<![CDATA[Segmenting plant leaves in natural environments was challenging due to fluctuating lighting, complex backgrounds, and heterogeneous leaf morphology. This study was conducted aiming at addressing the above mentioned issues by developing a modified U-Net architecture for segmenting Eucalyptus pellita seedlings in open nursery settings. The proposed solution introduced a ResNet50 encoder pre-trained on ImageNet, enhanced regularization in the decoder, and a combined loss function comprising Binary Cross-Entropy and Dice Loss to optimize pixel-wise accuracy and shape conformity. A total of 2,181 high-resolution RGB images were collected under three distinct lighting conditions: cloudy, sunny, and scorching. All images were manually annotated, stratified, and augmented with geometric and photometric transformations. Model training employed adaptive learning rates and early stopping strategies. The results showed the highest median segmentation score of 0.867 under cloudy conditions, followed by 0.853 under scorching conditions, and 0.838 under sunny conditions. Statistical testing confirmed significant differences across lighting scenarios. Visual inspection further demonstrated the model’s ability to preserve spatial details and mitigate the impact of shadows, reflections, and cluttered backgrounds. Despite the decline in precision under sunny conditions, segmentation consistency remained high. In conclusion, the developed model successfully addressed key challenges in leaf segmentation under variable outdoor lighting. The findings support its use for robust, high-precision segmentation, offering a foundation for real-time plant health monitoring in nursery-scale applications.]]>
