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【2020 Application Example】 "Intelligent Agriculture and Big Data Application Platform" effectively reduces fertilizer use by 50%!

編輯群2020-07-01

Life is tough for lettuce village farmers who are at the mercy of the elements

Global market trends are volatile. In terms of agriculture, it must compete with cities for land and water, and also face other crises, such as: mitigating climate change and protecting natural habitats. In particular, climate change makes it more difficult for farmers to plan cultivation in traditional ways. For organic lettuce exporters, they have to overcome problems such as climate and pests to ensure that the lettuce can meet the standards of overseas customers. This is an issue that the industry is facing...

Difficulties and needs of Taiwan Lettuce Village

Although Taiwan Lettuce Village currently uses an internationally certified standard process (G.GAP) for cultivation, and also uses the "Intelligent Agriculture Management System" developed by Info-Link Services for cultivation management, it still faces the dilemma of not being able to control crop yields and quality due to climate abnormalities.

Efforts to solve pests and production problems in recent years not only consumes labor, but also doubled the use of pesticides. However, using cultivation and production models of the past will cause the industry to stagnate or even face elimination.

Therefore, it hopes to add value through AI, and make the lettuce village can be more information-based, intelligent, analytical, and predictive in cultivation, so as to expand the industry’s exports and diversify the industry's development in the future.

Current Demand of the Lettuce Village

The agriculture industry in Taiwan Lettuce Village currently only implements "information management." Even though it has the concept of data application, there are no implementation methods and direction, and fields are still manually inspected and the dosage of pesticides is determined based on experience. Since crop production varies due to environmental factors each season, the accumulated temperature conditions required for lettuce growth can be estimated by comparing crop yield and harvest date based on historical meteorological data, thereby establishing an accumulated temperature calculation module to estimate the cultivation schedule, allows the system to automatically analyze and make prediction based on the current temperature and humidity of the overall environment. During the crop harvesting period, it assists field personnel in optimizing their work, reducing the need for daily inspections to determine when to schedule the next task.

Agricultural information system + AI allows lettuce to grow smoothly

"The stability of the cultivation environment" plays an important role in the growth process of crops. Understanding the growing conditions of crops can greatly increase production and maintain a certain level of quality. Combined with "smart equipment in the field" and "linebot," field management can be carried out and warnings can be received at any time, allowing managers to respond quickly to reduce potential losses, and assist in disease prevention, growth period, and harvest prediction. It can be further integrated with data from the Central Weather Administration to establish a "cultivation database," and conduct agricultural analysis through data collection, such as: fertilizer dosage planning, analysis of lettuce growth days in different months, analysis of the quality and weight of lettuce output based on temperature, and even disease prevention predictions.

Comparison of differences before and after digitization

▲ Comparison of differences before and after digitization

Compiled the cultivation data collected by the Lettuce Village from field equipment and external data , such as: temperature, humidity, sunlight, and farmland fertility, and applied the data in four aspects, including:

1. establishing crop progress and growth obstacle information, analyzing the temperature ranges that are suitable for growth and hinder growth, importing open data (real-time and future weather forecast data) to establish forecast standards, and using weather sensing equipment for field monitoring, in order to achieve real-time warning notifications and preventive effects.

2. Utilize cultivation data for growth predictions, in order to achieve the goal of estimating harvest date.

3. Using mobile phones for weather monitoring achieves the goal of real-time control and adjustment of field operations, allowing Lettuce Village to effectively manage manpower, material costs, and crop quality.

4. Compiled farmland fertility data to provide the fertilizer ratios for suitable for farmland and reduce the frequency of fertilization, improving farmland fertility while improving the overall environment.

Description of Data Applications

▲ Description of Data Applications

The system will continue to be optimized and promoted it to more units

The "Intelligent Agriculture Big Data Application Platform" allows farmers in Lettuce Village to no longer be limited to the traditional agricultural business model, achieve systematic cultivation and production management, and standardize specifications to improve quality, stabilize output, and reduce labor consumption and material costs.

Improved pest and disease detection accuracy from 80% to 100%

In the future, we hope to increase the accuracy of pest and disease detection, so that farmers can monitor the status of crops in real time, making the system more complete. We also hope to apply this system model to more crops, and allow more farmers to consistently grow high-quality crops at low cost through government promotion.

