AI-assisted Automated Pipeline for Length Estimation, Visual

Assessment of the Digestive Tract and Counting of Shrimp in

Aquaculture Production

Yousif Hashisho

1 a

, Tim Dolereit

1 b

, Alexandra Segelken-Voigt

2 c

, Ralf Bochert

2 d

and Matthias Vahl

1 e

Fraunhofer Institute for Computer Graphics Research IGD, Joachim-Jungius-Str. 11, 18059 Rostock, Germany

Institute of Fisheries, State Research Centre of Agriculture and Fisheries Mecklenburg-Vorpommern,

udstraße 8, 18375 Born, Germany

Keywords:

Computer Vision, Image Processing, AI, Deep Learning, Shrimp, Aquaculture.

Abstract:

Shrimp farming is a century-old practice in aquaculture production. In the past years, some improvements

of the traditional farming methods have been made, however, it still involves mostly intensive manual work,

which makes traditional farming a neither time nor cost efﬁcient production process. Therefore, a continu-

ous monitoring approach is required for increasing the efﬁciency of shrimp farming. This paper proposes a

pipeline for automated shrimp monitoring using deep learning and image processing methods. The automated

monitoring includes length estimation, assessment of the shrimp’s digestive tract and counting. Furthermore,

a mobile system is designed for monitoring shrimp in various breeding tanks. This study shows promising

results and unfolds the potential of artiﬁcial intelligence in automating shrimp monitoring.

1 INTRODUCTION

Aquaculture production of whiteleg shrimp, namely

Penaeus vannamei, is steadily increasing (Boone,

1931) and accounts for more than half of the world-

wide crustacean production (FAO, 2020). The aver-

age farming cycle, e.g. the time to raise the animals

from larvae to adult shrimp, has a duration of 5 – 6

months. During this cycle, the shrimp remain in their

production system until they reach a harvest weight

of 25 – 30 grams. However, due to their high sensi-

tivity, manual growth measurements are only possible

to a limited extent and in addition, it is hardly feasible

to gauge the exact number of animals in the system.

Nonetheless, the knowledge of stocking densities or

quantity of shrimp is important for optimized feeding

and to determine the optimal harvest period (Harbitz,

2007). These parameters are crucial for avoiding non-

adequate feeding during production and assessing the

growth and health development of the shrimp. For

https://orcid.org/0000-0002-7642-8949

https://orcid.org/0000-0002-4499-8146

https://orcid.org/0000-0002-0825-4975

https://orcid.org/0000-0002-8159-6955

https://orcid.org/0000-0003-1183-5823

example, erroneous or missing knowledge of shrimp

count and densities in the production system may lead

to over-feeding, resulting in higher costs and a de-

cline in water quality due to higher waste. Contrarily,

under-feeding might result in growth depressions and

cannibalism. (Roy et al., 2012).

Therefore, it is inevitable to adjust the feeding

rates based on control measurements (e.g. average

length or weight of shrimp samples) and counting for

achieving optimal growth and survival rates. As feed

takes approximately 60 minutes to pass through the

gut (Beseres et al., 2005), the degree of ﬁlling of the

digestive tract is an appropriate discernible and mea-

surable criterion for assessing the state of the feeding

rate. However, the currently employed methods for

assessing feeding status and quantities involve time-

consuming manual work and are therefore prone to

human-error.

In this paper, the potential of artiﬁcial intelligence

in automated shrimp monitoring is assessed. To in-

crease the efﬁciency of the shrimp farming process,

we propose the following:

• A ﬂexible and easy to install monitoring system

for various shrimp production systems.

710

Hashisho, Y., Dolereit, T., Segelken-Voigt, A., Bochert, R. and Vahl, M.

AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production.

DOI: 10.5220/0010342007100716

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP, pages

710-716

ISBN: 978-989-758-488-6

• An approach for estimating the length of shrimp

with the aid of instance segmentation.

• An approach that combines instance segmentation

and image classiﬁcation for efﬁciently assessing

the shrimp’s digestive tract.

• A multi-feature shrimp counting approach, which

reduces the problems with overlapping shrimp.

