Scene Graph Generation With Structured Aspect of Segmenting the Big Distributed Clusters

Abstract

Accurate fruit counting is one of the significant phenotypic traits for crucial fruit harvesting decision making. Existing approaches perform counting through detection or regression-based approaches. Detection of fruit instances is very challenging because of the very small fruit size compared to the whole size image of a tree. At the same time, regression-based counting techniques contributes impressive results but presents inaccurate results while number of instances increases. Moreover, most approaches lack scalability and are applicable only on one or two fruit types. This paper proposes a fruit counting mechanism that combines loose segmentation and regression counting that works on six fruit types: Apple, Orange, Tomato, Peach, Pomegranate and Almond. Through relaxed segmentation, fruit clusters are segmented to extract the small image regions which contain the small cluster of fruits. Extracted regions are forwarded for the regression counting of fruits. Relaxed segmentation is achieved through a state-of-the-art deconvolutional network, while modified Inception Residual Networks (ResNet) based nonlinear regression module is proposed for fruit counting. For segmentation, 4,820 original images, including corresponding mask images, of all six fruit types are augmented to 32,412 images through different augmentation techniques, while 21,450 extracted patches are augmented to 89,120 images used for the regression module training. The proposed approach attained a counting accuracy of 94.71% for individual fruit types higher than techniques reported in literature.

Keywords

• Deep learning
• segmentation
• fruit counting
• agricultural yield estimation
• economic growth
• agriculture