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Leaf blight, caused by bacterial and fungal infections, threatens plant health by inducing leaf discoloration and reducing crop yield. To address this, we propose a MobileNetV3-based classifier for the Jasmine plant, leveraging streamlined Convolutional Neural Networks (CNNs) to precisely identify disease stages. The design combines depthwise convolutional layers with max-pooling layers to extract superior features, focusing on vital low-level features that signal the condition. Via preprocessing methods that include data augmentation with a Conditional GAN and Particle Swarm Optimization for feature selection, the classifier achieves reliable performance. Assessment on carefully assembled datasets shows a remarkable 97% training accuracy, demonstrating its potency. Real-world trials across varying settings, including severe camera tilts and fluctuating illumination, verify the model’s durability, delivering test accuracies within the 94% to 96% range. The dataset’s bespoke structure for CNN-based classification ensures the reliability of the outcomes. Notably, the model’s compact classification, characterized by swift processing speed and minimal size, makes it a practical choice for immediate deployment. This all-encompassing strategy highlights the presented classifier’s value in managing leaf blight spot disease difficulties in cash crops.

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.

Grain buyers reject canola kernels that have been heated or remain immature due to excessive drying or frost exposure, as these defects reduce both oil extraction rates and commercial value. The current grading protocol relies on visually inspecting each seed's endosperm hue and manually tallying the damaged specimens. Given that this method is tedious, labour-intensive, and prone to human error, the present work proposes an automated grading pipeline based on object detection, multi-object tracking, and enumeration. The detection phase was accomplished using a refined YOLOv7 architecture (YOLOv7_ours), which was adapted to bolster its small-object recognition accuracy by embedding two convolutional block attention modules into the neck section, while simultaneously lowering its computational demand (cost) and footprint by swapping conventional convolutional layers for ghost layers across all Efficient Layer Aggregation Networks modules and within the Spatial Pyramid Pooling Cross Stage Partial module native to YOLOv7. The trained network parameters were then passed to the ByteTrack multiple-object tracker, enabling frame-by-frame tracking of detections throughout a video sequence. The distinct identifiers generated by the tracker for each target of interest were subsequently used to tabulate defect counts using a line-crossing procedure. Following training, the YOLOv7_ours model delivered a mean average precision ([email protected]) that was 1.02% superior, alongside a 32.1% reduction in cost and a 37.1% reduction in size relative to the baseline YOLOv7 model. Assessed on a test video, the full pipeline achieved a multi-object tracking accuracy of 84.8% and a counting accuracy of 93.9%. This three-component framework is ready for integration into edge devices, enabling grain buyers to precisely grade canola kernels in real time.