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Obtaining body mass in large-scale rearing and production environments is difficult, making non-contact assessment methods indispensable. This paper introduces an original pipeline for predicting pig body mass based on an adapted mask region convolutional neural network (mask R-CNN). The adaptation deploys ResNeSt as the backbone feature-extraction architecture, aiming to bolster the capacity to derive features from imagery. A feature pyramid network (FPN) was appended to this backbone to enable feature merging across multiple scales. Within the region proposal network (RPN), a channel attention mechanism (CAM) was installed alongside a spatial attention mechanism (SAM), enabling the dynamic combination of localized characteristics with their long-range interdependencies. This configuration captures holistic contextual knowledge, ultimately improving segmentation fidelity. The adapted architecture achieved precision (P) of 90.33%, recall (R) of 89.85%, and mean average precision (MAP) of 95.21%, thereby isolating pig areas in images with notable effectiveness. Five descriptive features drawn from the imagery were examined: back area, body length, body width, average depth, and eccentricity. Leveraging the pig depth images, five regression learning algorithms—ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)—were trained to forecast mass values. AdaBoost delivered the strongest predictive performance, evidenced by a coefficient of determination (R2) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg², and a mean absolute percentage error (MAPE) of 8.45%. The experimental results confirm that machine learning models can reliably predict pig mass, providing a technical foundation for the informed management of pig production facilities.

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.

Remarkable progress has been made in applying deep learning models to distinguish between weed and crop species within agricultural settings. Still, a significant gap persists in the body of work on how models are trained and evaluated when environmental conditions diverge. The dominant tendency involves either dividing a single dataset distribution into training, validation, and test splits or first pooling datasets originating from varied conditions or distributions and then partitioning them. What our study brings forward is an effort to close this gap: we incorporate multiple broadleaf weed categories spanning dissimilar distributions, probe what happens when convolutional neural networks are trained exclusively on condition- or distribution-specific data, and then gauge their behaviour in entirely foreign environments across three experimental setups. By surveying an array of network designs alongside distinct learning modes (fine-tuning versus feature extraction), testing multiple architectures, adopting varied training protocols, and consolidating data, we arrived at straightforward guiding principles that ensure models retain precision and accuracy even under starkly contrasting deployment conditions. Experiment 1, confined to a single uniform environment, produced accuracies of 80%–100% across models and strategies; fine-tuning yielded higher accuracies (94%–99.9%) than feature extraction (80%–92.96%). Experiment 2 showed a steep drop-off in performance: whenever the test partition came from sources other than the training and validation sets, accuracy ranged from 25% to 60%, most often around 40%. Experiment 3, centred on merging datasets and distributions, delivered encouraging numbers — ResNet in fine-tuning mode attained a high of 99.6%, whereas InceptionV3 in feature extraction mode dropped to a low of 69.9%. These core insights underscore that fusing data from mismatched distributions, paired with fine-tuning on modern backbones like ResNet and MobileNet, markedly improves capability, unlike the comparatively modest outputs of leaner architectures such as AlexNet. Our results suggest that accepting data heterogeneity and flexible training regimens are fundamental to sharpening weed classification pipelines. The study provides a workable approach for handling diverse datasets that reflect genuine agricultural variability.

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.

The surveillance of poultry movement patterns is an essential diagnostic tool for welfare evaluation, yet manual monitoring routines impose considerable temporal demands and physical strain, particularly in high-density flocks. Inadequately trained personnel may additionally fail to recognize subtle shifts in behavioral intensity, thereby overlooking critical windows for prophylactic intervention and remedial action. The present work merges conventional camera-based observation with a sophisticated automated recognition architecture to classify diverse broiler conduct within commercial housing. Deployment of a two-stage deep learning pipeline occurs as follows: initial detection localizes individual birds, followed by identification of five principal anatomical landmarks (cranial region, ventral region, bilateral limbs, and caudal appendage). From these points, a skeletal framework anchored in the ventral region is generated, yielding six angular measurements via trigonometric computations. A long short-term memory network subsequently processes these angular sequences, estimating the likelihood across behavioral categories encompassing “Standing”, “Walking”, “Resting”, “Eating”, “Preening”, and “Flapping”, with the highest-probability classification retained. To quantify the temporal distribution between stationary and dynamic states, a cascaded fuzzy-logic inference architecture was implemented, providing a resilient assessment of overall activity magnitude. Validation conducted within a mixed-gender rearing facility demonstrated that the proposed methodology attained performance floors of 85.2% for broiler categorization, 79.2% for anatomical landmark localization, and 50.8% for behavioral classification accuracy. Activity-level determinations generated by the system were consistent with evaluations by veteran poultry husbandry specialists.

A large segment of India’s rural population depends on agriculture for its livelihood, yet difficulties with manual processes and disease management persist. To address these problems, this study presents an automated framework for detecting leaf damage and identifying plant diseases using advanced deep learning methods. The introduced technique unfolds in six steps: first, YOLOv8 is harnessed to pinpoint regions of interest from drone-acquired images; second, DeepLabV3+ is applied for background stripping to aid disease categorization; third, a CNN model is deployed for reliable disease sorting, registering strong training and validation accuracy figures (96.97% and 92.89%, respectively); fourth, UNet-based semantic segmentation is utilized for meticulous pixel-scale damage identification, with an assessment accuracy of 99%; fifth, disease severity is gauged; and sixth, customized remedial measures are put forward according to the disease category and the extent of damage. Experimental trials using the Plant Village dataset confirm the technique’s ability to detect various defects across crops, including apple, tomato, and corn. This mechanized strategy carries significant potential to boost agricultural output and strengthen disease management both within India and globally.