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Real-time tracking of soybean seedling development demands an efficient approach for correctly recognizing seedlings and eliminating weeds. Difficulties stem from the tiny scale and shape-based likeness between seedlings and weeds, which makes standard detection techniques more complex. In response to these issues, a real-time detection framework termed YOLOv9-GSSA is introduced. A refined Mosaic-Dense algorithm boosts object concentration at the input stage of the model, improving its power to capture subtle features. Additionally, a neck optimization component called GSSA, integrating GSConv with Gated Self-Attention, aids essential information extraction and the exchange of features across scales. The Swin-GSSA prediction head further draws on spatial positioning cues, enabling better small object recognition and resolution of overlapping occlusion. Tests reveal that our model secures a mAP of 47.5% with a per-image detection speed of 23.42 ms, making it viable for real-time surveillance. The upgraded model notably strengthens the recognition of soybean seedlings and weeds, functioning as a useful asset for efficient farmland oversight. This ultimately contributes to precise yield forecasting and informed choices in precision agriculture.

Achieving sustainability and efficiency in maintaining high crop yields while minimizing agricultural chemical pollution requires precise weed mapping to fully deploy site-specific weed management—a persistent, major hurdle in contemporary farming. This investigation tested the resilience of training epochs for the You Only Look Once (YOLO) v5s convolutional neural network (CNN) model in building an automated system for classifying crops and weeds from UAV-acquired imagery. The images were labeled with bounding boxes and trained on Google Colab across 100, 300, 500, 600, 700, and 1000 epochs. The network identified and sorted five distinct categories: sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. Training proceeded over multiple epochs to pinpoint the peak test-set performance and was terminated once the test metrics (overall accuracy, precision, and recall) began to decline. The findings reveal a marked improvement in classifier capability as the epoch count climbed from 100 to 600. However, a modest reduction surfaced at 700 epochs, where overall accuracy, weed precision, and recall registered 65, 43, and 43%, respectively, contrasted with the 67, 78, and 34% achieved on these same metrics at 600 epochs. This erosion continued at 1000 epochs, producing an overall accuracy of 65%, a weak precision of 45%, and a recall of 40%. These outcomes confirm that the training epoch length in YOLOv5s substantially governs the model’s reliability for automated crop and weed discrimination, singling out 600 epochs as the optimal performance point.