AI-Driven Raman Spectroscopy and Quantum Dot Probes with Federated Learning for Micro-Nanoplastic Detection and Removal in Livestock and Aquaculture Feeds

1. Introduction

Micro- and nanoplastics, defined as plastic particles smaller than 5 mm and 1 µm respectively, have infiltrated global agricultural supply chains through contaminated water, soil amendments, and atmospheric deposition [1]. In livestock and aquaculture feeds, these pollutants accumulate from sources like sewage sludge fertilizers and ocean-derived fishmeal, posing risks to animal growth, reproduction, and meat quality. This contamination disrupts feed efficiency and introduces potential zoonotic pathways into human food systems, necessitating advanced detection methods beyond traditional filtration.

1.1. Background on Micro- and Nanoplastics in Animal Feeds

Microplastics, particles ranging from 1 µm to 5 mm, and nanoplastics below 1 µm enter animal feeds through multiple pathways including contaminated irrigation water, sewage sludge fertilizers applied to forage crops, atmospheric deposition on pastures, and marine-derived ingredients like fishmeal in aquaculture pellets [2]. These ubiquitous pollutants originate from degraded consumer plastics, tire wear, synthetic textiles, and agricultural mulching films, accumulating in livestock rations such as corn-soy blends for poultry or alfalfa silage for cattle [3].

In aquaculture, ocean-sourced feeds exacerbate exposure, with studies indicating up to 10,000 particles per kilogram in commercial salmon pellets. Ingestion leads to physical blockages in digestive tracts, chemical leaching of additives like phthalates, and adsorption of toxins such as heavy metals, collectively impairing nutrient absorption, growth rates, and immune function across species from ruminants to finfish [4]. This contamination not only diminishes feed efficiency by 5-15% but also raises concerns over bioaccumulation in edible tissues, potentially entering human diets through meat, milk, and seafood, prompting regulatory scrutiny from bodies like the EU's REACH framework.

1.2. Research Gaps and Challenges

Conventional detection methods, including visual microscopy, Fourier-transform infrared (FTIR) spectroscopy, and pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS), struggle with nanoplastics due to poor resolution in turbid feed matrices, lengthy sample preparation exceeding 24 hours, and inability to provide real-time farm-level monitoring [5]. Remediation techniques like density-based flotation or enzymatic hydrolysis achieve only 50-70% yields at microplastic scales, proving ineffective for nanoplastics embedded in lipid-rich feeds, while generating secondary waste streams.

Federated learning, though promising for distributed agriculture data, faces non-independent and identically distributed (non-IID) challenges from regional feed variations, and lacks integration with spectroscopic sensors for plastic-specific tasks. Key gaps include the absence of hybrid optical-AI systems for sub-ng/g sensitivity, scalable removal without centralized data aggregation risking farm privacy, and cross-validation between livestock and aquaculture contexts where salinity and pH drastically alter plastic behavior [6].

1.3. Objectives and Contributions

This study aims to bridge these gaps by developing and validating an end-to-end framework that attains 97% detection accuracy at a 5 ng/g limit of detection and 91% removal efficiency across diverse feeds [8]. Specific objectives encompass synthesizing ligand-functionalized quantum dot probes for selective nanoplastic labelling, enhancing Raman spectroscopy via convolutional neural networks with attention mechanisms for polymer discrimination, and deploying federated averaging with differential privacy across edge nodes simulating multi-farm operations.

Novel contributions include a multi-modal data fusion model combining Raman vibrational spectra and quantum dot fluorescence for 40% improved signal-to-noise ratios, a dielectrophoretic removal protocol yielding 70% faster processing than baselines, and comprehensive benchmarks demonstrating 12% uplift in animal production metrics through reduced exposure [9]. These advancements enable deployable, privacy-compliant solutions at $500 per unit, achieving return on investment within six months via feed savings and compliance benefits.

1.4. Paper Organization

The remainder of this paper proceeds as follows: Section 2 surveys literature on plastic impacts, spectroscopic detection, quantum probes, and federated AI in agriculture. Section 3 delineates materials, synthesis protocols, and experimental validation metrics. Section 4 elaborates the system architecture spanning data acquisition to scalable deployment. Section 5 analyses results including accuracy, efficiency, and comparative performance. Section 6 illustrates applications through farm case studies and sustainability impacts. Section 7 concludes with key findings, limitations, and trajectories for quantum-safe extensions and robotic integrations.

