Accurately identifying crop pests and diseases ensures agricultural productivity and safety. Although current YOLO-based detection models offer real-time capabilities, their conventional convolutional layers involve high computational redundancy and a fixed receptive field, making it challenging to capture local details and global semantics in complex scenarios simultaneously. This leads to significant issues like missed detections of small targets and heightened sensitivity to background interference. To address these challenges, this paper proposes a lightweight adaptive detection network—StarSpark-AdaptiveNet (SSANet), which optimizes features through a dual-module collaborative mechanism. Specifically, the StarNet module utilizes Depthwise separable convolutions (DW-Conv) and dynamic star operations to establish multi-stage feature extraction pathways, enhancing local detail perception within a lightweight framework. Moreover, the Multi-scale Adaptive Spatial Attention Gate (MASAG) module integrates cross-layer feature fusion and dynamic weight allocation to capture multi-scale global contextual information, effectively suppressing background noise. These modules jointly form a “local enhancement-global calibration” bidirectional optimization mechanism, significantly improving the model’s adaptability to complex disease patterns. Furthermore, the proposed Scale-based Dynamic Loss (SD Loss) dynamically adjusts the weight of scale and localization losses, improving regression stability and localization accuracy, especially for small targets. Experiments on the eggplant fruit disease dataset demonstrate that SSANet achieves an mAP50 of 83.9% and a detection speed of 273.5 FPS with only 2.11 M parameters and 5.1 GFLOPs computational cost, outperforming the baseline YOLO11 model by reducing parameters by 18.1%, increasing mAP50 by 1.3%, and improving inference speed by 9.1%. Ablation studies further confirm the effectiveness and complementarity of the modules. SSANet offers a high-accuracy, low-cost solution suitable for real-time pest and disease detection in crops, facilitating edge device deployment and promoting precision agriculture.

In recent decades, brain tumors have emerged as a serious neurological disorder that often leads to death. Hence, Brain Tumor Segmentation (BTS) is significant to enable the visualization, classification, and delineation of tumor regions in Magnetic Resonance Imaging (MRI). However, BTS remains a challenging task because of noise, non-uniform object texture, diverse image content and clustered objects. To address these challenges, a novel model is implemented in this research. The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN, which effectively captures the fine-grained tumor features to enhance segmentation precision. MRI images are initially acquired from three online datasets: Dataset 1—Brain Tumor Segmentation (BraTS) 2018, Dataset 2—BraTS 2019, and Dataset 3—BraTS 2020. Subsequently, the Switchable Normalization-based Faster Regions with Convolutional Neural Networks (SNFRC) model is proposed for improved BTS in MRI images. In the proposed model, Switchable Normalization is integrated into the conventional architecture, enhancing generalization capability and reducing overfitting to unseen image data, which is essential due to the typically limited size of available datasets. The network depth is increased to obtain discriminative semantic features that improve segmentation performance. Specifically, Switchable Normalization captures the diverse feature representations from the brain images. The Faster R-CNN model develops end-to-end training and effective regional proposal generation, with an enhanced training stability using Switchable Normalization, to perform an effective segmentation in MRI images. From the experimental results, the proposed model attains segmentation accuracies of 99.41%, 98.12%, and 96.71% on Datasets 1, 2, and 3, respectively, outperforming conventional deep learning models used for BTS.