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【導入案例】海量數位工程AOI機器智能手臂檢測系統 大幅提高瑕疵檢測精準度
Massive Digital Engineering AOI Intelligent Robotic Arm Inspection System Significantly Improves Defect Detection Accuracy

Taiwan is known as a manufacturing powerhouse, yet quality defect detection has always been a chronic sore point in production lines While AOI equipment is available to assist, most use fixed machinery which are limited by angles, resulting in less precise diagnostics and high false positive rates Massive Digital Engineering introduced an AOI intelligent robotic arm detection system that effectively reduces false positives and increases the accuracy of defect detection Generally, the yield rate of products affects the costs for enterprises and the return rate for customers The quality defect detection process in the manufacturing industry often necessitates a substantial amount of quality inspection labor Although there is AOI equipment to assist, these tools are mostly fixed detection machines Fixed cameras are easily limited by angles, resulting in less precise diagnostics and high false positive rates Thus, personnel need to re-screen and inspect afterwards, often manually visual inspection misses defects on average about 5, and can be as high as 20 Three major pain points in manufacturing quality detection Robotic Arm AOI with dynamic multi-angle inspection helps to solve these issues According to the practical understanding by Massive Digital Engineering, there are three main pain points in detecting product quality within the manufacturing industry Pain point one, manual inspection of product quality is prone to errors Currently, the manufacturing industry largely relies on human labor to inspect product appearance, but human judgment often entails errors, such as surface scratches, color differences, solder appearance, etc The error rate in defect judgment is high, and can only be inspected at the finished product stage, often leading to whole batch rejections and high costs in labor and production Pain point two, inability to quantify and record data from quality inspections Traditional manual inspections do not maintain inspection data, which makes it difficult to assign responsibility when quality disputes occur Moreover, high-end contract manufacturing orders from overseas brands often require traceability and corresponding defect records, which traditional human inspection methods struggle to meet Pain point three, limitations of traditional AOI visual inspection systems Current manufacturing uses AOI visual inspection systems, which due to the limitations of visual software technology, employ fixed cameras, fixed lighting, and single-angle operations This method may handle flat or linear-shaped products like rectangular or square items at a single inspection point However, it is more challenging to implement for products with complex shapes eg, irregular automotive parts, requiring multi-point and multi-degree inspections Massive Digital Engineering developed an AOI intelligent robotic arm detection system, effectively improving the accuracy of defect detection To address the pain points in quality inspection in manufacturing, Massive Digital Engineering initiated the concept of developing a multi-angle, movable inspection device, starting with the combination of two representative technologies in factory automation - robotic arms and machine vision By integrating robotic arms with AOI for dynamic multi-angle AI real-time quality inspection, the limitations of fixed inspection systems are addressed, and visual inspection techniques are enhanced by leveraging artificial intelligence, further elevating the sampling of images from flat to multi-dimensional and multi-angular Selected the automotive industry as the real-world testing ground to quickly respond to customer needs The AOI intelligent robotic arm detection system, utilizing AI technology including unsupervised learning, supervised learning, and semi-supervised learning, allows operators to use unsupervised deep learning techniques to learn about good products even when initial samples are incomplete or there are no defective samples, applying it in the visual inspection of automatic welding of car trusses This can solve issues of limited angles with fixed machinery before implementation, less precise diagnostics, and high false positive rates Automotive components are high in unit price and demand a stricter defect detection accuracy In industries that have adopted AI services, the automotive manufacturing sector was chosen as the real-world testing ground Massive Digital Engineering states that the automotive industry mainly consists of related component manufacturers and components typically have a higher unit price, hence requiring more in terms of quality inspection and yield rates, and demanding stricter accuracy Therefore, the automotive sector was chosen as the area for introduction By using a robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection, not only can the defect quality error rate of automotive components be improved, but the fixed-point AOI optical inspection can be enhanced to meet the measurement needs of most industries and finally, establishing a third-party system platform to build an integrated monitoring system platform, enabling immediate response and action when issues arise This system allows for recording and storing important data of products leaving the factory, serving as a basis for future digital production lines and virtual production At the same time, in the event of defects, it can immediately connect to Massive's MES monitoring system, quickly responding to the relevant manufacturing decision-making department, subsequently utilizing ERP systems for project management and reviews, effectively improving production efficiency and reducing production costs Helps to reduce communication costs and aims to become an industry standard In terms of industry integration, it provides a foundational standard for data continuity among upstream and downstream businesses, reducing communication costs within the supply chain Through certification of the contract manufacturers and brand owners, there is a chance to become the industry standard configuration Through the data database established by this project, operators can further optimize their supply chain management solutions using big data analysis Data Analysis, based on data, establish forecast planning, and utilizing technology to link upstream and downstream data of the supply chain, accurately controlling product quality In the future, when interfacing with European, American, and Japanese markets, which demand highly fine-tuned orders, operators can respond and integrate the industry supply chain Supply Chain more swiftly Ultimately, through the benchmark demonstration industry's field verification, such as with the automotive component manufacturing industry used as the benchmark demonstration field, by implementing the robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection system project, the supply chain connection between automotive contract manufacturers and OEMs can be optimized, becoming the industry standard Further seeking more AI teams to join the cross-industry development on the field collaboration platform, driving the overall ecosystem combining AI innovation with field application Self-driving vehicle developed by Massive Digital Engineering「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【解決方案】搭上綠能商機 華鉬實業打造全釩液流電池儲能系統設備 長效儲能的最佳選擇
Taking advantage of green energy business opportunities, Hua Molybdenum Industry creates all-vanadium redox flow battery energy storage system equipment, the best choice for long-term energy storage

