Fish disease detection using AI

1. ✔ Title
✔ Authors
✔ Affiliations
✔ Contacts (email)

2. The Importance of Fish forming Is the Fish diseases affect the human
The current world population of 7.2 billion is projected to increase by 1 billion over the next
12 years and reach 9.6 billion by 2050 (UNFAO 2014). With this steadily rising population
pressure on the earth’s resources, as well as its political systems, social structures, and food
production alternatives, there are many challenges to reaching the goal of a sustainable
planet (Brown 2009; Friedman 2008). Two of the most important challenges are sufficient
food and sufficient energy; both from sustainable sources.
Diseases of fishes can be important because they may induce mortality.
Additionally, sublethal diseases may cause poor growth onversion, poor
flesh quality, or undesirable visual changes. furthermore, some
pathogens of fish are infectious to humans
Infections or infestations of animals that can be transmitted to humans
are called zoonoses. Although cases of human disease arising from fish
and shellfish are rare in Australia, there are a few ‘fish diseases’ that
workers in the aquaculture and fishing industries need to be conscious of
when handling or processing fish
Conventional Methoda to
Identify the Fish Dieases
1. Clinical Observation:
2. Microscopic Examination
3. Bacterial Culture:
4. PCR (Polymerase Chain Reaction): -
5. Histopathology: -
6. Serological Tests:
7. Genomic Sequencing:
8. Immunohistochemistry:
Speed of diagnosis is always a concern,
especially with acute losses relying on
histopathology, ultrastructural
confirmation or long periods of
tissue/media culture. The time span
required for confirmatory diagnosis is
frequently overcome by remedial
action being based on presumptive
diagnoses such as tissue smears, gross
pathology or behavioural changes

3. The essential contribution of aquaculture to global food security underscores the urgency for inventive approaches to promptly identify and address fish diseases. Conventional
methods encounter difficulties in fully grasping the subtle patterns signaling these ailments within varied aquatic settings. The complex dynamics of fish health necessitate the
utilization of advanced technologies to surpass the constraints inherent in traditional approaches
The fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) represents a groundbreaking solution. LSTM, known for its capability to capture temporal
dependencies, effectively tackles the challenges related to observing gradual shifts in fish behavior over time. Concurrently, CNN, celebrated for its prowess in image recognition,
amplifies the recognition of visual indicators linked to diverse fish diseases. The integration of these cutting-edge technologies assures the development of a more precise,
streamlined, and timely fish disease detection system, with the overarching goal of reducing economic losses and fostering the advancement of sustainable practices within the
aquaculture industry.
Drawbacks in diagnostic diseases Complexity
Conventional identification techniques, which heavily depend on manual
observation and rule-based systems, are susceptible to subjectivity and
insufficient precision essential for the timely detection of diseases. The potential
oversight of visual cues and behavioral shifts may result in an underappreciation
of the gravity of health risks. This limitation emphasizes the pressing need for a
refined and automated strategy in the identification of fish diseases
Identifying fish diseases presents inherent challenges due to the nuanced and
ever-changing dynamics of aquatic ecosystems. Traditional approaches
frequently encounter difficulties in recognizing initial symptoms promptly,
resulting in delayed responses and heightened financial implications. The
diverse factors shaping fish health, including water quality and species-specific
behaviors, compound the intricacies associated with timely disease detection.
Introduction
Proposed Diagonistic method

4. Tools used in Fish disease detection
Image processing tools, such as OpenCV and TensorFlow, are employed in fish detection to analyze
aquatic images. These tools enhance feature extraction, pattern recognition, and segmentation, enabling
automated identification of fish species, health conditions, and behaviors. They streamline data analysis,
support research, and contribute to efficient monitoring in aquaculture.
Here Static image capturing mode is
used. dynamic capturing is not used but
this proposed system can be extended to
dynamic with use of ML models and IoT
sensors
Category Diseases
Eyes - Cloudy Eye, - Exophthalmia (Popeye), - Ocular
Lesions
Fins - Fin Rot, - Tail Rot, - Fungal Infections on Fins
Skins - Ichthyophthirius (Ich or White Spot Disease)
- Columnaris (Cotton Wool Disease), -
Dermocystidium (Dermal Cyst), - Skin Ulcers
Gills - Gill Flukes, - Parasitic Infections, - Ammonia Burns
Internal - Ascites (Abdominal Dropsy),- Swim Bladder
Disorders,- Internal Parasites
General - Dropsy (Edema), - Bacterial Infections, - Viral
Infections, - Nutritional Deficiencies
Category and Type of Diseases
Overview of Proposed solution

