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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.

Vegetation cultivated in aquaponics environments is prone to multiple diseases or biotic pressures throughout its development, similarly to conventional farming. Timely identification of these diseases is essential to maintaining the effectiveness of the aquaponics setup and advancing its development. Aquaponics merges recirculating aquaculture with soilless hydroponic cultivation and has the potential to ensure food security, alleviate water shortages, and reduce carbon emissions. To facilitate the widespread adoption of this agricultural method, an integrated platform is required to identify plant ailments and assist scientists and growers in identifying potential causes and remedies at an early stage. This research introduces an automated plant diagnostic framework for recognizing biotic pressures and controlling diseases in four leafy green vegetables—lettuce, basil, spinach, and parsley—cultivated within an aquaponics environment. Initially, a collection of 2640 images is assembled. Subsequently, a disease identification system operating in three stages is created. The initial stage involves a crop classification mechanism that determines the plant species. The second stage comprises a disease identification mechanism that assesses the plant’s health condition. The concluding stage is a disease-detection mechanism that pinpoints diseased and healthy areas on foliage and classifies the illness. The suggested method has yielded encouraging results, achieving accuracies of 95.83%, 94.13%, and 82.13% in the three stages, respectively. The ultimate disease-detection mechanism is then combined with an ontology framework via a cloud-based interface. This ontology framework includes domain expertise concerning plant pathology, specifically the origins and remedies for various ailments of the examined leafy green vegetables, which can be automatically retrieved upon illness detection, enabling farm operators to implement preventive actions. The suggested application is important as a decision-support tool that automates health surveillance in aquaponics facilities and helps agricultural practitioners make decisions on crop and disease control.

In contemporary farming, visual perception systems grounded in deep learning are emerging to equip autonomous vehicles with the capability to perform crop-field interventions. Yet securing strong performance from these techniques hinges on access to large volumes of data, a resource that is typically scarce in agricultural contexts. The core reason is that constructing an agricultural dataset that captures a given problem in exhaustive breadth is difficult, since the visual presentation of symptoms is subject to change. There is pronounced sensitivity to ambient variables such as heat, dampness, and lighting regimes. An efficient pipeline is therefore imperative to methodically shepherd the full sequence of building an agricultural dataset, from initial image capture through to its web-facing release. This manuscript describes the Eden Library, a platform conceived to help close the recognized shortfall in freely available crop and plant repositories that span both close-range and overhead imagery. The end-to-end workflow underpinning the platform’s conception and launch is also set forth and explored. This workflow merits attention because the datasets on offer are designed not as static, one-off research outputs confined to a narrow slice of species, growth stages, and environmental circumstances, but rather as evolving resources to be stewarded and expanded across time. Annotations delineating plants and their associated symptoms are supplied, thereby relieving users of the manual labeling workload. At the time of writing, the Eden Library spans 15 distinct crop species, 9 weed varieties, and 30 forms of disorder (encompassing pests, diseases, and nutritional shortfalls). The Eden Library endeavors to narrow this divide by delivering a voluminous, richly varied image trove of plants, curated coherently, to accelerate the development of future vision-driven, AI-augmented field tools.

Traditional weed control strategies are ineffective and cannot be readily combined with intelligent farm machinery. The automated recognition and categorization of weeds is crucial for weed management and for enhancing agricultural output. The success of intelligent, precise spot-spraying platforms hinges on the accuracy of computer-vision-based detectors used for autonomous weed suppression. This work examined the practicality of deep learning frameworks (Alexnet, GoogLeNet, InceptionV3, Xception) for detecting weeds in RGB pictures taken in a bell pepper crop. The architectures were trained with differing epoch values (10, 20, 30) and batch sizes (16, 32), while hyperparameters were fine-tuned to secure the best possible results. Overall accuracy for the selected architectures spanned from 94.5 to 97.7%. InceptionV3 stood out, delivering peak performance with a 30-epoch run and a batch size of 16, attaining 97.7% accuracy, 98.5% precision, and 97.8% recall. Regarding this InceptionV3 configuration, the type I and type II error rates were 1.4% and 0.9%, respectively. The proven power of deep learning models charts a straightforward course for integrating these algorithms with image-guided herbicide delivery units to achieve precise weed management.