Green energy is the future trend and will surely lead to huge business opportunities in the future Wind power has been one of the green energy sources that have attracted global attention in recent years It will become an important force in my country's renewable energy and help Taiwan's power generation reach the goal of 20 by 2025 to improve Taiwan's energy independence As the number and power of domestic wind turbines wind turbines increases year by year, it is particularly important to ensure that the power storage equipment achieves safe, long-term performance, is not easily attenuated during charging and discharging, and is sustainable, low-carbon and environmentally friendly At the same time, the wind turbine equipment itself Health inspection, maintenance and repair have also become the focus of wind farm operators In order to meet the needs of wind farm customers, the green energy business unit of Hua Mo Industry has launched long-lasting energy storage all-vanadium redox flow battery electrolyte and wind turbine AI predictive operation and maintenance, providing 100 safety, long-term efficiency and reducing customer initial manufacturing costs cost-effective power energy storage equipment, and through AI predictive operation and maintenance services to help customers reduce power generation costs by 10 and save up to 30 in maintenance and warranty costs Hua Molybdenum Industry was established in 1998 The industry started by refining vanadium, molybdenum and rare metal elements and other products, and used them in high-end steel, professional chemicals and specialty chemicals industries, and vanadium is more like a steel-making Vitamins can increase the effectiveness of steelmaking Among them, vanadium and molybdenum related products are one of the company's main projects The company sees that the all-vanadium redox flow battery, which is 100 vanadium-based, will be a very promising mainstream green energy technology in terms of long-term energy storage in the future, and before 2010 The government has actively invited legal entities such as the Industrial Research Institute to conduct research on related component materials in solid-state batteries and all-vanadium batteries In addition, the Ministry of Economic Affairs expects renewable energy to account for 20 of power generation in 2025 and reach 15GW Based on the above Considering this, Hua Molybdenum Industry decided to devote all its efforts to research and invest in the technological development of self-developed all-vanadium redox flow battery electrolyte in 2017, in order to accelerate the compliance rate of renewable energy in 2025 Hua Molybdenum pointed out that "renewable energy power is relatively unstable, and Taiwan itself lacks lithium resources In lithium battery manufacturing, almost 80-90 of battery cells must rely on foreign procurement, and there is a lack of 100 domestic self-sufficient energy storage Resources and technology "Similarly, how does Taiwan overcome the problem of having no natural vanadium resources To this end, Hua Molybdenum Industry uses original technology to use waste catalysts from petrochemical industries such as CNPC refineries or Taishuo petrochemical processes Up to 10 of the vanadium ion content can be used to extract high-value vanadium resources, thereby producing Taiwan's 100 self-made all-vanadium redox flow battery electrolyte without being affected by resources, effectively achieving resource recycling Since 2017, Hua Molybdenum Industrial has successfully created all-vanadium flow electrolyte technology, and has successfully passed product verification by the Industrial Research Institute, the Nuclear Research Institute and many international manufacturers Taiwan’s power storage energy target is to reach 15GW in 2025 Its power distribution includes 500MW in Taipower’s automatic frequency regulation system, 500MW in E-dReg and 500MW in existing or newly built solar power plants For example, electricity consumption is mainly between 4 pm and 10 pm, which is the peak period for people's daily electricity consumption For this reason, the Energy Administration specifically requires Taipower to strengthen the upgrade of energy storage equipment, which has also driven the market's interest in all-vanadium redox flow batteries Energy storage system equipment is in high demand In addition, Taiwan's current total power reserve construction and contribution has not yet reached 100MW, and the gap from the 2025 target of 15GW of power storage is still more than 15 times Using all-vanadium redox flow batteries to successfully create 100 safe, low-carbon, environmentally friendly and long-lasting energy storage system equipment Compared with the short-term power storage of lithium batteries, the biggest advantage of all-vanadium redox flow batteries is that it is globally recognized as a long-term power reserve It can store energy for a long time up to 12 hours, which means that if it is charged for 12 hours, It can release power for 12 hours Compared with the electricity measurement method of general energy storage