Next Article in Journal
A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation
Next Article in Special Issue
A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery
Previous Article in Journal
Sharp Interface Approach for Regional and Well Scale Modeling of Small Island Freshwater Lens: Tongatapu Island
Previous Article in Special Issue
Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing
Open AccessArticle

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

1
Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
2
Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain
3
Engineering Department, Miguel Hernandez University of Elche, 03312 Orihuela, Spain
4
Food Engineering and Agricultural Equipment Department, Technical University of Cartagena, 30203 Cartagena, Spain
*
Authors to whom correspondence should be addressed.
Water 2018, 10(11), 1634; https://doi.org/10.3390/w10111634
Received: 25 September 2018 / Revised: 1 November 2018 / Accepted: 7 November 2018 / Published: 12 November 2018
(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)
Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies. View Full-Text
Keywords: fruit segmentation; video processing; different light intensities; artificial neural networks; cultural algorithm; irrigation management fruit segmentation; video processing; different light intensities; artificial neural networks; cultural algorithm; irrigation management
Show Figures

Figure 1

MDPI and ACS Style

Sabzi, S.; Abbaspour-Gilandeh, Y.; García-Mateos, G.; Ruiz-Canales, A.; Molina-Martínez, J.M. Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation. Water 2018, 10, 1634.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop