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.
Highlights
Yield estimation is becoming increasingly important in digital agriculture, which assists farmers to streamline harvesting resources which boost the cost-cutting for harvesting, enabling them to market the yield in a better way to get higher profits
Deep learning techniques are efficient enough to generalize across various fruit types and environments that are dynamic in lighting conditions
Almost all the known fruits have some common characteristics, such as circular shape, skin texture, and background which makes it a suitable fit to count the lack of big dataset for single fruit type
Summary
Yield estimation is becoming increasingly important in digital agriculture, which assists farmers to streamline harvesting resources which boost the cost-cutting for harvesting, enabling them to market the yield in a better way to get higher profits. The primary focus is to build a highly accurate deep learning mechanism and to develop a generalized approach so that model can be trained without prior knowledge about the type of the fruit. From the supervised learning point of view, annotating individual instances is a challenging task compared to annotating the segmented regions containing a small number of fruit instances. Generalization of the model is very important to learn directly from annotated data without explicit information about the fruit type. Driven by the inabilities of proposed techniques, we have devised a completely data-driven counting method based on loose semantic segmentation and direct regression form images. Our experiment illustrates that the proposed loose segmentation-based counting model obtains better and more efficient counting output than detection-based regression methods. The 5th section summarizes our work and feeds the future course
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