In the heart of China’s agricultural innovation, a breakthrough in shiitake mushroom cultivation is set to revolutionize the industry. Researchers have developed a cutting-edge detection model that promises to streamline harvest evaluation, reducing labor intensity and boosting efficiency. This advancement, published in the journal *智慧农业*, could have significant commercial impacts for the agriculture sector, particularly in large-scale mushroom production.
The study, led by WANG Fengyun and colleagues from the Shandong Academy of Agricultural Sciences and Qilu University of Technology, addresses a critical challenge in the shiitake mushroom industry: the manual labor-intensive process of harvesting and grading. “Although mixing, bagging, sterilization, and inoculation have been largely automated, harvesting and grading still depend heavily on manual labor,” explains lead author WANG Fengyun. “This leads to high labor intensity, low efficiency, and inconsistency caused by subjective judgment, thereby restricting large-scale production.”
To tackle this issue, the researchers proposed an improved real-time detection model named FSE-DETR, based on the RT-DETR framework. The model incorporates several innovative features, including the FasterNet Block, a Small Object Feature Fusion Network (SFFN), and the Efficient Intersection over Union (EIoU) loss function. These enhancements enable the model to achieve a remarkable balance between precision and efficiency, with an accuracy of 95.8%, a recall of 93.1%, and a mAP50 of 95.3%.
The implications for the agriculture sector are profound. “The proposed FSE-DETR model integrated the FasterNet Block, Small Object Feature Fusion Network, and EIoU loss into the RT-DETR framework, achieving state-of-the-art accuracy while maintaining lightweight characteristics,” says WANG Fengyun. “The model showed strong adaptability to small targets, occlusion, and complex illumination, making it a reliable solution for intelligent mushroom harvest evaluation.”
The commercial potential of this research is immense. By automating the harvest evaluation process, farmers can reduce labor costs, increase efficiency, and improve the consistency of their products. This could lead to significant economic benefits, particularly for large-scale mushroom producers. Moreover, the model’s robustness and reliability in practical mushroom factory environments make it a valuable tool for real-world applications.
Looking ahead, this research could shape future developments in the field of agricultural technology. The FSE-DETR model’s success in detecting shiitake mushrooms suggests that similar models could be developed for other crops, further enhancing the efficiency and productivity of agricultural operations. As the agriculture sector continues to embrace technological innovation, breakthroughs like this one will play a crucial role in shaping the future of farming.
The study, led by WANG Fengyun, WANG Xuanyu, AN Lei, and FENG Wenjie, was published in *智慧农业* and represents a significant step forward in the application of deep learning and object detection in agriculture. With its balance of precision and efficiency, the FSE-DETR model demonstrates great potential for deployment in real-world factory production and provides a valuable reference for developing high-performance, lightweight detection models for other agricultural applications.