Australian nut flowers, known as macadamia inflorescences, is one variety of the Australian native trees in Yunnan Province, China. However, large-scale cultivation under high altitude has led to frequent off-season flowering and asynchronous flowering in the same tree. Reducing flowering synchrony is also detrimental to pollination, fertilization, fruit set, fruit retention, and uniform harvesting, thereby affecting yield stability and fruit quality. In addition, conventional flowering regulation can rely heavily on manual operations, leading to labor-intensive, low efficient, costly, and highly dependent on subjective experience. As a result, it is urgent demand for the high precision regulation of the macadamia inflorescences in smart orchard. In this study, an intelligent identification was proposed for macadamia inflorescences under natural environments using an improved YOLO11n object detection model. Specifically, several challenges were solved in real orchard environments, including complex background interference, multi-scale morphologies of inflorescences, partial occlusion, and the constraints of real-time edge deployment. 1) Partial Multi-Scale Feature Aggregation (PMSFA) module was introduced to integrate the partial convolution with multi-scale feature extraction. Multi-source heterogeneous features were efficiently captured from different network layers. Thereby, the small and easily occluded inflorescences were detected under cluttered natural backgrounds. 2) A Slim-neck architecture was constructed to balance accuracy and computational efficiency using Group-Shuffle Convolution (GSConv) and the Variety of View Group Shuffle Cross-Stage Partial Network (VoV-GSCSP). Computational complexity and parameter size were reduced to maintain the high accuracy of the detection, thus improving the edge deployment. 3) In terms of loss function optimization, Focaler-PIoU v2 was selected to replace the original Complete Intersection over Union (CIoU) loss in YOLO11n. The stability of bounding-box regression was improved from the gradient contribution of high-quality samples during training. Localization accuracy was enhanced under challenging conditions, such as occlusion and scale variation. A comparison was finally conducted on the macadamia inflorescences dataset from Yunnan Province. The results demonstrated that the improved YOLO11n model consistently outperformed mainstream object detection over all evaluation metrics. Specifically, precision increased by 20.1, 5.4, 1.7, 3.5, 3.6, 2.0, and 0.8 percentage points, respectively, while Recall increased by 1.4, 16.2, 8.1, 5.0, 6.4, 9.1, and 6.7 percentage points, respectively, compared with the Faster R-CNN, SSD, RT-DETR, YOLOv5s, YOLOv8n, YOLOv10n, and YOLOv12n. Mean average precision at an IoU threshold of 0.5 (mAP_0.5) was also improved by 6.4, 15.9, 5.7, 4.3, 4.1, 5.0, and 3.3 percentage points, respectively. Furthermore, the improvements reached 20.1, 33.5, 11.9, 13.5, 10.6, 11.0, and 9.7 percentage points, respectively, under the more mAP_0.5–0.95 threshold. There were superior precision, recall, and overall detection accuracy under complex natural conditions. To verify the feasibility, the improved model was also deployed on the Orange Pi AI Pro edge-computing platform. A real-time detection speed of 72.8 frames per second (FPS) was achieved using Neural Processing Unit (NPU) inference with multi-threaded scheduling, fully meeting the requirements of accurate and real-time recognition in natural orchard environments. Overall, there was a favorable balance among detection accuracy, computational efficiency, and deployment feasibility. The finding can also provide strong technical support for the precise flowering-period regulation for macadamia production in Yunnan Province, China.

Soil continuous cropping has been an ever-increasingly serious threat to modern agriculture in recent years. The soil disinfection method can be expected to effectively alleviate the soil's continuous cropping. Furthermore, the soil steam disinfection can be sown in a short period of time after disinfection, due to the non-toxic, pollution-free, compared with the chemical methods. But the low disinfection and heating efficiency has been limited in the process of red loam steam disinfection in practice. In this study, the disinfection pipe structural parameters were optimized to clarify the influence on the soil disinfection heating efficiency. Firstly, image processing was used to construct a soil pore structure discrete model. Secondly, the simulation was implemented to analyze the steam flow field in the disinfection pipe and the single-factor soil steam disinfection. Finally, the experiment was performed on the optimized structural parameters of multi-factor disinfection pipes using the soil vapor disinfection test platform. The results show that: 1) The circumferential and axial outlets of the disinfection pipe posed a significant effect on the heating efficiency of steam disinfection (P<0.01). The optimal structural parameters of the disinfection pipe were achieved: 2 mm pore size of the outlet pore, 3 circumferential pores, 2 axial pores. The average disinfection duration of the test when the average soil temperature reaches 80 ℃ is 394 s, and the average error rate between the test results and the predicted results is 5.3%; 2) The soil heating rates of each treatment reached the peak when the steam disinfection was carried out for 200 s. The efficiency of disinfection and heating gradually decreased after 200 s of disinfection. Future practical operations need to be guided by the variation of soil heating rate. Specifically, the steam valve should be gradually closed, the steam flow should be suspended, and the excess water in the soil should be removed to provide the soil pore permeability when the heating rate decreased gradually. Therefore, an intelligent control system can be expected to be combined, when conducting steam disinfection operations on Yunnan red loam in the later stage. The steam flow rate should be gradually reduced when disinfecting for 200 s. The soil temperature was redistributed and gradually decreased, as time increased. Once the soil temperature was below 60 ℃, the steam flow rate should be increased again, and the flow rate should be reduced and increased in a cyclic manner to ensure that the soil temperature was always above 60 ℃, in order to achieve the goal of efficient disinfection. The findings can provide a theoretical basis for efficient disinfection operations. 3) The range of single-pipe high-temperature areas in each treatment was mainly concentrated in the horizontal direction 0-150 mm and the vertical direction 0-200 mm. This study can provide a theoretical basis for the structural design of the disinfection pipes in the steam sterilizers and the intelligent disinfection operation, and lay a theoretical research foundation for the design of multi-pipe spacing of end actuators