New Generation of Agriculture-- Technological Agriculture Started by Big Data

Yi-Hsuan Wu（China Productivity Center Smart Agriculture Promotion Department）

The R&D and application of diversified technologies have greatly changed our daily life and business. Various industries have undergone tremendous changes in their operation modes with the rapid development of new technologies. What about agriculture? We have witnessed many smart products appearing in our lives, from mobile phones, home appliances, smart buildings to even smart cities. What smart applications can we find in the domain of agriculture? Smart agriculture, like other smart industries, is meant to make operation easier. Smart agriculture can help farmers save labor and systematically record operational information and crop growth data, thereby increasing crop production, improving the quality, and creating a safe and efficient agricultural environment.

The application of technologies in agriculture is mainly rested on several technologies: smart robots, information and communication technology (ICT), Internet of Things (IoT), artificial intelligence (AI) and big data. Agricultural robot technology can save labor cost through its automatic operation, such as automatic harvesting and spraying of pesticides, thus helping farmers reduce heavy workload. ICT is introduced into agriculture for the collection of production information, such as temperature, humidity, sunlight, soil moisture, salt, carbon dioxide, weather, etc., to assist farmers in various aspects of agricultural management. The use of IoT in agricultural production, operation, management and services enables us to understand the market trends and consumer needs, and to further connect production, logistics and sales to achieve agricultural efficiency, safety, and lowered risk. Artificial intelligence helps the farmers to digitize their experience and gathers the experience of the masses to improve the effectiveness of artificial intelligence, so that the problem that individual experience cannot be passed on can be solved, and stable quality and quantity of crops can be expected.

Big data has been successfully used in breeding, soil analysis, fertilization, pesticide application, diseases and insect pests, market supply and demand, etc. Digital data can be analyzed to plan suitable farming schedules, adjust cultivation methods and control measures, and propose suitable crops and yield planning, in order to predict possible risks and to reduce losses and increase overall profits. As one of the fastest growing areas of smart agriculture, big data analysis has brought about many changes to the agricultural production process. As mentioned above, smart agriculture has been used in many ways to enhance agricultural competitiveness. There are many successful cases both domestically and overseas of big data integrated with new technologies and introduced into smart agriculture.

The United States Department of Agriculture (USDA) assisted in the development of the Sustainable Water and Innovative Irrigation Management (SWIIM) program. SWIIM provides water use data to assist farmers in managing and monitoring information of the transportation and consumption of water in their field, and other weather data like the amount of rainfall, so that users can make correct irrigation decisions. SWIIM collects data more than 60 times per hour for aggregation and presents detailed reports through the operation platform, to facilitate real-time decision-making, and to control water use through the real-time monitoring and early warning system. The system is of great help to areas with scarce water. In addition to saving water, it also saves farming costs and uses water resources for other areas to produce more benefit.

Big data analysis is also often used in the prediction of agricultural pests and diseases. A joint project was conducted by the University of Cambridge, the British Met Office, the Ethiopian Institute of Agricultural Research, the Ethiopian Agricultural Transformation Agency, and the International Maize and Wheat Improvement Center (CIMMYT) to develop a set of Open Data Kit (ODK), a wheat rust prediction system for Ethiopia. Team experts make predictions of the possible spread of wheat rust spores based on meteorological data through field surveys, mobile phone monitoring, and the spread scope of rust spores and the adaptability of the disease environment. Risk assessment reports are presented to government agencies and instant messages are sent to farmers for planning the use of limited medicines in areas with the needs. The Open Data Kit wheat rust prediction system effectively controls the spread of diseases. The large amount of data collected and analyzed makes this early warning technology adoptable in other areas and used in the control of other wind-borne diseases and pests.

Many agricultural enterprises in Taiwan now use big data to optimize their business. Qyo Biotechnology Corp. is a well-known mushroom producer in Taiwan. To enhance the competitiveness internationally, it makes huge investment introducing automated equipment and installing intelligent production modules, and uses big data to analyze the biological resources and production data of mushrooms to build a real-time monitoring and big data early warning system. The management receives visible data, trend analysis and special analysis for reference in the decision-making. The ideal of standardized, scientific and precision agricultural management is achieved. Through the integrated big data system, the operation status of the equipment in the plant is constantly monitored, and the management of Qyo Biotechnology Corp. can make quick decisions and judgment in case of abnormalities. This reduces the chance of human errors, improves the accuracy of productivity forecast, reduces inventory costs, and increases overall revenue.

Royal Base Co., Ltd. has digitized its cultivation and management of moth orchid (Phalaenopsis) with a set of intelligent production and sales system. R&D results are utilized in the greenhouse and the contract supply chain management, to gradually accumulate big data for subsequent analysis. Smart devices like mobile phones or tablets are used in greenhouses for cultivation management, with the integrated QR code system of the batch numbers, varieties, specifications, and traceability information of the seedlings so the practitioners are fully aware of the growth status and number of the seedlings. The data are integrated with the cultivation management records and greenhouse system data to serve as an important basis for decision-making and big data analysis. With this, the estimated shipment volume, subsequent sales process analysis, and planning are all in good control to achieve timely and stable product shipment. The introduction of big data applications, besides reducing the costs and increasing revenue for agricultural operators, is also helpful for future planting planning or market arrangement, to empower the farmers to keep pace with the times and continuously improve competitiveness.

Big data analysis has brought opportunities for changes in agriculture. There are still many unsolved problems and challenges, but no doubt smart agriculture will greatly reduce the workload of the practitioners. To adopt inter-disciplinary innovative technologies to promote the transformation of agriculture into a sustainable, more valuable and profitable industry is certainly the new opportunities and also challenges for smart agriculture. The integrated application of big data and other new technologies will play an indispensable role in this regard.