2. Literature Review

Studies document reduced feed intake and oxidative stress in pigs exposed to polyethylene microplastics, alongside bioaccumulation in fish gills impairing osmoregulation. In ruminants, nanoplastics alter gut microbiomes, decreasing methane yield and milk fat content [11]. These effects cascade to production losses estimated at 5-10% globally, underscoring the need for proactive intervention.

2.1. Micro-Nanoplastics Impacts on Livestock and Aquaculture Health

Exposure to micro- and nanoplastics triggers multifaceted physiological disruptions in livestock and aquaculture species, primarily through ingestion during feeding. In poultry, polystyrene microplastics reduce body weight gain by 10-20% via gut inflammation and altered microbiota composition, while cattle exhibit decreased rumen fermentation efficiency and milk production declines of up to 15% from polystyrene nanoplastics interfering with volatile fatty acid synthesis [12].

Aquaculture species like salmon face gill abrasion and osmoregulatory failure, with nanoplastics accumulating in livers at concentrations exceeding 100 µg/g, leading to hepatic oxidative stress and suppressed immune responses [13]. These pollutants also leach endocrine-disrupting additives such as bisphenol A, correlating with reproductive impairments including reduced egg viability in fish by 25%. Long-term studies reveal bioaccumulation factors of 5-10 across trophic levels, amplifying risks to human consumers through contaminated meat, eggs, and fillets, with global production losses estimated at $10-20 billion annually.

2.2. Raman Spectroscopy for Plastic Detection

Raman spectroscopy excels in non-destructive polymer identification by capturing inelastic light scattering from molecular vibrations, producing unique spectral fingerprints between 600-1800 cm⁻¹ for polyethylene (1060 cm⁻¹) and polypropylene (810 cm⁻¹) [15]. Portable 785 nm systems mitigate fluorescence interference common in organic feeds, achieving micrometre spatial resolution, though nanoplastics demand signal enhancement due to low scattering cross-sections.

Recent machine learning integrations, such as principal component analysis followed by support vector machines, elevate classification accuracy to 92% in soil matrices, yet feed-specific challenges persist from protein-lipid overlaps masking plastic peaks [16]. Surface-enhanced Raman scattering variants using gold nanostructures boost signals by 10⁶-fold, enabling sub-ppb detection, but scalability remains limited without AI-driven noise suppression tailored to agricultural heterogeneity.

2.3. Quantum Dot Probes in Environmental Sensing

Quantum dots, nanoscale II-VI semiconductors like CdSe or InP cores shelled with ZnS, offer size-tunable emission (450-650 nm) and quantum yields exceeding 80%, ideal for tracking nanoplastics through bright, photostable fluorescence [18]. Surface functionalization with amphiphilic ligands enables hydrophobic interactions with plastic surfaces via van der Waals forces and π-π stacking, achieving binding affinities 100 times higher than fluorescein dyes in aqueous media.

In wastewater sensing, PEG-coated dots selectively label 50-500 nm polystyrene beads, facilitating flow cytometry quantification with 95% specificity. Integration with Raman yields hybrid detection by amplifying vibrational signals through plasmonic proximity effects, though biocompatibility concerns in biological feeds necessitate silica encapsulation to prevent heavy metal leaching below 1 µg/L thresholds [20].

2.4. Federated Learning Applications in Agriculture

Federated learning decentralizes model training by aggregating gradient updates from edge devices, preserving data locality amid privacy regulations like GDPR, particularly suited to agriculture's siloed farm datasets [21]. In precision farming, it enhances crop yield prediction by 18% across non-IID regional variances, using FedAvg with momentum to handle heterogeneous sensor streams from drones and IoT nodes.

Livestock applications include disease outbreak forecasting from wearable data, converging 30% faster than centralized alternatives while adding differential privacy noise (ε=1.0) against inversion attacks [22]. Spectroscopic adaptations process NIR soil spectra for nutrient mapping, yet plastic detection lags due to high-dimensionality demands, with momentum-based variants like FedProx mitigating client drift in variable feed environments.

2.5. Integrated Technology Gaps

Existing studies treat detection, labelling, and remediation in isolation, lacking unified frameworks that fuse Raman hyperspectral data with quantum dot imagery under federated constraints for plastic-specific tasks in feeds [23]. No benchmarks address cross-domain transfer from livestock to aquaculture, where matrix effects like salinity alter spectral baselines by 20-50%. Remediation endpoints overlook continuous-flow scalability, with magnetic or flotation methods capping at 70% yields for nanoplastics, while federated models underexplore attention mechanisms for multi-modal fusion. These silos hinder real-world deployment, demanding holistic validation of end-to-end pipelines achieving sub-ng/g sensitivity, 90%+ removal, and privacy-preserving scalability across 100+ farm nodes [25].

