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Smart tools and artificial intelligence for enhanced quality and safety in agriculture, fisheries, and aquaculture: A review | ||
| Iranian Journal of Fisheries Sciences | ||
| دوره 24، شماره 4، مهر 2025، صفحه 913-951 اصل مقاله (824.33 K) | ||
| شناسه دیجیتال (DOI): 10.22092/ijfs.2025.134035 | ||
| نویسندگان | ||
| I. Kilinc* 1؛ B. Kilinc2؛ C. Takma3؛ Y. Gevrekci3 | ||
| 1Katip Celebi University, Fisheries Faculty, Fish Processing Technology Department, 35610 CiGli-Izmir, Turkiye | ||
| 2Ege University, Fisheries Faculty, Fish Processing Technology Department, 35100 Bornova-Izmir, Turkiye | ||
| 3Ege University, Agriculture Faculty, Animal Science Department, Biometry and Genetics Unit, 35100 Bornova-Izmir, Turkiye | ||
| چکیده | ||
| This study investigates the transformative potential of smart tools and artificial intelligence (AI) in enhancing quality assurance and safety within the agriculture, fisheries, and aquaculture sectors. A structured analytical framework is used to evaluate key AI algorithms—Naive Bayes, Support Vector Machines (SVM), Deep Learning, Machine Learning (ML), Artificial Neural Networks (ANNs), Fuzzy Logic, and Random Forests—emphasizing their mathematical foundations and practical integration into intelligent systems. The convergence of AI with advanced technologies such as computer vision (CV), the Internet of Things (IoT), and sensor-based monitoring is identified as a catalyst for real-time decision-making, robust quality control, and improved operational efficiency across the food supply chain. In agriculture, AI-powered tools enable precision farming, early pest and disease detection, and data-driven crop health monitoring. In fisheries and aquaculture, intelligent systems support automated feeding, disease prediction, and sustainable resource utilization. This study applies a structured literature-based analysis combined with performance benchmarking from empirical studies, showcasing validated use cases and quantitative accuracy metrics across various AI applications. The integration of AI technologies significantly improves traceability, reduces post-harvest losses, and enhances food safety in complex supply networks. Reported outcomes indicate high performance, with accuracy rates exceeding 80% in areas such as pathogen prediction, food recognition, microplastic detection, aquaculture optimization, and species classification. Specific applications show notable precision in microalgae classification (97.67–97.86%), seaweed identification (93.5%), and fish freshness assessment (up to 100%). Despite these advancements, the study acknowledges ongoing challenges related to data standardization, infrastructure, and regulatory frameworks. The findings highlight the need for interdisciplinary collaboration and continuous innovation. Ultimately, the strategic adoption of AI and smart tools is essential for building resilient, secure, and sustainable food systems and also offers significant indicators for future research. | ||
| کلیدواژهها | ||
| Artificial intelligence؛ Smart tools؛ Intelligent manufacturing؛ Enhanced processing traceability | ||
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