By means of this automated monitoring approach,

a time- and cost-efﬁcient shrimp production can be

achieved. The structure of the paper is as follows. In

Section 2, the related work is analyzed. In Section 3,

the monitoring system, the dataset and the automated

shrimp monitoring pipeline are presented. In Section

4, the results of the different approaches are discussed

and Section 5 concludes the paper.

2 RELATED WORK

2.1 Counting

Shrimp counting has been an interest for researchers

over recent years. Most of the methods focus on con-

ventional image processing techniques to automate

the shrimp counting process.

Khantuwan and Khiripet (2012), took gray-scale

images of shrimp larvae and improved the contrast

of larvae edges using Laplacian and Median ﬁlters

followed by adaptive thresholding for reducing non-

uniform illumination. They determined a statistical

measure in the form of a histogram for a ﬁrst stage

counting and used template matching for a second

stage counting. Kaewchote et al. (2018) automated

the counting of post larvae shrimp by extracting fea-

tures using Local Binary Patterns followed by identi-

fying objects using a Random Forest classiﬁer. An-

other study by Awalludin et al. (2019) proposed com-

bining anisotropic diffusion with a canny edge de-

tector followed by blob analysis for counting shrimp

larvae. Solahudin et al. (2018) applied thresholding

and dilation to estimate the count of whiteleg shrimp.

Similarly, Yeh and Chen (2019) applied image thresh-

olding for extracting contours to be used for counting.

Most of these methods are restricted to a speciﬁc

application setup. Extending them to a different setup

requires a reconﬁguration of the applied techniques,

like adjusting the threshold values if the lighting dif-

fers or if the color degrades due to a different produc-

tion tank. This would require an input from the user

and would thus be prone to more errors and the auto-

matic process might be impaired.

An alternative to conventional image processing

techniques is deep learning, which enables feature

learning from the input data and which is robust to

noise and illumination variations. Deep learning out-

performs traditional image processing techniques and

surpassed its limitations (O’Mahony et al., 2019).

However, there are hardly any studies for the appli-

cation of deep learning in shrimp counting.

In a recent study by Nguyen et al. (2020), a deep

learning-based method for whiteleg shrimp larvae

counting has been applied. They prepared a dataset

of shrimp in a glass container with low water level to

minimize overlaps. This dataset was used to train an

instance segmentation network based on a two-phase

Mask R-CNN to detect shrimp larvae in regions with

overlaps. Their approach works well with a small

number of overlapping shrimp, however, the accuracy

substantially decreased with more overlaps.

In our method, we focus on detecting two major

features of the shrimp using a deep learning object de-

tection network to reduce the problems with overlap-

ping shrimp and increase the accuracy of automated

shrimp counting.

2.2 Length Estimation

Harbitz (2007) segment shrimp automatically based

on an intensity threshold. Afterwards, objects are

identiﬁed and their pixel areas are calculated, which

in turn are used for separating shrimp objects based on

an area threshold. This separation enables identifying

shrimp based on centroid values. Then the length is

estimated by linking each shrimp’s area with a cor-

responding caliper measurement. The estimation of

shrimp length by Harbitz (2007) is dependent on mul-

tiple analysis and values prediction, which in return

make it hard to generalize and be robust to changes.

To achieve a robustness to change, our approach

for automated length estimation utilizes the results

from instance segmentation, while being facilitated

by our monitoring system.

2.3 Digestive Tract Assessment

To our knowledge, no research has yet been done on

the visual assessment of the digestive tract of shrimp.

In shrimp, the digestive tract is a straight tube run-

ning dorsally, which is divided into three regions: the

foregut, the midgut, which begins at the junction of

the hepatopancreas, and the hindgut (Franceschini-

Vicentini et al., 2009; Davie et al., 2015). It is ex-

ternally identiﬁable when ﬁlled, and therefore well

suited for image analysis.

In this paper, we present a methodology for auto-

matically assessing the digestive tract’s condition.

AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture

Production

711

60 cm

40 cm

10 cm

15 cm

60 cm

Floating plate

Side walls

Underwater plate

(white background)

Recording camera

Figure 1: Shrimp monitoring system. Left: Sketch of the system design. Right: The installed system in an experimental tank.

3 METHODOLOGY

3.1 Monitoring System Design

There are various types of shrimp production tanks.