systems, which is daily electricity consumption power in kilowatts x time in hours, for all-vanadium redox flow batteries, power and hours are different Special design, the power is also called a stack, which is composed of four materials metal, polymer mold, carbon felt and graphite plate, and the power consumption time is calculated based on the amount of electrolyte in cubes Therefore, when the power electric push x the amount of electrolyte the daily electricity consumption of our all-vanadium redox flow battery for energy storage The product features of the all-vanadium redox flow battery energy storage system equipment include four major features safety, long-term performance, not easy to decay during charging and discharging, and sustainable, low-carbon and environmentally friendly The quality of the all-vanadium flow battery is 100 safe Since the electric energy is stored in the vanadium-containing electrolyte, it can avoid any flammable accidents caused by a fully charged energy storage system In terms of battery life, compared to the short battery life of lithium batteries, all-vanadium redox flow batteries can have a battery life of more than 20-25 years through changes in price Regarding the charge and discharge performance of energy storage, unlike lithium batteries which have a certain number of charge and discharge times 5000-600 times, there is no limit to the number of charge and discharge times of all-vanadium redox flow batteries Regarding zero carbon emissions, which is highly valued globally, unlike lithium batteries which have recycling issues, the electrolyte of the all-vanadium redox flow battery can be used permanently The material components of the stack are environmentally friendly and fully recyclable to create a truly sustainable and low-cost Carbon-friendly energy storage system Onshore wind turbine AI prediction smart operation and maintenance allows customers to reduce power generation costs by 10 and save maintenance and warranty costs by up to 30 Hua Molybdenum Industry not only improves the long-term power storage efficiency of renewable energy customers through all-vanadium redox flow battery energy storage system equipment and helps customers reduce initial purchase costs, but also uses AI smart operation and maintenance empirical calculations for offshore and onshore wind turbines Field demonstrations were drawn on Taipower's onshore wind farm, and we actively accumulated our own technical experience and energy in AI predictive operation and maintenance With the support of the AI HUB project of the Industrial Bureau of the Ministry of Economic Affairs, the cooperation site will focus on the Phase I wind farm of Taipower Corporation and provide smart operation data of wind turbines for more than 6 months for analysis The AI predictive operation and maintenance system for onshore wind turbines uses machine learning The main technology provider comes from ONYX Insight, a subsidiary of British Petroleum BP The company uses AI Hub analysis software technology to analyze the wind turbines faced by Taipower Pain point analysis, including power generation loss of road-based wind turbines and damage prediction of key components of land-based wind turbines such as gearboxes, pitch bearings under abnormal vibration three-dimensional vibration frequency or abnormal temperature, etc output Through this implementation, it can effectively help Taipower reduce power generation costs by 10, increase asset value by 12, and save up to 30 in maintenance and warranty costs In the past three years, ONYX Insight has successfully predicted and operated more than 20,000 offshore or onshore wind turbines around the world, accumulating extremely high AI model accuracy It is believed that the international partnership established with ONYX Insight will effectively guide and accelerate the green energy division of Hua Molybdenum Industry in its goal and layout to become an independent technology service provider for wind turbine AI predictive operation and maintenance Works with partner ONYX insight to provide customers with an AI predictive operation and maintenance system, including wind turbine power generation loss and damage prediction of key wind turbine components Building a solid foundation for domestic wind turbine operation and maintenance, using Taiwan as a base to expand to Southeast Asian wind farms The market output value of offshore wind turbine AI predictive operation and maintenance in Taiwan will exceed NT30 billion in the future, and the energy storage market has an output value of more than 100 billion US dollars globally In the future company vision, Hua Molybdenum Industrial hopes to become An independent technical service provider for vanadium flow battery electrolyte and wind turbine AI predictive operation and maintenance The long-term goal is to establish a local supply chain of vanadium flow battery electrolytes around the world by accumulating abundant technology and performance capital to supply industry needs nearby 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

