Author：Yi-Lung Chang, Yu-Ching Tsai, Hui-Ju Chang

Introduction

In today's agriculture, the integration of advanced technologies like artificial intelligence (AI) is reshaping the way how we grow crops. With increasing demands on food production and the growing need for efficiency, AI is stepping in to provide farmers with powerful tools for improving their operations. One such tool is image recognition, which has emerged asset for assessing seedling quality in nurseries. For crops like cabbages, ensuring seedling health and uniformity is critical to a successful harvest. Traditionally, nursery workers manually inspect each seedling, a process that is labor-intensive, time-consuming, and prone to errors. However, AI-driven image recognition systems might be transforming this process by providing a faster, more accurate, and scalable solution.

Current Challenges in Seedling Quality Assessment

Ensuring the quality of cabbage seedlings is essential for achieving high crop yields. Traditional methods of assessing seedlings present several challenges. Manual inspection is a time-consuming process that requires significant labor. A single nursery can house thousands of seedlings, and checking each one for size, health, and uniformity can take hours a day. Moreover, human inspection is inherently inconsistent. Different workers may apply different standards when assessing seedlings, leading to variability in quality control. Fatigue, distractions, and environmental conditions can all affect the accuracy of manual evaluations. We might think that by automating this process with AI-power, these challenges can be addressed. But what exactly does it bring to the table?

How Image Recognition Enhances Seedling Quality Assessment

The systems use cameras mounted on automated rigs to capture images of seedlings when they grow in trays. These cameras move along the X and Y axes, scanning every seedling from consistent angles. After the scanning, images will be then processed using machine learning algorithms, such as YOLO (You Only Look Once), which analyze each seedling's size, shape, and color. Data augmentation techniques are applied to ensure accuracy. This involves creating multiple variations of each image—rotating, scaling, or adjusting brightness—to train the model on different scenarios. The more data the system processes, the better it becomes at recognizing patterns that indicate healthy or underperforming seedlings. In trials conducted in TSIPS on 2023, AI systems have achieved an initial recognition accuracy of average 83%, with around 22 out of 128 cells in a typical tray being unidentifiable. This level of accuracy might not yet fit the standard for industries, but definitely a good start in this field.

What Farmers Can Expect

Adopting new technology can be a big decision for farmers, especially when considering costs, ease of use, and reliability. While the upfront investment for the system may seem substantial, the long-term benefits far outweigh the initial cost. By reducing labor needs and increasing productivity, many farmers see a return. We can expect that the cost of the technology will become more accessible over time, so the cost might not the only considering at all. The design with user-friendliness might be the critical element. Can farmers monitor their seedling health from a tablet or smartphone with real-time data updated? This would be very fundamental issues keeping in mind. Furthermore, how the customer support and training work to ensure smooth operation will be another considering. At last, the systems have to demonstrate high reliability in real-world settings, achieving consistent performance across multiple nursery trials. With built-in error detection and regular software updates, the system can maintain a high level of accuracy over time. TSIPS are testing in different nurseries to make sure quality assessments is stable.

Future Applications and Conclusion

Looking ahead, the role in nursery farming is expected to expand beyond seedling assessment. Potential future applications include: (1)Early disease and pest detection: It could be used to detect early signs of disease or pest by analyzing subtle changes in seedling color or texture. (2) Automated transplanting systems: while integrated with robotic systems to handle the transplanting process, ensuring that only the good quality seedlings are selected. (3) Integration with IoT: when working alongside IoT devices, such as moisture sensors or climate control systems, to automate and optimize the entire nursery environment.

AI-powered image recognition is revolutionizing the way nursery farmers assess seedling quality. By automating the inspection process, this technology offers farmers a faster, more accurate, and scalable solution that reduces labor costs and improves operational efficiency. The future of AI in agriculture looks promising, with potential applications that go beyond quality assessment, helping farmers manage their crops more effectively and sustainably. As this technology continues to evolve, we are sure to devote more into this industry to accelerate the transformation.