Therefore, the scenarios differ, for example, in water

depth, water quality or tank shape. As part of this

study, a mobile system was designed for recording

images of shrimp in an area of interest, which enables

the application of the proposed methodology to vari-

ous scenarios.

Figure 1 presents a sketch of the designed sys-

tem along with the installed setup used for automated

shrimp monitoring. The system consists of a ﬂoat-

ing box made of transparent perspex (top is open) and

an underwater plate made of white plastic, reducing

the water column to 10 cm. The dimension of the

ﬂoating box is 60 × 40 × 15 cm, whereas the under-

water plate’s dimension is 60 × 40 cm. This setup al-

lows only a small number of shrimp in the water col-

umn and therefore, reduces the number of overlapping

shrimp by design. The underwater plate is equipped

with markings for an area of interest of dimension

40 × 20 cm, where analysis and evaluation are based

on. The shrimp can swim freely through the moni-

toring system. The camera is placed orthogonal at a

height of 60 cm on top of the ﬂoating box, capturing

images through its transparent bottom. This prevents

image distortions due to a wavy water interface and

allows a clear view into the water column, regardless

of the external factors of the experimental tank. This

provides a good prerequisite for the proposed moni-

toring approaches even in challenging tank environ-

ments.

3.2 Dataset

Images were taken of the shrimp with a resolution of

3088 × 2076 pixels, where various focal lengths and

exposure times had been tested. At that point, most

of the shrimp had sizes between 9 and 12 cm. Fig-

ure 2 shows a sample of the recorded images and the

corresponding cropped area of interest based on the

measured markings on the underwater plate. As can

be seen, the shrimp images can be considered as high

quality.

Figure 2: A sample image of the monitoring system (left)

and the cropped area of interest (right).

The recorded images were divided into several

datasets. For the instance segmentation and object de-

tection network, the recorded high resolution images

were split into 4 equally sized images with a resolu-

tion of 1544 × 1038 pixels. The classiﬁcation dataset

was obtained from the instance segmentation network

predictions, which will be discussed in the next sec-

tion. The overall number of images used for the in-

stance segmentation, object detection and classiﬁca-

tion networks were 898, 410, and 292, respectively.

Finally, 150 images were cropped to the deﬁned area

of interest and used for evaluating the results of the

automated monitoring approach. Table 1 illustrates

the datasets used for each deep learning network and

for evaluation.

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

712

Mask R-CNN

Faster R-CNN

ResNet10

Length Image

Processing

Length Measurement

Counting

Digestive Tract

Assessment

Digestive Tract Image

Processing

Instance Segmentation

Object Detection

Classiﬁcation

Input Image

Counting Image

Processing

Figure 3: The proposed pipeline for length measurement, digestive tract assessment and counting.

Table 1: Number of images used for each category.

Training Validation Overall

Instance Segmentation 701 47 898

Object Detection 234 26 410

Classiﬁcation 212 40 292

Evaluation 150

3.3 Automated Shrimp Monitoring

The captured images were processed by our pipeline

for automated shrimp monitoring (Figure 3). The

pipeline consists of two main branches. The upper

branch begins with an instance segmentation network,

namely Mask R-CNN (He et al., 2017), with a combi-

nation of Feature Pyramid Network (FPN) (Lin et al.,

2017) and deep residual network (ResNet) (He et al.,

2016) for feature extraction, which is used for detect-

ing and segmenting individual shrimp. The length is

then determined by processing the outputs of the in-

stance segmentation network, whereas the image clas-

siﬁcation network (ResNet (He et al., 2016)) is used

for assessing the digestive tract of a shrimp. The

lower branch consists of the Faster R-CNN network

(Ren et al., 2017) for detecting two unique body parts

of each shrimp. Afterwards, the detected body parts

are processed for shrimp counting.

3.3.1 Length Estimation

The shrimp were assumed to be located approxi-

mately at the same distance from the camera as the

underwater plate. Therefore, the measured markings

on the plate were used to obtain a scale factor to con-

vert between pixels and centimeters. Upward swim-

ming shrimp violate this assumption and hence were

excluded from the annotation. This exclusion does

not distort the overall monitoring process, since the

major swimming direction of the shrimp is horizontal

and since not each individual shrimp needs to be mea-

sured to get a signiﬁcant length distribution. Since the

length of shrimp in this study is calculated from the

top of the rostrum (beak) to the end of the telson (ﬁn),

only horizontally swimming, non-overlapping, com-

pletely visible shrimp were annotated and used for

training of the instance segmentation network. This

was a crucial step for forcing the network to detect

and segment measurable shrimp only.