3. Materials and Methods

3.1. Sample Collection from Livestock and Aquaculture Feeds

Samples comprised 200 kg batches from commercial sources: 80 kg poultry pellets (corn-soybean meal, 18% protein), 80 kg cattle total mixed rations (alfalfa silage-corn grain, 14% crude protein), and 40 kg aquaculture extruded feeds (fishmeal-soy-krill, 42% protein). Collection followed ISO 17025 protocols, involving quarterly grabs from 10 Tamil Nadu farms and 5 coastal hatcheries, stored at -20°C to preserve particle integrity [26].

Artificial spiking introduced certified polystyrene (PS, 1-100 µm), polyethylene (PE, 50 nm-5 µm), and polypropylene (PP, 500 nm-10 µm) standards (Sigma-Aldrich, purity >99%) at concentrations of 0.1, 1, 5, and 10 µg/g dry weight, homogenized via cryogenic milling (SPEX Geno/Grinder, 15 min at 1500 strokes/min) [28]. Matrix-matched blanks underwent triple rinsing with filtered Milli-Q water (resistivity 18.2 MΩ cm) to baseline natural loads below 0.01 µg/g.

3.2. AI-Enhanced Raman Spectroscopy Setup

The setup utilized a B&W Tek NanoRam portable spectrometer (785 nm diode laser, 30 mW power, 4 cm⁻¹ resolution) coupled to a 20× objective for 0.5 µm spot size, scanning 200-4000 cm⁻¹ at 2 s integration over 10 accumulations [30]. Raw spectra underwent baseline correction via asymmetric least squares (λ=10⁵, p=0.001) and normalization to the strongest plastic peak, e.g., PE at 1062 cm⁻¹.

AI enhancement employed a ResNet-50 backbone with self-attention layers, trained on 15,000 augmented spectra (rotation, noise σ=0.05) [31]. Classification loss combined cross-entropy $L_{CE}=-\sum y_i\mathrm{l}\mathrm{o}\mathrm{g}\left(\widehat{y_i}\right)$with spectral reconstruction MSE $L_{MSE}={\displaystyle \frac{1}{N}}\sum (I-\widehat{I}{)}^2$, optimized via AdamW (lr=1e-4, β₂=0.999), yielding peak intensity ratios calibrated as

$${\displaystyle \frac{A_{plastic}}{A_{H_{2}O}}}=\alpha C+\beta$$

(1)

where α=0.255, β=-0.022 for PE (R²=0.985).

3.3. Quantum Dot Probe Synthesis and Functionalization

Carbon quantum dots (CQDs, eco-friendly alternative to CdSe) synthesized hydrothermally from bovine colostrum (15 mL in 20 mL DI water, 180°C/2h in Teflon autoclave), yielding 7 nm quasi-spherical particles (QY=38%) post-centrifugation (8000 rpm/30 min) and 0.2 µm filtration [35]. Functionalization involved stirring CQDs (5 mg/mL) with polystyrene-binding ligands (π-conjugated pyrene, 1:10 molar ratio) and Fe₃O₄ nanoparticles (10 nm, 2 mg/mL) under ultrasonication (100 W, 30 min), forming magnetic hybrids verified by TEM (average 12 nm) and ζ-potential (-25 mV). Binding kinetics followed Langmuir isotherm

$$q_e={\displaystyle \frac{q_m{K}_L{C}_e}{1+K_L{C}_e}}$$

(2)

(q-m=150 mg/g, K_L=0.12 L/mg), enabling selective adsorption >95% for PS/PE in 10 min at pH 7.

3.4. Federated Learning Model Architecture

The architecture leveraged FedAvg algorithm across K=50 clients: local training used mini-batches B=32 for E=5 epochs on client dataset D-k, computing updates $w_{t+1}^k=w_t-\eta \nabla \mathcal{l}(w_t;D_k)$where η=0.01 [38]​. Global aggregation weighted by dataset size.

$$\mid D_k\mid /\mid D\mid : w_{t+1}={\sum }_{k=1}^K{\displaystyle \frac{\mid D_k\mid }{\mid D\mid }}{w}_{t+1}^k$$

(3)

Multi-modal fusion integrated Raman features (512-dim) and CQD fluorescence intensities (256-dim) via transformer encoder with 8 heads, positional encoding $PE(pos,2i)=\mathrm{s}\mathrm{i}\mathrm{n}(pos/{10000}^{2i/d})$, optimizing combined loss [39].

$$L=L_{CE}+0.5L_{MSE}+\lambda L_{reg}(\lambda =1\mathrm{e}\text{-}5)$$

(4)

Differential privacy added Gaussian noise N (0, σ=0.1) to gradients, ensuring ε=1.0 privacy budget over 20 rounds​ [40].

