Abstract
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.
Highlights
Segmentation is an important step in designing machine vision systems for agricultural and gardening purposes
Incorrect segmentation involves part of the background being considered as the object of interest, and vice versa [1], reducing accuracy of the subsequent machine vision processes
The main objective of the present research is to design a new algorithm to segment apples on clustering for applications with noise (DBSCAN), to generate superpixel regions which preserve trees using images extracted from features video under different natural light use of this edges
Summary
Segmentation is an important step in designing machine vision systems for agricultural and gardening purposes. It is one of the most difficult and critical parts of such systems, since background can contain objects with a wide variety of colors and textures similar to those of the plants. Incorrect segmentation involves part of the background being considered as the object of interest, and vice versa [1], reducing accuracy of the subsequent machine vision processes. (b,d,f,h,j) Corresponding segmented images, with background in black (a,c,e,g,i) Original color images. (b,d,f,h,j) Corresponding segmented images, with background in black
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