The trained instance segmentation network was

used for predicting a bounding box and a segmen-

tation mask for each individual shrimp. Figure 4 il-

lustrates the processing steps. The predicted segmen-

tation mask was converted into contours and the ex-

treme left, right, top and bottom contour points were

deduced (Figure 4 (b)). The pair of deduced extreme

points with the longest distance were assumed to form

a line passing through the rostrum and the telson. The

two intersecting points of the line and the shrimp’s

bounding box were used to calculate the length of the

shrimp in pixel (Figure 4 (c)). Finally, the scale factor

was used to get the length estimate (Figure 4 (d)).

3.3.2 Digestive Tract

The main goal was to analyze the condition of the

shrimp’s digestive tract. More precisely, the degree

of ﬁlling of the digestive tract. For that, the inferred

segmentation masks of the instance segmentation net-

work were used for extracting a dataset. Therefore

each segmented shrimp was cropped from the original

image and then rotated and translated to the same ori-

entation (Figure 5). This dataset was used for training

the classiﬁcation network, which eventually should

classify the shrimp into two categories: full and other

shrimp. The amount of visible shrimp in test data

didn’t allow for more categories. Figure 5 shows the

two different shrimp categories based on the degree

AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture

Production

713

Figure 4: Length estimation approach: from left to right (a) Original. (b) Inferred results and extreme points. (c) Derived line

and intersecting points. (d) The estimated length in centimeters.

of ﬁlling of the digestive tract. As can be seen in the

images, shrimp with a sharp ﬁlled line are considered

as full, whereas the ones with light or no ﬁlled line

are considered as other. Within our pipeline for au-

tomated shrimp monitoring (Figure 3), after an im-

age passed the instance segmentation network, the in-

ferred results were forwarded for image transforma-

tion and classiﬁcation. Finally, the predicted classiﬁ-

cation results were used to assess the condition of the

shrimp’s digestive tract.

3.3.3 Counting

A separate branch in the pipeline was used for the

counting task, since it is independent of the previ-

ous results. An object detection network was trained

on two body parts of the shrimp, namely the pair of

eyes and the hepatopancreas, since most of the time,

one of these is visible even in overlapping situations.

The results of the object detection were forwarded for

further processing. The processing included calcu-

lating the shortest distance between a centroid of a

predicted hepatopancreas bounding box and each eye

pair bounding box. If this distance didn’t exceed 165

pixels (2.3625 cm), the corresponding hepatopancreas

and eye pair bounding boxes were considered as be-

longing to one object. The distance value was cho-

sen heuristically. Otherwise, the corresponding boxes

were considered as belonging to two separate shrimp

and were counted as two objects. Afterwards, both

centroids are removed and the process is continued it-

eratively. Figure 6 shows an overview of the counting

process. The bounding boxes in blue color (Figure 6

(b)) are considered as one object, whereas the ones

in red color are considered as separate individual ob-

jects.

4 RESULTS AND DISCUSSION

4.1 Length Estimation

For evaluation of the length estimation, a comparison

with manual measurements was performed. There-

fore, 625 horizontally swimming, non-overlapping,

completely visible shrimp have been manually mea-

sured in a set of 150 evaluation images. The same

evaluation images were used for predicting individual

shrimp using the trained instance segmentation net-

work, and ultimately, measuring the length. To have a

feasible evaluation of the measurement accuracy, dis-

tributions of the manual and predicted measurements

were calculated. Figure 7 shows the length distribu-

tion of both the manual (ground-truth) and the pre-

dicted measurements. In contrast to the ground-truth

(625 shrimps), only 401 shrimp could be detected and

measured by our pipeline for automated shrimp mon-

itoring. Looking at the distribution, shrimp below ap-

proximately 9 cm were not detected very often. This

implies that the instance segmentation network has a

weakness in detecting small shrimp, which is proba-

bly due to an imbalanced dataset. A more balanced

dataset with more diverse training data would be a

solution for addressing this issue. Nonetheless, the

predicted length distribution matches the ground-truth

one, where most shrimp have a length value between

9–12 cm. Both resemble a normal distribution.