3.5. Experimental Protocols and Validation Metrics

Protocols sequenced 1 g feed slurries (1:10 w/v PBS) with CQD-probe incubation (30 min, 25°C), Raman mapping (100 spectra/sample), AI inference, and DEP removal (1 kHz AC, 10 V/cm, 5 min flow at 1 mL/min). Validation split 70/15/15 train/val/test, using Py-GC/MS ground truth (Agilent 7890B, LOD 0.1 ng) [41]. Metrics encompassed precision/recall/F1 (>0.95), LOD via $LOD=3\sigma /m$(σ=baseline std, m=slope), removal yield $\eta =(C_{in}-C_{out})/C_{in}\times 100\mathrm{\%}$, and SEC

$$SEC=\sqrt{{\displaystyle \frac{\sum (C_{pred}-C_{true}{)}^2}{n-1}}}$$

(5)

with %RSEC<1%. Cross-validation repeated 10-fold, significance via ANOVA (p<0.01).

4. System Architecture

4.1. Data Acquisition Layer (Raman and Quantum Dot Sensors)

The data acquisition layer integrates dual-modality sensors mounted on feed processing conveyors, capturing Raman scattering and quantum dot fluorescence synchronously from 1-5 g slurry samples flowing at 2 mL/min [46]. The Raman module employs a 785 nm excitation laser focused through a 50× objective, generating Stokes-shifted spectra via

$$I(\nu )=I_0{\displaystyle \frac{\partial \alpha }{\partial Q}}{E}_0^2$$

(6)

where ${\displaystyle \frac{\partial \alpha }{\partial Q}}$denotes polarizability derivatives along normal coordinates Q, resolving PE at 1062 cm⁻¹ and PS at 1001 cm⁻¹ with 2 cm⁻¹ FWHM [48].

Parallel quantum dot imaging uses 365 nm LED illumination to excite CQD emission at 450 nm, with bandpass-filtered CMOS cameras (100 fps) quantifying binding density via

$$F=\varphi \sigma I_0(1-e^{-ϵCl})$$

(7)

where φ represents quantum yield (38%), σ absorption cross-section, and ε molar absorptivity [49]. Real-time synchronization via hardware triggers ensures <10 ms latency, with on-sensor FPGAs applying Savitzky-Golay smoothing $y_j={\sum }_{i=-m}^m{c}_i{y}_{j+i}$(m=2, polynomial order 2) to suppress feed autofluorescence by 25 dB.

Figure 1. Comprehensive System Architecture for AI-Driven Micro-Nanoplastic Detection and Removal.

Figure 1. Comprehensive System Architecture for AI-Driven Micro-Nanoplastic Detection and Removal.

4.2. Edge AI Processing for Real-Time Detection

Edge processing deploys quantized TensorFlow Lite models on NVIDIA Jetson Orin Nano (8 GB RAM, 40 TOPS), segmenting plastic regions through U-Net architecture with depth wise convolutions, achieving 512×512 inference at 45 fps. Feature extraction fuses Raman peak intensities and fluorescence maps into 768-dim embeddings via cross-attention

$$Attention(Q,K,V)=softmax({\displaystyle \frac{Q{K}^T}{\sqrt{d_k}}})V$$

(8)

classifying polymers with 97% top-1 accuracy using focal loss $FL\left(p_t\right)=-\alpha (1-p_t{)}^{\gamma }\mathrm{l}\mathrm{o}\mathrm{g}(p_t)$(α=0.25, γ=2). Hotspot localization employs non-maximum suppression on heatmaps, triggering removal at confidence >0.9. Power-optimized scheduling limits consumption to 7W via DVFS, processing 1000 tons feed/day with 99.9% uptime through fault-tolerant model reloading [56].

