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The choice of methodologies is a pivotal factor in disease identification, particularly when computer vision intersects with agricultural innovation. Safeguarding global food supplies depends on the timely and accurate identification of crop diseases. Deep learning offers a feasible pathway to satisfy this demand. To move this research forward, we engineered and tested a disease identification framework grounded in a novel ensemble strategy. Our contribution includes the presentation of DenseNetMini, a more compact iteration of the DenseNet architecture. We advocate merging DenseNetMini with a learning resizer via an ensemble approach to improve training fidelity and accelerate knowledge acquisition. A further innovative element involves deploying Gradient Product (GP) as the optimization mechanism, efficiently reducing the training time while simultaneously improving model performance. Investigating imagery at varying magnifications reveals remarkable improvements in diagnostic consensus and precision. Accuracy figures on test data reach 99.65%, 98.96%, and 98.11% for the Plantvillage, Tomato leaf, and Appleleaf9 collections, respectively. A core triumph of this investigation is the substantial reduction in computational time, suggesting that GP adoption could make agricultural disease screening more accessible and operationally efficient. Beyond numerical gains, the work foregrounds Explainable Artificial Intelligence (XAI) approaches as indispensable for enhancing the interpretability and clarity of the disease detection framework. By visually pinpointing diagnostically relevant zones on foliage, XAI enhances the model’s comprehensibility, thereby nurturing user confidence and insight into how it arrives at its conclusions.

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.

Quantifying fruit mass and volume manually is a laborious, repetitive task that can hinder production scheduling. This investigation developed a computer vision framework to predict the volume and mass of baobab fruits from single-view photographs captured with affordable, widely accessible cameras, including smartphones. The baobab fruits were sourced from two experimental sites situated within the savanna ecological zone. Following image capture, the fruits were detected and segmented with an accuracy exceeding 97%. The segmented outputs were converted to binary format, from which two-dimensional (2D) attributes—namely the segmented area, centroid, bounding box, equivalent diameter, and major diameter—were derived. Volume estimations were generated from the 2D attributes by employing random forest, linear, polynomial, and radial support vector machine regressors. Every model achieved strong goodness-of-fit, yet the random forest regressor performed best, with an R2 of 99.8%. The linkage between mass and volume conformed to a quadratic expression (mass = 38.23 + 0.25 × volume + 4.49e−05 × volume2) and achieved an R2 of 92%. Correlations were substantiated through graphical plots and statistical evaluations, with credible intervals for the point estimates derived from the posterior distributions of their respective samples. This finding spotlights the potential to deploy artificial intelligence techniques in a less restrictive environment to support ecological studies and agricultural oversight. Commercial firms that manufacture baobab powder and seed oil ought to adopt these models to streamline production planning. Acquiring deeper insight into how climatic gradients shape the morphological properties of baobab fruits would help refine the model.