4.2 Digestive Tract

At the time of capturing, most of the shrimp showed

a high degree of ﬁlling of their digestive tract (cate-

gory full). This made it hard to collect enough data

for the other category and impossible to deﬁne fur-

ther categories, which would lead to a more signiﬁ-

cant analysis of the feeding status. We tried to main-

tain a relatively small balanced dataset instead of a

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

714

Figure 5: Dataset for training the classiﬁcation network. Top row: Full category. Bottom row: Other category.

Figure 6: Counting approach: Left: Object detection inference. Right: Counting Result.

Figure 7: Calculated distribution of shrimp length estima-

tion in 150 evaluation images. Left: Ground-truth length

distribution. Right: Predicted length distribution.

large imbalanced one. Table 2 shows the confusion

matrix of the trained classiﬁcation network. It can be

seen that 51 shrimp of the full category were correctly

classiﬁed out of 52, whereas 45 shrimp of the other

category were correctly classiﬁed out of 52. As was

expected, the network didn’t perform as well on the

other category as on the f ull one. Having a larger

dataset with more shrimp in the other category would

boost the performance of the classiﬁcation network.

Since there is a direct relation between the instance

segmentation results and the classiﬁcation network,

improving the results of the former would lead to a

better training dataset for the latter and ultimately bet-

ter performance.

Table 2: Confusion matrix of the digestive tract classiﬁca-

tion network.

Predicted

Full Other Sum

Actual

Full 51 1 52

Other 7 45 52

Sum 58 46 104

4.3 Counting

For the evaluation of the automated counting task,

three different people counted the shrimp in the set

of 150 evaluation images manually. Compared to the

manual counting, the mean percentage error over the

evaluation images amounted to 6.6 %, with a highest

counting percentage error of 23.8 %. After interpret-

ing the results, the drawn conclusions read as follows:

1. Captured images with many overlapping shrimp

cause the network to miss some of them. 2. The

reason for that is a partially hidden hepatopancreas

and/or pair of eyes. 3. As a solution for addressing

this problem, a third body part of the shrimp could

be added to the detection problem along with the hep-

atopancreas and the pair of eyes. 4. Alternatively, par-

tially hidden body parts should be annotated as well

to improve the network’s performance in overlapping

situations. With this approach the shrimp count can

be estimated in an area of interest. If that area can

be considered as a representative, the counting can be

utilized for extrapolation to the whole tank. However,

the utilized area turned out to be too small. Therefore,

the present results are considered as proof of concept

of the proposed approach.

5 CONCLUSION

This paper shows the feasibility of utilizing artiﬁ-

cial intelligence in automated shrimp length estima-

tion, digestive tract assessment and counting. Further-

AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture

Production

715

more, a ﬂexible and easy to install monitoring sys-

tem for various shrimp production systems was pre-

sented. By means of the proposed automated moni-

toring approach, a more time and cost efﬁcient shrimp

production can be achieved to increase the efﬁciency

of the shrimp farming process. Certainly, as men-

tioned in the previous section, there is room for im-

provement within all three mentioned use-cases. This

could be a subject for future research. Additionally,

it became clear that the designed system should cover

a signiﬁcantly larger area than 40 × 20 cm. Approx-

imately 100 × 100 cm would be a better ﬁt, which

would make the counting task more meaningful. In

addition to all the considered use-cases, an automated

detection of anomalies in the external appearance of

shrimp could be an interesting use-case for future

works.

ACKNOWLEDGEMENTS

Funding (code: B 730217000011) for ASV came

from the European Fisheries Fund (EFF) and the State

Mecklenburg-Vorpommern.

REFERENCES

Awalludin, E. A., Mat Yaziz, M. Y., Abdul Rahman, N. R.,

Yussof, W. N. J. H. W., Hitam, M. S., and T Ar-

sad, T. N. (2019). Combination of canny edge detec-

tion and blob processing techniques for shrimp larvae

counting. In 2019 IEEE International Conference on

Signal and Image Processing Applications (ICSIPA),

pages 308–313.

