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This research presents a comprehensive assessment of the latest YOLOv8 architecture’s suitability for object detection applications, specifically adapted for smoke and wildfire recognition, with particular attention to agricultural and environmental safeguarding. Every available YOLOv8 variant underwent initial fine-tuning on a field-specific dataset that covers diverse conditions essential for robust agricultural oversight. Performance indicators led to selecting the ‘large’ model (YOLOv8l) for deeper hyperparameter tuning. This model was subjected to meticulous hyperparameter refinement via the One Factor At a Time (OFAT) strategy, zeroing in on pivotal variables such as learning rate, batch size, weight decay, epochs, and optimizer. Knowledge gained from the OFAT phase was used to define search boundaries for the subsequent Random Search (RS). The ultimate model produced through RS displayed considerable advancement over the baseline fine-tuned version, boosting overall precision by 1.39%, recall by 1.48%, F1-score by 1.44%, [email protected] by 0.70%, and [email protected]:0.95 by 5.09%. The improved model’s performance was confirmed across a wide range of real-world photographs depicting different agricultural landscapes, affirming its robustness in smoke and fire recognition. These outcomes underscore the model’s trustworthiness and practical value in contexts vital to agricultural protection and environmental surveillance. This investigation, marking a notable leap forward in machine learning-based fire and smoke identification, lays a firm foundation for subsequent inquiries and innovations aimed at protecting agricultural areas and natural ecosystems.

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.