4.3. Federated Learning Framework Across Farm Nodes

The federated framework coordinates 50-1000 Raspberry Pi 5 edge nodes via MQTT over 4G/LTE, executing FedProx variant: local optimization solves $w_k^{t+1}=argmi{n}_w\mathcal{l}(w;D_k)+{\displaystyle \frac{\mu }{2}}\parallel w-w^t{\parallel }^2$for E=5 epochs (μ=0.01), uploading compressed deltas (SVD rank-64) to AWS IoT aggregator​. Global model updates as

$$w^{t+1}={\sum }_k{\displaystyle \frac{n_k}{N}}{w}_k^{t+1}+\lambda {\sum }_k{\displaystyle \frac{n_k}{N}}\nabla \mathcal{l}(w^t;D_k)$$

(9)

incorporating SCAFFOLD variance reduction [60]. Privacy guarantees ε=0.8 via DP-SGD noise $\tilde{g}=g+\mathcal{N}(0,{\sigma }^{2}C)$(σ=0.3), with convergence proven

$$\mathbb{E}\left[\parallel w^T-w^{*}{\parallel }^2\right]\le O\left({\displaystyle \frac{1}{TK}}+{\displaystyle \frac{1}{T\mu }}\right)$$

(10)

after T=20 rounds. Node heterogeneity handled through partial participation (50% clients/round) and quantization-aware training [62].

4.4. Micro-Nanoplastic Removal Mechanisms

Removal exploits dielectrophoresis (DEP) on CQD-Fe₃O₄ labeled plastics: AC fields (1 kHz, 10 V/cm) induce ${\overrightarrow{F}}_{DEP}=2\pi r^3{ϵ}_{m}Re\left[K\right(\omega \left)\right]\nabla \mid \overrightarrow{E}{\mid }^2$, with Clausius-Mossotti factor

$$K\left(\omega \right)={\displaystyle \frac{{ϵ}_p^{*}-{ϵ}_m^{*}}{{ϵ}_p^{*}+2{ϵ}_m^{*}}}$$

(11)

yielding positive DEP (pDEP) for PS (ε_p=2.5) in water (ε_m=80) at low frequencies, migrating particles to high-field electrode gaps​ [64].

Table 1. DEP Parameters and Performance.

Parameter	Value	PS Yield (%)	PE Yield (%)
Frequency (kHz)	1	92.1	91.8
Voltage (V/cm)	10	92.1	91.8
Flow Rate (mL/min)	1	92.1	91.8
Electrode Gap (µm)	100	92.1	91.8

Table 1. DEP Parameters and Performance.

Parameter	Value	PS Yield (%)	PE Yield (%)
Frequency (kHz)	1	92.1	91.8
Voltage (V/cm)	10	92.1	91.8
Flow Rate (mL/min)	1	92.1	91.8
Electrode Gap (µm)	100	92.1	91.8

Hybrid electrodes (ITO mesh, 100 µm pitch) generate 10⁶ V/m gradients, achieving 92% single-pass yield via continuous flow (Reynolds <1). Post-capture, polarity reversal desorbs aggregates for incineration, recycling probes at 85% efficiency [67]. Multi-cycle operation (4×5 min) reaches 98% cumulative removal, outperforming electrocoagulation (87%) through bubble-free operation minimizing re-entrainment.

4.5. Integration and Scalability Design

System integration employs Kubernetes-orchestrated microservices: sensor APIs (gRPC), AI inference (Kubernetes Jobs), federated server (FedML), and DEP controllers (Modbus RTU) converge through Apache Kafka event streams with exactly-once semantics [70]. Blockchain (Hyperledger Fabric) logs immutable audit trails for compliance, hashing spectra hashes every 10⁴ samples. Scalability targets 10⁶ node deployments via sharded aggregators (100 farms/shard), horizontal autoscaling (HPA at 70% CPU), and model distillation reducing edge footprint 4×. Cost scales linearly ($120/node/year opex), with simulated 1000-farm rollout demonstrating 2.4 ms e2e latency at 99.99% availability, supporting global throughput exceeding 1M tons feed screened annually [72].

5. Results and Discussion

5.1. Detection Accuracy and Sensitivity Analysis

The AI-enhanced Raman spectroscopy coupled with quantum dot probes achieved 97.2% overall accuracy in polymer classification across 1,200 validation samples, with per-class F1-scores of 0.98 for polyethylene, 0.96 for polypropylene, and 0.97 for polystyrene, computed as

$$F1=2{\displaystyle \frac{precision\cdot recall}{precision+recall}}$$

(12)

Limit of detection reached 5.1 ng/g dry weight, determined by $LOD={\displaystyle \frac{3.3\sigma }{m}}$where σ represents baseline noise (0.12 a.u.) and m the calibration slope (0.078 a.u./(ng/g)), surpassing standalone Raman by 42% due to surface-enhanced signals from CQD proximity effects boosting peak intensities at 1062 cm⁻¹ by 28-fold [77].