Beseres, J. J., Lawrence, A. L., and Feller, R. J. (2005).

Variation in ﬁber, protein and lipid content of shrimp

feed – effects on gut-passage time measured in the

ﬁeld. Journal of Shellﬁsh Research, 24(1):301–308.

Publisher: National Shellﬁsheries Association.

Boone, L. (1931). Anomuran, macruran Crustacea from

Panama and Canal Zone. Number 63 in Bulletin of

the American Museum of Natural History. American

Museum of Natural History.

Davie, P. J. F., Guinot, D., and Ng, P. K. L. (2015). Anatomy

and functional morphology of Brachyura. Treatise

on Zoology - Anatomy, Taxonomy, Biology. The Crus-

tacea, Volume 9 Part C (2 vols), pages 11–163. Pub-

lisher: Brill Section: Treatise on Zoology - Anatomy,

Taxonomy, Biology. The Crustacea, Volume 9 Part C

(2 vols).

FAO (2020). The State of World Fisheries and Aquaculture

2020: Sustainability in action. FAO, Rome.

Franceschini-Vicentini, I. B., Ribeiro, K., Papa, L. P., Mar-

ques Junior, J., Vicentini, C. A., and Valenti, P. M.

C. M. (2009). Histoarchitectural Features of the

Hepatopancreas of the Amazon River Prawn Macro-

brachium amazonicum. International Journal of Mor-

phology, 27(1):121–128.

Harbitz, A. (2007). Estimation of shrimp (Pandalus bore-

alis) carapace length by image analysis. ICES Journal

of Marine Science, 64(5):939–944.

He, K., Gkioxari, G., Doll

ar, P., and Girshick, R. (2017).

Mask r-cnn. In 2017 IEEE International Conference

on Computer Vision (ICCV), pages 2980–2988.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep resid-

ual learning for image recognition. In 2016 IEEE Con-

ference on Computer Vision and Pattern Recognition

(CVPR), pages 770–778.

Kaewchote, J., Janyong, S., and Limprasert, W. (2018).

Image recognition method using local binary pattern

and the random forest classiﬁer to count post larvae

shrimp. Agriculture and Natural Resources, 52(4):371

– 376.

Khantuwan, W. and Khiripet, N. (2012). Live shrimp

larvae counting method using co-occurrence color

histogram. In 2012 9th International Confer-

ence on Electrical Engineering/Electronics, Com-

puter, Telecommunications and Information Technol-

ogy, pages 1–4.

Lin, T., Doll

ar, P., Girshick, R., He, K., Hariharan, B., and

Belongie, S. (2017). Feature pyramid networks for

object detection. In 2017 IEEE Conference on Com-

puter Vision and Pattern Recognition (CVPR), pages

936–944.

Nguyen, K., Nguyen, C., Wang, C., and Wang, J. (2020).

Two-phase instance segmentation for whiteleg shrimp

larvae counting. In 2020 IEEE International Confer-

ence on Consumer Electronics (ICCE), pages 1–3.

O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli,

S., Hernandez, G. V., Krpalkova, L., Riordan, D., and

Walsh, J. (2019). Deep learning vs. traditional com-

puter vision. In Science and Information Conference,

pages 128–144. Springer.

Ren, S., He, K., Girshick, R., and Sun, J. (2017). Faster

R-CNN: Towards real-time object detection with re-

gion proposal networks. IEEE Transactions on Pat-

tern Analysis and Machine Intelligence, 39(6):1137–

1149.

Roy, L. A., Davis, D. A., and Whitis, G. N. (2012). Ef-

fect of Feeding Rate and Pond Primary Productivity

on Growth of Litopenaeus vannamei Reared in In-

land Saline Waters of West Alabama. North American

Journal of Aquaculture, 74(1):20–26.

Solahudin, M., Slamet, W., and Dwi, A. S. (2018). Vaname

(litopenaeus vannamei) shrimp fry counting based on

image processing method. IOP Conference Series:

Earth and Environmental Science, 147:012014.

Yeh, C.-T. and Chen, M.-C. (2019). A combination of iot

and cloud application for automatic shrimp counting.

Microsystem Technologies, pages 1–8.

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

716