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Driven by the worldwide surge in fish maw commerce, authenticity verification has emerged as an urgent concern due to mounting adulteration incidents. Conventional approaches to verification—whether sensory-based, chemically oriented, or molecular—suffer from practical deployment constraints. The present study reframes fish maw genuineness assessment as a detection task within the object recognition paradigm and introduces RDT-YOLO, a streamlined detection model grounded in YOLO11n architecture. Addressing the twin difficulties of insufficient nuanced texture representation and processing overhead in this domain, we engineer hierarchically structured reparameterized modules for feature extraction that deploy reparameterization tactics to amplify multi-resolution texture discrimination. To counteract degradation of information arising during cross-scale feature merging, a Dynamic Adaptive Multi-Scale Pyramid Processing (DAMSPP) component is devised, integrating adaptive convolution dynamics that facilitate responsive feature synthesis. Concurrently, an Adaptive Task-Aligned Detection Head (ATADH) is formulated, fusing interactive task mechanisms with weight-shared convolutions to achieve parameter economy alongside precision gains. Moreover, Wise-ShapeIoU, a novel loss formulation, is constructed by injecting Wise-IoU's focusing coefficient into the Shape-IoU framework, thereby sharpening detection fidelity through refined contour alignment. Benchmark experiments confirm that RDT-YOLO delivers precision of 91.9%, recall of 89.6%, and [email protected] of 94% while slashing parameter count, storage footprint, and FLOPs by 75.6%, 73.8%, and 63.8%, respectively, against YOLO11s. Comparative trials versus YOLOv10s and YOLOv12s reveal [email protected] elevations of 0.8 and 0.5 percentage points. This investigation furnishes an automated pipeline for verifying fish maw genuineness, bearing implications extending across food integrity applications.

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.

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.

The delineation of hazelnut plantations has the potential to assist land management and utilization strategies, thereby enabling the advancement of collaborative precision agriculture infrastructures. This investigation addresses the recognition of hazelnut cultivation by leveraging both optical and radar remote sensing. A comparative appraisal of various Machine Learning methodologies is conducted. The framework put forward harnesses multi-temporal information from the Sentinel-1 and Sentinel-2 archives, acquired over several years and processed in cloud computing environments. A data collection encompassing 62,982 annotated instances is furnished, with 16,561 cases assigned to the ‘hazelnut’ category and 46,421 to the ‘other’ category, gathered from 8 disparate geographic sectors within the province of Viterbo. Two distinct comparative assessments are performed: initially, a Nested 5-Fold Cross-Validation routine is used to fit, tune, and compare multiple Machine Learning classifiers confined to a single sector. In a follow-on trial, the classifiers were fitted on one sector and then validated against the remaining seven geographic sectors. The investigation confirms that AI-driven interpretation of Sentinel-1 and Sentinel-2 imagery is a viable solution for hazelnut cartography. The outcomes indicate that Random Forest delivers the most robust generalizability, achieving peak results in the second trial, with 96% accuracy and an F1 score of 91% for the ‘hazelnut’ group.

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.

Litchi, a delicate and juicy fruit with a short shelf life, has a harvest period of fewer than 14 days each year. The emergence of intelligent agriculture has spurred the integration of vision-based, automated systems for litchi collection. Nevertheless, standard techniques frequently rely on laboratory-based, high-capacity computing machinery, which raises concerns about bulk, power consumption, and real-world operability in litchi groves. To surmount these hurdles, we put forward a real-time litchi identification strategy tailored for complex settings, leveraging handheld, energy-efficient edge computing devices. To begin with, imagery from litchi orchards was compiled to strengthen data generalizability. After that, a single-stage detector, YOLOx, anchored in a convolutional neural network (CNN), was developed to pinpoint litchi fruits in captured images accurately. To ease installation on handheld, energy-miserly edge devices, channel pruning, coupled with layer pruning, was used to shrink the final model, reducing both its bulk and parameter count. In addition, knowledge distillation was applied to further polish the network. Test outcomes revealed the suggested technique achieved 97.1% compression, resulting in a tight litchi detection model occupying just 6.9 MB, while maintaining a 99 frames per second (FPS) frame rate. The technique proved 1.8 times swifter than the untreated model. Hence, our method slots effortlessly into compact, low-computation automatic harvesting apparatus, ensuring instantaneous and precise litchi spotting right inside orchard landscapes.

As global food requirements intensify and farming moves decisively toward sustainable and efficient models, establishing precise, forward-looking strategies to protect livestock health is increasingly critical. Within the egg and hatchery domain, hyperspectral imaging (HSI) has established itself as a sophisticated, non-invasive modality for swift, reliable egg quality evaluation, including the detection of embryonic death in chicks. Yet, steep costs and operational barriers relative to conventional RGB approaches present considerable barriers to HSI’s wide-scale uptake. Circumventing these constraints and enabling the broader promise of HSI, the reconstruction of hyperspectral data from ordinary RGB captures is a compelling answer. The current work focuses on producing hyperspectral imagery from RGB inputs to achieve non-destructive, early-stage identification of chick embryo mortality. An initial comparison was made among several image reconstruction frameworks—HRNET, MST++, Restormer, and EDSR—for generating hyperspectral representations of eggs at an early incubation phase. Following this, the reconstructed spectral information was used as input for XGBoost and Random Forest models to separate eggs with living embryos from those with deceased embryos. HRNET outperformed the alternatives in reconstruction quality, recording an MRAE of 0.0955, an RMSE of 0.0159, and a PSNR of 36.79 dB. This work promotes the concept that integrating imaging platforms with intelligent sensing and analytical processing can raise automation levels, strengthen biosecurity measures, and streamline resource use in pursuit of sustainable agriculture 4.0.

Remote sensing and machine learning have become prominent components of smart farming research, generating an expanding body of review literature across agricultural monitoring and prediction applications. This umbrella review synthesises review-level evidence concerning crop monitoring, disease detection, yield forecasting, and decision support while examining the methodological conditions that shape evidence strength and transferability. Peer-reviewed systematic reviews, scoping reviews, structured reviews, surveys, meta-reviews, and other eligible review-level articles were identified through searches of major academic databases and publisher platforms. Evidence was organised by application domain, sensing modality, analytical method, validation design, generalisation, uncertainty, reproducibility, and decision-support relevance. Crop-monitoring reviews describe extensive use of satellite, unmanned aerial vehicle, multispectral, hyperspectral, thermal, and multimodal data for crop mapping, growth assessment, phenotyping, stress monitoring, and related applications. Disease-detection reviews report rapid expansion of image-based machine learning and deep learning, but repeatedly identify data scarcity, class imbalance, curated-image dependence, and limited cross-context validation. Yield-forecasting reviews identify recurring integration of remote sensing, weather, soil, crop-model, and management information, although forecast horizons, spatial and temporal validation, uncertainty communication, and decision relevance remain inconsistent. Across domains, reported technical promise is moderated by heterogeneous datasets, benchmark inconsistency, review overlap, incomplete reproducibility, and limited evidence of transfer across crops, regions, seasons, and acquisition conditions. Review-level evidence supports a substantial research opportunity for remote sensing and machine learning in smart farming, but it does not independently establish field readiness, diagnostic reliability, operational forecasting performance, or deployment readiness. Stronger review reporting, external validation, transparent benchmarking, uncertainty assessment, reproducible workflows, and responsible decision-support evaluation are required.