Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan, Arif; Hadiyanto, Hadiyanto; Widodo, Catur Edi

doi:10.18280/ria.360611

Cited by 9 publications

(4 citation statements)

References 51 publications

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“…The second step employs deep learning techniques, involving preprocessing followed by the training and testing processes. In other studies, a digital camera was used to capture data of shrimps underwater [49,50], In this research, the egg data was obtained from local chicken farms directly. The areas of cracked and intact eggs are captured entirely by a camera using the following method: a blue-colored plate is placed with an egg on it, and then a camera is positioned above the egg to capture the image.…”

Section: Methodsmentioning

confidence: 99%

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Setiaji,

Adi,

Surarso

2024

ISI

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Eggs are widely consumed due to their high content of vitamin B12, choline and iron. External factors can be detected by observing the eggshell or taken into consideration. The nutrition in eggs is influenced by egg quality, which can be directly observed from the shell. If the shell is cracked, it can be inferred that the egg has poor quality because Salmonella bacteria are dangerous pathogens that can enter the egg. The current issue lies in the complexity and inefficiency of individually classifying eggs by workers, as it is a complicated, time-consuming, frustrating, and inefficient task. Therefore, it is important to separate them automatically. The selection of cracked and intact eggs in this research is an innovative approach to classification using a highly accurate machine learning method. The application of the GLCM-CNN method is an innovative strategy employed for selecting and classifying cracked eggs, as outlined in this research. VGG 19, one of the computational methods, is utilized as a comparative method alongside RESNET 50 and VGG 16. The GLCM-CNN algorithm in this research employed 1,000 images for each class, with a validation set of 20% for each class, resulting in an accuracy of 98%. The inefficient classification process and complexity of automated egg quality classification can be significantly addressed through the findings presented in this research.

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Section: Methodsmentioning

confidence: 99%

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Setiaji,

Adi,

Surarso

2024

ISI

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“…In [30], a new method for shrimp biomass estimation using morphometric features based on underwater image analysis and a machine learning approach is developed. The single camera is calibrated using triangle similarity (TS) and correction factor (CF), resulting in a hybrid method based on the triangle of similarity, correction factor, and multilinear regression (TS-CF-MLR).…”

Section: Imagementioning

confidence: 99%

“…These methodologies have been applied by the authors to only one species, and most of them extract only a few features that could aid in the morphological development analysis of the life stages of a species. Others, such as [30], extract morphometric features that are very specific to one species, and [22] extract features that can be used in multiple species but do not contemplate many form parameters directly related to shape and not size only. Essentially, most of the works deal with allometric relationships of a fish under specific conditions and positions.…”

Section: Imagementioning

confidence: 99%

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning

Ramírez-Coronel,

Rodríguez-Elías,

Esquer-Miranda

et al. 2024

Animals

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Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.

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“…Prediction of these landmarks then was used to automate morphological and weight measurements. Similarly, Setiawan et al (2022) 127 used underwater cameras to capture images of L. vannamei and KNN regression machine learning to estimate the weight of live shrimp. In relation to calculation of genetic merit of shrimp in genomic-based breeding programs, machine learning has also been evaluated against different genomic selection models and shown to have potential to improve accuracy of prediction over GBLUP approaches 65 .…”

Section: Future Directionsmentioning

confidence: 99%

100 years domestication of penaeid shrimp and meta-analysis of breeding traits

Ren,

Yáñez,

Perez-Enriquez

et al. 2024

Preprint

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Penaeid shrimp farming plays a pivotal role in ensuring future food security and promoting economic sustainability. Compared to the extensive long history of domestication observed in terrestrial agriculture species, the domestication and selective breeding of penaeids are relatively recent endeavors. Selective breeding aimed at improving production traits holds significant promise for enhancing efficiency and reducing the environmental impact of shrimp farming, thereby contributing to its long-term sustainability. Assessing genotype-by-environment (G-by-E) interactions is essential in breeding programs to ensure that improved penaeid shrimp strains perform consistently across different production environments, with genomic selection proving more effective than sib-testing alone in mitigating environmental sensitivity. Genome editing tools like CRISPR/Cas9 offer significant potential to accelerate genetic gains in penaeid shrimp by enabling rapid introduction of desired genetic changes, with recent advancements showing promising results in achieving high transfection efficiency in shrimp embryos. Additionally, artificial intelligence and machine learning are being leveraged to streamline phenotyping and enhance decision-making in shrimp breeding and farming, improving efficiency and accuracy in managing traits and predicting disease outbreaks. Herein, we provide an overview and update on the domestication of penaeid shrimp, including the current status of domestication for principal farmed species, key milestones in domestication history, targeted breeding traits in selective breeding programs, the advantages of integrating genomeic selection for enhancing production traits, and future directions for selective breeding of penaeid shrimp.

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Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Cited by 9 publications

References 51 publications

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning

100 years domestication of penaeid shrimp and meta-analysis of breeding traits

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