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【2021 Application Example】 Fongyu Uses AI Knowledge-based Fish Farming to Effectively Increase Aquatic Production by 10%

Fisheries is an important industry in an island economy. However, the fish farming industry has faced severe challenges in recent years, including climate change, labor shortage, and rising costs. In particular, nearly 110,000 workers in agriculture will retire due to old age over the next 10 years. For this reason, the need for aquaculture to move towards smart farming is becoming increasingly urgent.

Founded in 2014, Fongyu Corp. Ltd. has developed a unique eco-friendly farming model based on its own fish farming. It uses AI knowledge-based fish farming to effectively increase aquatic product production by 10%, and reduced labor cost by 15%.

The word "Fongyu" has a profound meaning. "Fong" represents good mountains and "Yu" represents good water, and is the hope that companies will allow Taiwan to always have good mountains and good water.It is also a homophone for "having a full figure," expressing the hope that products will give consumers a full and healthy body and mind. The founder of the company, Liu Chien-Shen, has been through the difficult entrepreneurial journey of becoming an apprentice in fish farming, raising funds, renting fish farms, establishing a fish farming company, building a brand, and expanding sales.

Labor shortage and aging workers are hidden worries in the fish farming industry

Currently, fish farms in Taiwan are still mainly traditional fish farms, and farming techniques are still passed down through word-of-mouth. In addition, the labor shortage and average age of workers exceeding 60 years old has made it impossible to effectively stably improve productivity and yield. This farming method makes it difficult to prevent and control diseases, and greatly increases the possibility of excessive use of drugs, environmental pollution, and water quality and ecological damage, creating a vicious cycle that lowers the quality of fish farming.

In addition, 65.1% of workers in Taiwan's fish farming industry are inadequately skilled. With limited support from IoT sensors, traditional fish farmers still mainly rely on their own experience and knowledge for water quality management, feeding, and disease detection. Fish farming management relies heavily on the ability of individual fishermen. Once experienced workers retire, the industry will not only face the issue of succession, but also the difficult of stably supplying a certain amount of harvest that meets quality standards. This may cause a dilemma for the entire industry from fish farming to sales.

In order to improve the pain point of inability to pass on experience in fish farming, and at the same time create a "digital" foundation for fish farming, the top priority must be to collect farming behavior data and develop AI services as an important starting point.

Fishery digital twin technology helps fishermen transition to smart farming

With the assistance of the Institute for Information Technology (III), Fongyu implemented the "fishery digital twin" technology to dynamically adjust the farming schedule. In other words, the fish farming schedule is adjusted according to the species, habits, and variables of the fish. The use of AI in fish farming not only effectively increase aquatic production by 10%, but also reduced labor cost by 15%.

In terms of specific methods, we first digitalized the fish ponds, feed, and decision-making behavior for each species, such as sea bass and Taiwan tilapia, and recorded the seasonal temperature changes from releasing seedlings to harvesting, all of which were digitalized, gradually recording the experience and methods of experienced workers into a rich database. Based on the recorded data, we analyzed the compound variables to find the best farming behavior and generate a dynamic farming schedule.

Recording each pond's farming master's data experience.

▲ The records for each pool provide data on workers' experience.

However, fish farming behavior generally relies on rules of thumb. Even experienced fish farmers cannot ensure that they will find the best solution. Therefore, new methods are proposed to solve this issue: That is, "to determine the best fish farming behavior by predicting the interaction with water quality and past data on feeding, and evaluating fish farming behavior based on water quality and fish farming," and provide fishermen with the most intuitive recommendations through daily schedules. To continue optimizing the dynamic fish farming calendar on a rolling basis, iterations of the model will be developed through the three-step cycle:

(1) Input the current fish farming calendar into the model;

(2) The model predicts the future environment;

(3) Shortcomings of the fish farming calendar are corrected based on the future environment to obtain a new version of the fish farming calendar.

In the process, the experience of aquaculture experts is used to establish the causal relationship between fish farming behavior and the environment. The establishment of a dynamic fish farming process and technology-based fish farming recommendation services provide a traceable and detailed fish farming process. It is one of the few technologies that can digitalize fish farming. Fishermen can quickly and easily record their daily behaviors to build knowledge without taking up too much time, but in the long run it can reduce labor cost by 15% and increase output and revenue by an average of 10%.

Smart fish farming has achieved outstanding results, reducing labor cost by 15% and increasing output by 10%

At the same time, the fish farming calendar can also be extended to different aquatic species, such as white shrimp, milkfish, clams, and Taiwan tilapia, to produce fish farming schedules for ponds with different specifications, and the harvested aquatic species can be traced according to different specifications, establishing vertically integrated services for safe food products. Fongyu's main products are divided into two categories. One is aquaculture modules, including fry, feed, materials and probiotics, production planning and processes, and monitoring, which can be sold separately or exported as modules.

Summit Fisheries' high-quality marine products consistently win awards. (Image: Summit Fisheries official website)

▲The high-quality aquatic products produced by Fongyu have repeatedly won awards. (Figure: Fongyu’s official website)

The other category is high-quality aquatic products, including seabass fillets, seabass balls, oil-free seabass balls, seabass dumplings, and seabass soup. The products have won various awards, including the top ten souvenirs in Pingtung in 2017, "Barramundi Fillet" won the 2017 Eatender of the Council of Agriculture (COA), "Oil-Free Barramundi Fillet" won the 2018 Eatender Gold Food Award of the COA, and "Dumplings of Barramundi" and "Barramundi Broth" won the 2019 Eatender of the COA. The consecutive awards represent that the "quality" of Fongyu’s aquatic products can be seen and eaten with peace of mind.

