Chlorophyll is a key substance in plant photosynthesis, and its content detection methods are of great significance in the field of agricultural AI. These methods provide important technical support for crop growth monitoring, pest and disease identification, and yield prediction, playing a crucial role in improving agricultural productivity and the level of intelligence in farming. This paper aims to explore an efficient and low-cost non-destructive method for detecting chlorophyll content (SPAD) and investigate the feasibility of smartphone image analysis technology in predicting chlorophyll content in greenhouse tomatoes. This study uses greenhouse tomato leaves as the experimental object and analyzes the correlation between chlorophyll content and image color features. First, leaf images are captured using a smartphone, and 42 color features based on the red, green, and blue (R, G, B) color channels are constructed to assess their correlation with chlorophyll content. The experiment selects eight color features most sensitive to chlorophyll content, including B, (2G − R − B)/(2G + R + B), GLA, RGBVI, g, g − b, ExG, and CIVE. Based on this, this study constructs and evaluates the predictive performance of multiple models, including multiple linear regression (MLR), ridge regression (RR), support vector regression (SVR), random forest (RF), and the Stacking ensemble learning model. The experimental results indicate that the Stacking ensemble learning model performs the best in terms of prediction accuracy and stability (R² = 0.8359, RMSE = 0.8748). The study confirms the feasibility of using smartphone image analysis for estimating chlorophyll content, providing a convenient, cost-effective, and efficient technological approach for crop health monitoring and precision agriculture management. This method helps agricultural workers to monitor crop growth in real-time and optimize management decisions.

To address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. Based on YOLO11m-pose, the original backbone network is replaced with the lightweight StarNet-S1 backbone, reducing model complexity. Additionally, an improved C3K2_PartialConv neck module is used to enhance information interaction and fusion among multi-scale features while maintaining computational efficiency. The Focaler-IoU loss function is employed to improve keypoint localization accuracy. Results show that the improved model achieves an mAP50-95 of 0.924, with a floating-point operation count (GFLOPs) of 32.1, and reduces the model size to 1.229 × 10⁷ parameters. This model demonstrates better computational efficiency and lower resource consumption, providing an effective lightweight solution for crop phenotypic analysis.