5. Results
Increased Accuracy: The fusion of LSTM and CNN facilitates the concurrent examination of both sequential data, encompassing temporal patterns, and image data,
resulting in a more thorough comprehension of fish health indicators. LSTM's proficiency in capturing temporal dependencies contributes to
identifying nuanced changes over time, thereby improving the precision of disease detection..
Early Detection LSTM and CNN collaboration identifies early symptoms often missed by traditional methods, while CNN excels in recognizing visual cues
related to different fish diseases.
Adaptability to Diverse
Environments:
Fish health is influenced by a range of factors such as water quality and species-specific behaviors. The proposed solution's ability to process
both sequential and image data makes it more adaptable to the dynamic nature of aquatic environments.
Proactive Disease
Management
The early detection capabilities of the model enable fish farmers and aquaculturists to take proactive measures in managing and mitigating the
spread of diseases.
Timely intervention can significantly reduce economic losses and improve the overall health of fish populations.
Comprehensive Analysis: Traditional methods often focus on specific aspects of fish health, whereas the proposed solution provides a holistic approach by considering
both temporal and visual information.
This comprehensive analysis contributes to a more thorough understanding of the multifaceted aspects of fish diseases.
Efficient Resource
Utilization
By leveraging advanced machine learning techniques, the proposed solution optimizes resource utilization by automating the detection
process. This reduces the need for manual monitoring and allows for more efficient use of human resources in aquaculture operations.
Scalabilty This proposed solution is scalable and can be further improved using sensors
Benefits against proposed solution

6. Methods Results Critical Analysis
Methodology LSTM and CNN
The use of LSTM and CNN provides enhanced accuracy and early detection capabilities, surpassing traditional methods. However,
there are differences compared to other algorithms in terms of economic, technological, and methodological aspects.
Results Comparison with
Existing Literature
Improved accuracy and early detection
While the proposed LSTM and CNN model yields favorable results, differences in datasets, training parameters, and disease
prevalence across studies can influence outcomes. Comparisons with existing literature highlight variability in reported accuracies.
Economic Implications
Potential cost-effectiveness through
automated detection
Economic considerations involve initial setup costs and computational requirements. However, automated detection can lead to
long-term economic benefits by reducing labor-intensive manual monitoring and minimizing economic losses from disease
outbreaks.
Technological Differences Advanced AI and ML integration
Technological advancements in AI and ML contribute to improved disease detection. Disparities in technology infrastructure may
impact the accessibility and implementation of these methods across diverse aquaculture environments.
Weaknesses of the Proposed
Methods
Model complexity, interpretability challenges
The combined use of LSTM and CNN may lead to complex models that are difficult to interpret, hindering user understanding and
trust. Balancing model complexity with interpretability remains a challenge for effective implementation in real-world scenarios.
Reality Check (Economic
Considerations)
Initial costs vs. long-term benefits
While initial setup costs may be higher, the long-term benefits, including reduced labor costs and minimized economic losses,
position the proposed method as a viable and economically advantageous solution for fish disease detection in aquaculture.
Promising Directions for
Future Research
Explainability, user-friendly
interfaces, dataset diversity
Future research should focus on enhancing model interpretability, developing user-friendly interfaces, and
exploring diverse datasets.
Literature survey
Comparison of Fish Disease Detection

7. Conclusion
In conclusion, the synthesis of our research on fish disease detection using LSTM and CNN underscores the promising potential of integrating advanced artificial intelligence techniques
in aquaculture health management. Our methodology, building upon existing literature, demonstrates advancements in accuracy and early-stage detection. The economic reality of
aquaculture settings, including considerations of affordability and resource accessibility, plays a pivotal role in shaping the feasibility and sustainability of proposed solutions.
While the research hypothesis centered around the effectiveness of LSTM and CNN in fish disease detection is substantiated by positive outcomes, it is essential to acknowledge the
inherent challenges and potential limitations. Economic constraints and the need for substantial computational resources may influence the widespread adoption of these technologies
in diverse aquaculture contexts. However, the concept remains industrially promising, offering a proactive and technologically sophisticated approach to disease management.
Moving forward, interdisciplinary collaboration between AI experts, aquaculture scientists, and economists is crucial to address these challenges and refine our methodologies. Future
research endeavors should focus on enhancing model generalization, incorporating explainable AI techniques, and exploring practical implementations, such as edge computing and
sensor integration. By navigating these avenues, we aim to bridge the gap between technological innovation and economic sustainability, ultimately contributing to the advancement of
effective and economically viable fish disease detection solutions in the aquaculture industry.