這是一張圖片。 This is a picture.
CCTV Intelligent Video Search System

Search for a specific person, find someone with a suitcase entering the factory in Gao'an area Color features of the person and the object confirmed, person in blue and black top, suitcase in black color, throughCCTV the intelligent video search system, by setting object and color retrieval conditions, it can successfully locate three video clips containing the target subject This greatly aids operational staff in finding the target items, and through this system, search speed can far surpass manual effort6fold Pain Points The CSE-Kaohsiung Plant is densely equippedCCTVto monitor every corner of the plant area, but when an incidenthappens, it's impossible within a limited time throughCCTVvideo playback to find the incident, the implications and risks behind this are self-evident Many areas that are usually unmanned can easily become security blind spots Thus, how to monitor a vast plant area more intelligently and effectively is one of the crucial aspects of building a smart plant for the semiconductor industry The AES Plant in Kaohsiung covers a vast area, with many important sites requiring monitoring of personnel movements to ensure corporate secrets and employee safety 1 Automated production lines and warehouses In semiconductor enterprises’ automated production lines and warehouses, oftenAGV（Automated Guided VehicleAGVs automated guided vehicles travel at high speeds if plant personnel inadvertently enterAGVthe moving area and cannot issue a warning to the person, then the regrettable accidents that occur will be too late to reverse 2 Material and product storage areas Materials used in semiconductor-related processes are costly if areas storing materials or products are breached, there is a risk of loss of high-value materialsproducts 3 High-security areas Trade secrets relate to the core technological competitiveness of semiconductor-related enterprises if someone breaches the high-security areas, there is a risk of corporate secrets being leaked The safety of trade secrets has always been one of the most critical issues for semiconductor enterprises 4 Loading docks At AESLButthe dock area often has loading vehicles coming and going if someone intrudes into the dock area, there is a risk of vehicle collisions and accidents Additionally, goods awaiting shipment at the dock area could be stolen or potentially damaged from collisions, thus causing significant reputation and financial losses for the company, further leading to production and shipping inconvenience When an abnormal event occurs, how to quickly search for the relevant key footage from massive data Many important locations within the AES Kaohsiung Plant need to be equippedCCTVfor safety checks, butCCTVWith thousands to tens of thousands of cameras, manually searching through footage for an event requires laborious frame-by-frame review which is time-consuming and inefficient In light of advancements in computer vision, it's beneficial to utilizeAIto replace manual playback and searching Problem Scenario Object Detection The data source for object detection comprises two parts Open-source datasetsOIDv4and AES Kaohsiung PlantCCTVImage files For these files, search for usable data, specificallyOIDv4image files For these files, extract the defined nine major categories of objects for training data among them, two object categories, knives and gasoline barrels, were not found inOIDv4found usable data for knives and gasoline barrels, while the remaining seven categories of objects are available fromOIDv4useful training data found for the remaining seven categories of objects, all marked Regarding the Kaohsiung PlantCCTVimage files, select some frames Frame of the footage, and manually annotate the objects to be_detected for training and testing data Nine Major Objects Color Recognition The data source for color recognition is divided into two partsInternet image screenshots, and Kaohsiung PlantCCTVimage files Currently, no publicly available open-source datasets specifically for color recognition applications have been found, so images are collected from the web Search the web for images of the defined nine major object categories, save the images after separating the objects from the background, keeping only the object sections, and mark the images according to color Additionally, for the Kaohsiung PlantCCTVimage files, use the already-markedbounding boxextractCCTVimage files from variousFramesections of objects identified by color, and finally, visually identifiable images are marked according to color Each object category has its specific color definition, depending on the usual colors seen in these objects in real life Dynamic Ignore during Training FromOIDv4during the training of the object detection pilot model, since each image in this dataset is only marked for a single category, but the image may contain other desired detection categories unmarked For such cases, dynamic ignore techniques will be employed during training to avoid confusion Next, use the extracted training data from the Kaohsiung Plant toFine-Tuneenhance the detection rate of the object in specific designated areas Finally, select the model that computes the lowest loss value in the test set during the training process as the main object_detection model Dynamic Ignoring AIHelp You View CCTV The intelligent video search system primarily serves as an assistive system for searching surveillance footage, capable of speeding up the process of finding target events by setting search conditions for objects By simply defining the search conditions, you can quickly produce thumbnails of critical objects and playback for review, shortening the time required for manual case retrieval of the past The search time is quickly6doubled, allowing the front-end security unit to use this platform to strengthen the first line of risk management supervision and take timely preventive measures 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-12」

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