## 1 Introduction  
Imaging object detection, nowadays, is widely used to identify objects. However, the most advantageous to the classification of moving objects [1], especially underwater object detection, has been a challenge because the water influences the recognition of the object [2]. The use of image analysis has produced applications that help in the agriculture to medicine fields [3]. On the other hand, in aquaculture, multiple tasks can be performed using imaging detection. However, there are also modern practices involving automation and intelligent technology [4]; technology such as the internet of thing (IoT) helps to obtain data from the tank culture with the use of sensors and avoid poor levels of oxygen, water contamination, parasites, or disease transmission [5]. Additionally, some examples are the regression methods used in aquaculture for the management and water quality predictions, time series methods, artificial neural network methods, and support machine methods [6]. Moreover, agriculture 4.0 contributes to the development of agriculture because crops can be cultivated much easier with less effort in less time, as well as every time the growing variables can be monitored by a sensor and displayed on a PC or cell phone screen: digital technologies such as cyber-physical (CPS), artificial intelligence (AI), wireless sensor networks (WSN), big data analytics (BDA), autonomous robots systems (ARS), and ubiquitous cloud computing (UCC) [7]. An essential aspect of aquaculture farms is fish growth; the fish's body length and width are related to fish weight. This information calculates the feeding ratio, fish size classification, and harvest [8]. Regularly, these data are collected manually, transforming into an expensive and tiring, as well as a waste of time, experience for intensive systems [9]; moreover, handling the fish with hands causes stress to it, and stress causes cortisol. It is accepted that long-term elevation in cortisol results in reduced food intake and growth in fish [10]. In addition to avoiding stress fish, there are different non-invasive methods, such as mathematical models or the length-weight relationship (LWR). They are based on numerical observations that can estimate fish biomass [11]; moreover, there is substantial demand for automatic fish monitoring systems, because these could improve efficiency and reduce the labor requirements in the aquaculture industry [12]. A machine vision system (MVS) consists of an image acquisition system, image processing, and statistical analysis [13], and MVS is a non-invasive technique for estimating fish mass and size, which has been attracting the interest of researchers because it avoids stress and injury [14]. Another vital aspect of MVS is that these fish sampling systems involve two tasks: fish detection, which discriminates fish from non-fish objects in underwater videos, and fish species classification, which identifies the species of the detected fish [15]. The quality of the images will depend on the light conditions at the time of the shooting; otherwise, if this factor is ideal, more accurate processing results will be achieved [16]. Some aquaculture installations have poor lighting, and near-infrared (NIR) computer vision technology is not affected by visible light intensity. It can obtain good imaging results in relatively dim light environments [17]. The NIR had other advantages: autofluorescence, deeper tissue penetration, and minimum photodamage to biological samples [18]. Additionally, the NIR has the rapid acquisition of sample data, the potential for simultaneous determination of several parameters, and the ability to replace destructive, expensive, and time-consuming conventional reference methods [19]. Another tool is Haar cascading by machine learning method; this uses a classifier from many positive and negative photos [20]; the advantage of using the machine learning method is short detection time, high detection rate, and strong adaptability to light changes [21]. In the case of machine vision system, ref.