Growth Measurement of Tomato Fruit Based on Whole Image Processing

Abstract

Crop grow measurement technologies are important to increase the farm productivity. Detection and measurement of fruit volume are useful for forecasting and harvesting applications. Some environmental challenges such as lighting conditions or occlusions make the fruit detection difficult. Our approach is based on features extraction from images through a sub-image clustering technique. Then images being described as a number of pixel in various labels are used in a regression model to estimate the fruit volume. The validity of the proposed method in experimental condition is successfully verified. The method is evaluated also in a field condition but results were inferior to the expectation. This paper tries to elucidate the reasons of the insufficient performance and tries to improve the proposed method in terms of illumination condition, precision and calculation time. Key Words: Agriculture, Field Robotics Growth measurement, Image processing, Tomato volume, Regression