In addition, Fongyu has exclusive fingerlings that meet international needs, such as: Pure seawater cultured tilapia fingerlings and seawater Taiwan tilapia fingerlings from selective breeding (FY-01) are items that aquaculture companies in many countries are looking forward to. The company also has aquaculture modules, disease monitoring tools, and feeding materials designed in accordance with the environment, in order to provide customers with more stable income.

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這是一張圖片。 This is a picture.
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【導入案例】赫銳特科技VCSEL封裝元件瑕疵導入AOI檢測 提升產能效率20
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Preventing Problems Before They Arise: Leadtek Research Develops AI Technology for Early Detection of Heart Failure Symptoms

With the increase in the elderly population, the incidence of various chronic diseases is rising daily Among these, heart failure is not only a silent killer it has a very long disease course with a high recurrence rate, leading to increased burden on healthcare personnel However, by using medically certified electrocardiography acoustics devices, coupled with AI predictive assessment of heart failure risk and remote care systems, diagnosis can be aided significantly, helping doctors make accurate diagnoses for subsequent patient medical care or referrals Heart failure has a lengthy course and medical expenditure is five times that of diabetes If you find yourself short of breath even with minimal movement, or if you wake up from sleep needing to sit up to feel comfortable, or if you have symptoms such as swollen lower limbs, anxiety, restlessness, fatigue, or a loss of appetite, be cautious These could be signs of heart failure According to statistics, there are about 60 million people with heart failure worldwide, with 5 million new cases every year In China, nearly 290 million people suffer from cardiovascular diseases, accounting for the second leading cause of death among urban residents around 12 million of these are heart failure patients, accounting for over 59 of cardiac-related deaths The disease course of heart failure is exceptionally long, and both its recurrence and rehospitalization rates are exceedingly high, resulting in medical costs that are twice that of hypertension and five times those of diabetes According to US research statistics, the 30-day mortality rates for patients with myocardial infarction and heart failure are respectively 166 and 111, and the rehospitalization rates within 30 days are 199 and 244 The symptoms of heart failure, because they are similar to those of other diseases such as chronic obstructive pulmonary disease and asthma, have an 185 misdiagnosis rate, which poses a challenging problem for healthcare institutions 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based on the electrocardiogram and phonocardiogram data 3 Prediction of heart failure recurrence risk For patients with heart failure, it reads their phonocardiogram and electrocardiogram data, assessing the risk prediction of heart failure recurrence Leadtek states that the application of heart failure AI recognition technology can assist doctors in making more efficient and accurate diagnoses, facilitating subsequent medical treatment or referrals for patients As an instance, in studies of heart failure patients discharged from Taipei Veterans General Hospital, using the EMAT Electromechanical Activation Time index and SDI Systolic Dysfunction Index calculated by the synchronized electrocardiography-acoustic device as treatment guidelines resulted in a higher survival rate compared to those treated based on traditional symptoms This research has also been published in the authoritative international cardiology journal JACC, receiving recognition in the international market System manufacturers can apply heart failure AI recognition technology for other value-added applications Leadtek states that cooperating system manufacturers can choose to build their own heart failure AI risk prediction engine, uploading their system's electrocardiogram and phonocardiogram data to Leadtek's heart failure AI risk prediction engine, which then returns risk prediction values for integration by system manufacturers cooperating manufacturers as a value-added application input Not just for clinical use, the heart failure AI risk prediction engine can also be extended for use at home or in the workplace Additionally, this system can be extended to other applications, including One, hospital outpatient screening Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to conduct a 10-second rapid test in outpatient and emergency departments to assess a patient's cardiac history and heart failure risk Two, discharge risk assessment Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to assess the heart failure risk during a patient's hospital stay The test data can serve as a pre-discharge risk assessment and prognostic indicator Three, continuous home care Patients can use the electrocardiogram and phonocardiogram recorder, wearable electrocardiogram recorder, and transmit through a home transmission box gateway to measure electrocardiogram and phonocardiogram signals at home and upload them to the amor health cloud platform for heart failure AI risk prediction analysis Patients can manage their health autonomously via an APP, reviewing historical physiological trends disease management nurses can manage member health through the health management backend Web Four, home rehabilitation training Patients can wear a health bracelet to monitor activity, fatigue, circulation, and sleep, autonomously managing their health through the mobile APP and observing the risk of heart failure, engaging in exercise and rehabilitation training to aid in swift recovery The heart failure AI recognition technology system can also be extended to employee home care applications Additionally, in factories or offices, this system can also achieve employee health management goals, with applications including One, workplace safety units Provide employees with wearable electrocardiogram recorders before they start work duties Two, physiological monitoring for business executors While executing business duties or training, employees wear wearable electrocardiogram recorders for fatigue warnings, signaling whether physiological conditions allow continued execution of tasks Task segments can use data transmission boxes or apps to upload physiological monitoring information to the health management platform, assessing the heart failure risk for operations staff, with test data serving as an indicator for enterprise resource human units and public safety Three, workplace physiological monitoring center care The workplace physiological monitoring center can inspect and record employees' historicalphysiological trends through the health cloud platform Four, workplace nursing units Nursing units receiving instructions from the physiological monitoring center can provide health management advice based on employees' physiological trends nursing centers can manage employee health through the health management backend Web Five, employees can wear health bracelets to monitor activity, fatigue, circulation, and sleep, autonomously managing their health and observing the risk of heart failure through the mobile APP, engaging in exercise and rehabilitation training to aid in rapid recovery Workplace application of heart failure cloud care and big data center diagram「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」