It is of great significance to improve the efficiency and accuracy of crop pest detection in complex natural environments, and to change the current reliance on expert manual identification in the agricultural production process. Targeting the problems of small target size, mimicry with crops, low detection accuracy, and slow algorithm reasoning speed in crop pest detection, a complex scene crop pest target detection algorithm named YOLOv8-Entend was proposed in this research.

Firstly, the GSConv was introduecd to enhance the model's receptive field, allowing for global feature aggregation. This mechanism enables feature aggregation at both node and global levels simultaneously, obtaining local features from neighboring nodes through neighbor sampling and aggregation operations, enhancing the model's receptive field and semantic understanding ability. Additionally, some Convs were replaced with lightweight Ghost Convolutions and HorBlock was utilized to capture longer-term feature dependencies. The recursive gate convolution employed gating mechanisms to remember and transmit previous information, capturing long-term correlations. Furthermore, Concat was replaced with BiFPN for richer feature fusion. The bidirectional fusion of depth features from top to bottom and from bottom to top enhances the transmission of feature information acrossed different network layers. Utilizing the VoVGSCSP module, feature maps of different scales were connected to create longer feature map vectors, increasing model diversity and enhancing small object detection. The convolutional block attention module (CBAM) attention mechanism was introduced to strengthen features of field pests and reduce background weights caused by complexity. Next, the Wise IoU dynamic non-monotonic focusing mechanism was implemented to evaluate the quality of anchor boxes using "outlier" instead of IoU. This mechanism also included a gradient gain allocation strategy, which reduced the competitiveness of high-quality anchor frames and minimizes harmful gradients from low-quality examples. This approach allowed WIoU to concentrate on anchor boxes of average quality, improving the network model's generalization ability and overall performance. Subsequently, the improved YOLOv8-Extend model was compared with the original YOLOv8 model, YOLOv5, YOLOv8-GSCONV, YOLOv8-BiFPN, and YOLOv8-CBAM to validate the accuracy and precision of model detection. Finally, the model was deployed on edge devices for inference verification to confirm its effectiveness in practical application scenarios.

The results indicated that the improved YOLOv8-Extend model achieved notable improvements in accuracy, recall, mAP@0.5, and mAP@0.5:0.95 evaluation indices. Specifically, there were increases of 2.6%, 3.6%, 2.4% and 7.2%, respectively, showcasing superior detection performance. YOLOv8-Extend and YOLOv8 run respectively on the edge computing device JETSON ORIN NX 16 GB and were accelerated by TensorRT, mAP@0.5 improved by 4.6%, FPS reached 57.6, meeting real-time detection requirements. The YOLOv8-Extend model demonstrated better adaptability in complex agricultural scenarios and exhibited clear advantages in detecting small pests and pests sharing similar growth environments in practical data collection. The accuracy in detecting challenging data saw a notable increased of 11.9%. Through algorithm refinement, the model showcased improved capability in extracting and focusing on features in crop pest target detection, addressing issues such as small targets, similar background textures, and challenging feature extraction.

The YOLOv8-Extend model introduced in this study significantly boosts detection accuracy and recognition rates while upholding high operational efficiency. It is suitable for deployment on edge terminal computing devices to facilitate real-time detection of crop pests, offering technological advancements and methodologies for the advancement of cost-effective terminal-based automatic pest recognition systems. This research can serve as a valuable resource and aid in the intelligent detection of other small targets, as well as in optimizing model structures.

There's a growing demand among plant cultivators and breeders for efficient methods to acquire plant phenotypic traits at high throughput, facilitating the establishment of mappings from phenotypes to genotypes. By integrating mobile phenotyping platforms with improved instance segmentation techniques, researchers have achieved a significant advancement in the automation and accuracy of phenotypic data extraction. Addressing the need for rapid extraction of leaf age and canopy width phenotypes in strawberry plants cultivated in controlled environments, this study introduces a novel high-throughput phenotyping extraction approach leveraging a mobile phenotyping platform and instance segmentation technology.

Data acquisition was conducted using a compact mobile phenotyping platform equipped with an array of sensors, including an RGB sensor, and edge control computers, capable of capturing overhead images of potted strawberry plants in greenhouses. Targeted adjustments to the network structure were made to develop an enhanced convolutional neural network (Mask R-CNN) model for processing strawberry plant image data and rapidly extracting plant phenotypic information. The model initially employed a split-attention networks (ResNeSt) backbone with a group attention module, replacing the original network to improve the precision and efficiency of image feature extraction. During training, the model adopted the Mosaic method, suitable for instance segmentation data augmentation, to expand the dataset of strawberry images. Additionally, it optimized the original cross-entropy classification loss function with a binary cross-entropy loss function to achieve better detection accuracy of plants and leaves. Based on this, the improved Mask R-CNN description involves post-processing of training results. It utilized the positional relationship between leaf and plant masks to statistically count the number of leaves. Additionally, it employed segmentation masks and image calibration against true values to calculate the canopy width of the plant.

This research conducted a thorough evaluation and comparison of the performance of an improved Mask R-CNN model, underpinned by the ResNeSt-101 backbone network. This model achieved a commendable mask accuracy of 80.1% and a detection box accuracy of 89.6%. It demonstrated the ability to efficiently estimate the age of strawberry leaves, demonstrating a high plant detection rate of 99.3% and a leaf count accuracy of 98.0%. This accuracy marked a significant improvement over the original Mask R-CNN model and meeting the precise needs for phenotypic data extraction. The method displayed notable accuracy in measuring the canopy widths of strawberry plants, with errors falling below 5% in about 98.1% of cases, highlighting its effectiveness in phenotypic dimension evaluation. Moreover, the model operated at a speed of 12.9 frames per second (FPS) on edge devices, effectively balancing accuracy and operational efficiency. This speed proved adequate for real-time applications, enabling rapid phenotypic data extraction even on devices with limited computational capabilitie.

This study successfully deployed a mobile phenotyping platform combined with instance segmentation techniques to analyze image data and extract various phenotypic indicators of strawberry plant. Notably, the method demonstrates remarkable robustness. The seamless fusion of mobile platforms and advanced image processing methods not only enhances efficiency but also ignifies a shift towards data-driven decision-making in agriculture.