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【2024 Application Example】 Using Plant Growth Chambers as an Example - Standardizing Electronic Device Procedures Based on Imaging

In recent years, global climate change and environmental issues have become increasingly severe, causing major impacts on agricultural production. Traditional agriculture heavily relies on weather conditions, facing challenges such as unstable crop quality, plummeting yields, and difficult pest control. Particularly in Taiwan, agricultural biotech companies and farmers have suffered continuous losses, creating an urgent need for innovative solutions. Meanwhile, Taiwan's plant factory industry faces many challenges: high equipment and labor costs, an incomplete industrial chain diminishing international competitiveness, and a lack of cooperation among enterprises, all of which limit industry development. Additionally, COVID-19the pandemic has highlighted the importance of remote monitoring and management. Traditional manual inspections and data collection methods no longer meet the needs of modern agricultural production. These issues collectively underline the urgent need for smart agricultural solutions, driving companies like Taiwan's HaiBoTe to develop innovative projects integrating IoT, cloud computing, and artificial intelligence technologies.

 

HaiBoTe Cloud Data Integration and Analysis Platform
HaiBoTe Cloud Data Integration and Analysis Platform

 

Facing these challenges, the agricultural sector urgently needs a system that can precisely control growth environments, improve resource efficiency, enable remote monitoring, and facilitate intelligent management. Existing plant factory equipment often requires complete replacement, with poor compatibility with older equipment, and sensors and camera systems may require different interfaces, making them inconvenient to use. Therefore, there is a need for a flexible solution that can integrate various equipment and technologies, providing real-time monitoring and data analysis, and automatically adjusting environmental parameters based on plant growth conditions. This demand exists not only in Taiwan but is also a global trend in the development of smart agriculture. By incorporating artificial intelligence, more scientific evaluation standards can be established, optimizing production processes, improving yield and quality, while reducing energy consumption and environmental impact. Additionally, such smart solutions can attract more young people to participate in agricultural production, promoting industry upgrading and sustainable development. Overall, the demand for smart agricultural solutions stems from the urgent requirements to address climate change, enhance production efficiency, reduce costs, and achieve precise management, and this is exactly the problem companies like Taiwan's HaiBoTe are striving to solve.

 

 

Taiwan's plant factory operators are facing a series of severe challenges, which are gradually eroding their competitiveness and survival space. Firstly, the high cost of equipment and operations is their biggest burden. Each electricity bill feels like a heavy blow, forcing them to balance between ensuring product quality and controlling costs. Secondly, the unpredictability brought by climate change has become their nightmare. Sudden extreme weather events can destroy their carefully nurtured crops in a short time, causing massive economic losses. What's worse, they find themselves increasingly at a disadvantage in international market competition. In contrast, large overseas plant factories, with their advanced automation technology and well-organized supply chains, can produce stable-quality agricultural products at lower costs, putting unprecedented pressure on Taiwan's operators.

On the technical level, they also face numerous challenges. Compatibility issues between new and old equipment often put them in a bind, encountering various technical obstacles when trying to integrate different systems. Lack of precise data analysis and forecasting capabilities also makes it difficult for them to make production decisions and accurately determine the best growth conditions for each crop. Existing monitoring systems provide data that is often disorganized, difficult to interpret and apply. Human resource challenges are also severe, with young people generally lacking interest in agricultural work, making it difficult for them to recruit employees with modern agricultural skills. Even existing employees often feel exhausted from tedious manual operations and monitoring tasks. These problems are intertwined, creating a complex dilemma that leaves plant factory operators confused and anxious. They urgently need a comprehensive solution that can enhance factory operational efficiency, reduce costs, and improve product competitiveness, helping them overcome difficulties and regain their footing in the fierce market competition.

 

 
 

 

In facing the various challenges of plant factory operators, Taiwan's HaiBoTe company has demonstrated exceptional technical innovation and a flexible customer-oriented development strategy. They deeply understand that the solution must be able to seamlessly integrate existing equipment while providing highly intelligent management functions. To this end, HaiBoTe's R&D team adopted a modular design approach to develop a system that can be flexibly configuredIoT(IoT) system. The core of this system is a smart control hub that can communicate with various sensors and actuators. During development, HaiBoTe worked closely with customers, deeply understanding their specific needs and operational environments. They even dispatched engineers onsite to observe the daily operations of the plant factories, ensuring that the developed system actually solves practical problems. This in-depth cooperation not only helped HaiBoTe optimize their product design but also established a close relationship with customers, laying the foundation for subsequent continuous improvements.

