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

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.

Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

“…We used well-known performance indices: mean square error (MSE), root-mean-square error (RMSE) and mean absolute error (MAE). We also computed common regression (R) and determination (R 2 ) coefficients ( Pourdarbani et al., 2020 ; Sabzi et al., 2020 ).…”

Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties

“…Most methods published on a non-destructive estimation of fruit properties are based on samples collected and assessed under controlled laboratory conditions after fruit temperature equilibration [37]. Predictions of non-destructive methods based only on internal validations tend to be overestimated in the literature.…”

Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques

“…According to previous research, it is found that many studies have been accomplished on potatoes, most of them focused on the detection of visible defects and normally using a single detection algorithm. The innovation of the present study is the proposal, development, and validation of an ensemble classifier, combined with the majority voting rule [ 23 ] that includes hybrid artificial neural networks (ANN) and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors algorithm (KNN), to identify internal defects of potatoes that have no visible symptoms.…”