The aim of this study was to offer a pixel-by-pixel segmentation algorithm based on an ensemble with a majority voting rule for segmentation of plum fruits in orchards at different ripeness stages under natural conditions
Since light intensity changes during day and the main aim of the proposed segmentation algorithm is working in natural conditions related to light and background, segmentation algorithms should offer high accuracy in all conditions, so they should be trained under all possible light intensities

Summary

Introduction

Agricultural automation is inevitable to reduce costs, minimize labor difficulty, decrease environmental impact, increase timely farming and crop quality, and brink transparency in the supply chain [1,2]. One of the challenges of robots is to identify and harvest the ripe fruits. In this regard, cameras and sensors in the robot’s arms have been used to evaluate the information [3]. Performance of ANN-BBO Classifier in the Best State of Training. We show that 3% of the samples in the fruit class were incorrectly classified in the background class and 2.8% of the samples in the background class were incorrectly classified in the fruit class. All the criteria had values close to 100 and this proves that the classifier performed well

Objectives

Methods

Results

Conclusion