HaiBoTe's innovation is not just reflected in hardware design but also in their developed intelligent software system. This system integrates advanced machine learning algorithms, capable of precise forecasts and optimal control of plant growth conditions based on large amounts of historical data and real-time monitoring information. To help customers overcome technical barriers, HaiBoTe designed an intuitive and easy-to-use user interface, which even non-technical operators can master easily. Additionally, they provide comprehensive training and tech support services, ensuring customers can fully utilize all functions of the system. When facing challenges, HaiBoTe's technical team can quickly identify problems through remote diagnostics and provide solutions. In one incident, during a serious equipment failure emergency faced by a customer, HaiBoTe's engineers guided the customer through system remote access, successfully instructing them on repairs and avoiding potential massive losses. This full-range service not only solves customers' immediate difficulties but also strengthens their confidence in intelligent management, driving the entire industry toward more efficient and sustainable development.

 

HaiBoTe's developed smart agriculture solution not only brought revolutionary changes to plant factories but also painted an encouraging picture for the future of the entire agricultural industry. The excellence of this system is evident in several aspects: firstly, it achieves precise control of the plant growth environment, significantly improving crop yield and quality stability. Through advanced artificial intelligence algorithms, the system can forecast and adjust optimum growth conditions based on historical data and real-time monitoring information, ensuring each plant grows in the ideal environment. Secondly, it significantly reduces energy consumption and operational costs, improving resource efficiency. The intelligent management system optimizes water, electricity, and nutrient supply, reducing waste and lowering manpower costs. Additionally, the system's modular design and strong compatibility allow it to seamlessly integrate various new and old equipment, providing a flexible solution for gradual upgrades of plant factories. Most importantly, the system injects a sense of technology and modernity into agricultural production, helping to attract the younger generation to the field and injecting new vitality into the industry.

Looking ahead, HaiBoTe's smart agriculture system has broad application prospects and expansion potential. In addition to plant factories, this system can also be applied to traditional greenhouse cultivation, urban agriculture, and even home gardening. In the field of aquaculture, similar technology can be used to monitor and optimize the breeding environments for fish or shrimp. In the food processing industry, similar intelligent monitoring and forecasting systems can be used to optimize production processes and enhance food safety. Even in the pharmaceutical industry, this type of precise environmental management system could be applied to drug research and production processes. To further promote this system, HaiBoTe could adopt a multifaceted strategy. Firstly, they could collaborate with agricultural colleges and research institutions to establish demonstration bases, allowing more people to experience the benefits of smart agriculture firsthand. Secondly, they could develop customized solutions tailored to different scales and types of agricultural production, expanding the applicability of their products. Furthermore, they could raise awareness and acceptance of smart agriculture within the industry by hosting forums, online seminars, and sharing success stories. Lastly, they could explore collaborations with government departments to integrate this system into policies supporting the modernization and sustainable development of agriculture, thereby promoting the widespread adoption of smart agriculture on a larger scale. Through these efforts, HaiBoTe not only can expand its market share but also make a significant contribution to the sustainable development of global agriculture, truly realizing the vision of technology empowering agriculture.

 

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-12-09」

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【導入案例】維繫遊艇王國美譽 嘉信遊艇導入國內第一套FRP複材超音波智慧檢測
Maintaining the reputation of the “Kingdom of Yachts” - Kha Shing Enterprise introduces the first domestic FRP ultrasonic smart inspection of composite materials

