Advance Assessment and Counting of Ripe Cherry Tomato’s Via Yolo Model

Abstract

The use of intensive labor measurement and the computer-based techniques for the gathering of phenotypic information in the laboratories has been in trend previously. This study focuses the detection and counting of the cherry fruit during the growth of the plant in the greenhouse. It's unique because it uses the deep learning method for the imaging of fruit instead of the classical computer techniques of imaging. The Yolo method imaging has been used to detect the different steps of the growth of the cherry fruit in the greenhouse rather than in the laboratory. This is an advance method which closely detects the object and each pixel of the object; so that the results of this study are comparatively better than the earlier works with the classical methods. The use of Yolo method in this study is rather an innovative step in the field of agriculture which will be helpful in future not only in the collection of phenotypic information, but it will also be useful in the automation of the processes such as harvesting. The results attained have successfully evaluated the detection of cherry tomatoes along with counting clearly. After obtaining the results have 92.6% precision rate and  94.7% recall rate in case of detection, counting and assessment (Ripeness and unripens). Overall study can help to manage the better yield and better quality product after valuable assessment of cherry tomatoes via algorithms.