# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 1 Introduction  
Phalaenopsis are high-price plants, which need to be cautiously taken care of before they are sold. The orchid seedlings are traditionally irrigated by individually watering according to their growth condition. However, intensive manpower must be required for in-person watering, which would limit the massive production capacity of orchid seedlings, especially under the shortage of labors in some countries. For this intrinsic topic, the so-called _sprinkler irrigation_ is one of the most popular and economical irrigation systems for the greenhouse, where the seedlings are watered automatically by means of _showerheads_ installed over the assembly line. As being advantageous on watering a large area, with the addition of the machine vision, various schemes of global irrigation systems were proposed to locate and water the seedlings in the greenhouse [1; 2]. Nevertheless, a high volume of water is poured onto the top of the seedlings, which then suffer overmuch moist leaves, uneven watering, or yet a deluge of water at the root. Consequently, Phalaenopsis plants would be diseased. To solve this problem, this work aims at an effective vision-based watering approach to prevent plants on the assembly from being often mis-irrigated. In contemporary researches, the emergence of deep-learning based computer vision has contributed to detect and inspect the abnormal incidents in rather wide fields. For instance, a rapid recognition method was presented to examine the defects of electronic components [3]. Moreover, traffic conditions can be monitored by the computer vision techniques based on YOLOv3 (You Only Look Once version three) and spatial pyramid pooling (SPP) [4; 5]. A low-cost swine surveillance system was accomplished by an automated vision-based object detector, for husbandmen to manage a large-scale swine farm in a cost-effective manner [6]. Additionally, even the machine vision with deep learning becomes more and more popular for the object detection in several agricultural applications, e.g., the coffee beans and the orchid seedlings [7; 8; 9; 10; 11]. Over the past decade, a plenty of deep-learning based techniques have been launched for the object detection and recognition. For example, the region-based convolutional neural network (R-CNN) [12] was proposed by [PERSON] et al. to succeed in applying the deep learning on the object recognition, where a convolutional neural network (CNN) and a selective search for region proposals were hybridized for object detection and classification. Fast R-CNN [13] advanced the computational efficiency by utilizing the region of interest pooling (Rolpooling) to create the feature map for only single computation on the same region proposal, which was repeatedly computed in the classical R-CNN. Even though Fast R-CNN is superior to R-CNN in computational efficiency, both of them spend much time to acquire the region proposals by taking the same strategy--the selective search (SS). For this reason, the region proposal network (RPN) was firstly implemented in Faster R-CNN [14], to obtain the region proposals in an image frame by using neural networks to search positive and negative anchors, which then were classified by a _softmax_ function and were positioned more accurately by an optimal _bbox_ regression. Despite satisfactory accuracies of the R-CNN based methods, high computational cost would be required to result in a low detection speed when using general computers, e.g., desktops and laptops. To conquer the above-mentioned trouble, a new deep learning framework coined YOLO [15] was invented by [PERSON] et al. Unlike Faster R-CNN, YOLO considers the object detection as a regression problem and detects all interested objects of an entire image within the same period by the CNN, so it can perform better than R-CNN on the aspect of the computational efficiency. Soon afterwards, [PERSON] and [PERSON] proposed YOLOv2 [16], which specialized in better speed and accuracy for the object detection and recognition.