The Kaohsiung-based Kha Shing Enterprise Co, Ltd was established over 40 years ago, and is Taiwan's largest customized yacht company with customers all over America, Europe, Asia, and Australia, earning Taiwan the reputation of the "Kingdom of Yachts" Current FRP hull inspection still relies on traditional methods, such as visual inspection and knocking sounds, which is time-consuming and labor-intensive Kha Shing has applied PAUT array ultrasonic inspection to hull FRP composite materials for the first time, and combined it with AI to interpret ultrasound images, develop complete intelligent solutions, and create emerging markets for inspection companies Kha Shing Enterprise Co, Ltd was formerly Kha Shing Wood Industry Co, Ltd, and was a factory specializing in wood import in Kaohsiung Linhai Industrial Park when it was first established It began to design, manufacture, and sell yachts in 1977 After the second-generation successor of the company, President Kung Chun-Hao entered the company, he made a breakthrough in the previous manufacturing model that relied mainly on the skills of master craftsmen, introduced digital manufacturing to accelerate shipbuilding, and began to make larger yachts, ranking in the top 20 manufacturers worldwide among manufacturers of large yachts over 24 feet It also set a record of delivering 94 yachts within one year, earning Taiwan the reputation of "Kingdom of Yachts" Defect detection ensures yacht quality, using AI to replace humans to achieve higher efficiency Defect detection is very important to ensuring yacht quality At present, the yacht industry still uses very traditional defect detection methods The hull structure is usually made by hand lay-up or the vacuum infusion process, using visual inspection or knocking and the frequency of the sound to determine defects It requires time-consuming manual inspection If there are any defects, they must be reworked and repaired, and a gel coat subsequently sprayed The hull must be constructed in sections to facilitate inspection For large yachts over 24 meters long, construction in sections is very time-consuming and labor-intensive To shorten the time of the yacht manufacturing process, Kha Shing Enterprise will first carry out the gel coating process for the hull, and then perform the hand lay-on process The hull manufacturing process has two types of composite material test specimen structures In terms of 54-foot yacht hulls, the hull contains gel coat, core material, fiber and resin, and the total thickness is about 32cmplusmn01cm, which is twice the total thickness of FRP hull without core material of about 16cmplusmn01cm Defects such as incomplete impregnation of glass fiber or residual air bubbles between glass fiber and resin occasionally occur during the manufacturing process The types of defects include insufficient resin, voids, and delamination Once defects occur, the supply of hull materials will be insufficient and yacht delivery will be delayed Schematic diagram of types of FRP hull In order to solve this problem, Kha Shing Enterprise has engaged in technical cooperated with the metal materials industry and the AI technology industry, combining the ultrasonic inspection expertise of the metal materials industry with AI technologies developed by the AI technology industry in recent years to help solve issues of Kha Shing Enterprise with defect detection The method uses PAUT on the composite material structure of yachts, conducts FRP ultrasonic evaluation to determine the thickness of the yacht hull and material properties, and evaluates the ultrasonic probe frequency applicable to the hull structure based on professional ultrasonic experience After testing, a frequency of 5MHz and a probe width of 45mm can successfully find the location and size of defects in the simulated defect test specimen The three parties jointly found defect detection solutions from array ultrasonic evaluation, AI technology model development, and 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engineers 3 High-efficiency process inspection provides defect repair recommendations, reduces damage rate, and improves the strength and quality of composite materials The application of AI technology can optimize the yacht manufacturing process, reduce manual inspection, create added value through the application of AI in Taiwanrsquos yacht industry, increase international purchase orders, and allow Taiwan yachts to continue to enjoy a good reputation in the world Furthermore, this business model has also spread to fields of application related to composite materials, increasing cross-sector market usage It is estimated to contribute approximately NT14 to NT2 billion in economic benefits to Taiwan's equipment maintenance and non-destructive testing market

這是一張圖片。 This is a picture.
[2023 Case Study] AI Steps into Philanthropy: Stylish Tech at Food Banks

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and donors After receiving donations, these resources are then allocated to needy families or individuals There is a potential issue of uneven distribution of resources due to a lack of digitalization and integrated information management in these processes Warehouse and Transportation Centers and Mini Food Banks Distributing Resources to the Disadvantaged The location under validation by the Kaohsiung Charitable Organizations Association,Hereinafter referred to as 'Kaohsiung Charity' In109year6month24Officially inaugurated Taiwan's first 'Food Bank-Warehouse and Transportation Center' at a location measuring200square meters, enhancing the efficiency of food resource redistribution, proper storage, and management So far, nearly two hundred tons of vegetables and fruits have been saved, serving over a hundred organizations and benefiting over5thousand vulnerable households, and continues to serve19mini food banks, with planned completion across multiple districts in Kaohsiung, 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reinforcement learning constructs the management mechanism for the food bank's warehouse, perfecting the classification of donated goodsRNNTechnical construction of classification models further use of reinforcement learning constructs food bank warehouse management mechanisms, making the classification of donated goods perfectlike white rice, instant drinks, noodles, instant noodles, and canned goodscan then be automatically assigned storage based on storage assignment principles AI Service System Process and Description Source Taiwan Food Bank Association AtAIUnder forecasts, it can optimize the speed of resource transfer and allocation, effectively and accurately match resource donations reducing the loss in the donation process, increase the accuracy of resource distribution, and improve the service rate—the successful donation rate—reducing the waste of resources due to incorrect items, and enabling instant monitoring of food resource stock, ensuring operators can respond quickly 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【導入案例】巨量遙測空間數據AI分析雲端服務平台 使衛星遙測影像順利落地應用
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Although satellite remote sensing images can make all surface objects visible, it still requires a lot of time and manpower to be truly applied to the industry In order to effectively solve the problems that customers face in digesting huge amounts of image data and eliminate technical obstacles for cross-domain users to process satellite remote sensing images, ThinkTron has developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" as a new beginning for cross-domain AI applications for spatial information In recent years, in response to the impact of industrial globalization, Taiwan's agriculture has continued to transition towards technology-based and higher quality, improving the yield and quality of crops by solving problems, such as microclimate impacts and pest and disease control The demand of agriculture on images has expanded endlessly to accurately grasp the growing environment of crops In the early years when UAVs unmanned aerial vehicles were not yet 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long been an international trend AI Analysis Cloud Service Architecture for Remote Sensing Big Data