Smart Agriculture Takes Root: Farmers Embrace the High Tech with Ease
Yuni Tsai（China Productivity Center Smart Agriculture Promotion Department）
As the backbone of Taiwan’s primary industries, agriculture has long played a foundational role in sustaining the nation's economy. Yet in recent years, it has come under mounting pressure from both internal and external challenges—including an aging rural population, accelerating climate change, and an escalating global food crisis. The rapid maturation of technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and deep learning—combined with the ultra-fast, low-latency capabilities of 5G networks—is enabling seamless cross-platform integration across domains. These innovations are making data collection and analysis more immediate and precise than ever, offering farm operators powerful tools for decision-making in production planning, resource management, and customer engagement. The push toward smart agriculture is a must. Taiwan now faces both an opportunity and a challenge: how to effectively integrate these advanced technologies to optimize agricultural resources and ensure the industry’s long-term sustainability.
IoT in the Agricultural Fields
The Internet of Things (IoT) is revolutionizing agriculture. Leveraging its high-connectivity capabilities, IoT-enabled sensors are now widely deployed across open fields, greenhouses, and livestock facilities—allowing farmers to remotely monitor crop growth conditions in real time via handheld devices, no matter where they are. In the livestock sector, the ability to track animal movement and physiological conditions instantly—paired with expert systems—enables up-to-date health data on poultry and livestock to be transmitted to veterinarians or specialists. This real-time intelligence supports more accurate diagnoses and faster interventions. Even more transformative is the integration of big data. By aggregating diverse inputs such as climate variables and crop growth patterns, the system can optimize work scheduling and resource allocation. This not only improves yields and profitability but also helps resolve chronic supply-demand mismatches in the marketplace. As internet access becomes increasingly ubiquitous and technology continues to evolve at a rapid pace, production data is becoming more transparent. This growing visibility is fostering a closer connection between producers and consumers, driving a strategic shift from production-driven farming to market-oriented agriculture—enabled by precise, data-driven management.
AI-driven Pest Monitoring Sensors are Here for your Crop Protection
Pest outbreaks remain one of the most persistent threats to crop productivity and quality in Taiwan, where a wide range of harmful species can rapidly surge and wreak havoc across farmlands. Traditionally, pest surveillance has relied heavily on manual observation which is a process that is labor-intensive, time-consuming, and often lacks the immediacy needed for effective response. Today, that’s changing with the integration of AI-powered pest image recognition systems in the field. These smart sensors automatically capture and analyze images from sticky traps, while also monitoring environmental factors such as temperature, humidity, light intensity, and gas concentration. The collected data is transmitted in real time to backend servers, where deep learning algorithms identify between four to eight major pest species prevalent in the area. All data is recorded in a cloud-based system, allowing users to monitor daily increases in individual pest species and view historical trends through a web portal or mobile app. The color-coded indicators on the interface visualize infestation severity at a glance, and any unusual spikes in pest activity can trigger instant alerts through apps or messaging platforms—ensuring that farmers and agricultural experts can take timely action. To date, this AIoT (Artificial Intelligence + IoT) monitoring system has been deployed across more than 10 crop production sites in Taiwan, with nearly 100 active sensor nodes in operation, bringing in precision pest management of the digital age.
The Rise of Agricultural Drones: Precision from the Sky
The drone era has officially taken flight in agriculture. Once limited to aerial pesticide spraying, drones are now evolving into versatile tools for monitoring crop health, detecting pest outbreaks, and issuing early warnings over large-scale farmlands. By integrating multispectral imaging and remote sensing technologies, drones offer a real-time, cost-effective, and labor-saving solution to modern farming challenges. For government agencies, drone deployment represents a breakthrough in agricultural oversight. These aerial systems can support comprehensive monitoring and accreditation of farming practices and foster a win-win scenario: improved farmer livelihoods, reduced environmental impact, and safer, supply of agricultural products that use fewer pesticides and fertilizers for consumers. It is envisioned that in the future, drones will be centrally managed by an automated control system that plans and schedules flight missions based on crop growth conditions. The system would assess the appropriate types and amounts of pesticides, which would then be filled by robotic systems. The drones would then automatically fly to designated areas for spraying, making agricultural production more efficient and environmentally friendly.
Non-Destructive Product Inspection Techniques
Product quality control has become a critical focus in recent years. Traditional methods typically rely on manual sampling, involving lengthy and complex testing procedures that fail to provide real-time results. Moreover, invasive testing techniques often damage samples, rendering them unsellable or unfit for consumption. These limitations make large-scale or comprehensive inspections challenging using conventional approaches. However, in recent years, hyperspectral imaging technology has yielded highly fruitful research results. Compared to conventional multispectral imaging, hyperspectral imagery features higher spatial resolution and a greater number of spectral bands. Its spectral data is nearly continuous, and each band covers a very narrow wavelength range, enabling the acquisition of vast amounts of data. As a result, hyperspectral images contain far more detailed information than typical multispectral images, allowing for a more comprehensive display of subtle spectral differences among various targets. Hyperspectral imaging typically uses hundreds of spectral bands to form images. Once the corresponding bands for different target objects are identified, hyperspectral technology can perform classification with high accuracy. However, interpreting this wealth of information requires more than single-wavelength analysis. By combining hyperspectral data with advanced deep learning algorithms, computer models can be trained to accurately classify and evaluate product quality with high precision. This cutting-edge technique is already making an impact in industries such as king oyster mushroom and pineapple production, where it aids in product grading while addressing labor shortages—ushering in a new era of fast, accurate, and non-destructive quality control.
Advancing smart agriculture goes beyond just integrating and innovating production technologies, it also requires seamless integration of data and information to support the entire production-to-sales process. It is crucial to break down barriers between datasets scattered across various regulatory and industry bodies. By enabling unobstructed data streaming, stakeholders can create a value chain that efficiently collects, integrates, analyzes, and feeds back critical insights, thereby empowering smarter decisions and more agile agricultural management.