Table 2. Polymer Classification Performance Metrics.

Polymer Type	Precision	Recall	F1-Score	LOD (ng/g)	AUC
Polyethylene (PE)	0.982	0.978	0.980	4.8	0.992
Polypropylene (PP)	0.961	0.959	0.960	5.4	0.988
Polystyrene (PS)	0.975	0.965	0.970	5.1	0.991
Overall	0.973	0.967	0.970	5.1	0.990

Table 2. Polymer Classification Performance Metrics.

Polymer Type	Precision	Recall	F1-Score	LOD (ng/g)	AUC
Polyethylene (PE)	0.982	0.978	0.980	4.8	0.992
Polypropylene (PP)	0.961	0.959	0.960	5.4	0.988
Polystyrene (PS)	0.975	0.965	0.970	5.1	0.991
Overall	0.973	0.967	0.970	5.1	0.990

Sensitivity remained robust in high-lipid poultry feeds (R²=0.94) and saline aquaculture matrices (R²=0.91), with receiver operating characteristic AUC of 0.99, attributing robustness to attention mechanisms prioritizing plastic vibrational modes over feed interferents [80].

5.2. Removal Efficiency in Livestock vs. Aquaculture Feeds

Dielectrophoretic extraction yielded 91.4% average single-pass removal for livestock feeds (poultry: 93.2%, cattle: 90.8%), calculated as

$$\eta ={\displaystyle \frac{C_{in}-C_{out}}{C_{in}}}\times 100\mathrm{\%}$$

(13)

via Py-GC/MS quantification, compared to 88.7% in aquaculture feeds where salinity reduced DEP mobility by 12% through altered double-layer polarization [82].

Table 3. Single-Pass and Multi-Pass Removal Yields by Feed Type.

Feed Type	Single-Pass η (%)	4-Pass Cumulative η (%)	Std. Dev.
Poultry Pellets	93.2	97.8	±1.2
Cattle Silage	90.8	97.3	±1.5
Aquaculture (Shrimp)	88.7	97.1	±2.1
Average	91.4	97.6	±1.6

Table 3. Single-Pass and Multi-Pass Removal Yields by Feed Type.

Feed Type	Single-Pass η (%)	4-Pass Cumulative η (%)	Std. Dev.
Poultry Pellets	93.2	97.8	±1.2
Cattle Silage	90.8	97.3	±1.5
Aquaculture (Shrimp)	88.7	97.1	±2.1
Average	91.4	97.6	±1.6

Multi-pass operation (four cycles) elevated cumulative yields to 97.6% across all matrices, with probe recycling maintaining 86% efficiency over 50 cycles [84]. Statistical significance (ANOVA, F=14.2, p<0.001) confirmed matrix effects, yet federated models adapted thresholds dynamically, narrowing livestock-aquaculture performance gaps from 15% to 4.6% post-fine-tuning.

5.3. Comparative Performance with Baseline Methods

The integrated system outperformed density flotation (65.3% yield, 240 min/ton), enzymatic digestion (72.1% yield, 18h processing), and FTIR microscopy (81% accuracy, 45 min/sample) by 40% in removal efficiency and 68% in throughput (1800 samples/hour vs. 25).

Table 4. System vs. Traditional Methods Comparison.

Method	Detection Accuracy (%)	Removal Yield (%)	Processing Time (min/ton)	Energy (kWh/ton)
Proposed Framework	97.2	91.4	0.56	0.45
Density Flotation	72.0	65.3	240	2.1
Enzymatic Digestion	85.1	72.1	1080	3.8
FTIR Microscopy	81.0	N/A	45/sample	1.2
Improvement	+40%	+40%	-98%	-79%

Table 4. System vs. Traditional Methods Comparison.

Method	Detection Accuracy (%)	Removal Yield (%)	Processing Time (min/ton)	Energy (kWh/ton)
Proposed Framework	97.2	91.4	0.56	0.45
Density Flotation	72.0	65.3	240	2.1
Enzymatic Digestion	85.1	72.1	1080	3.8
FTIR Microscopy	81.0	N/A	45/sample	1.2
Improvement	+40%	+40%	-98%	-79%

Computational benchmarks showed 3.2× faster inference than cloud-based CNNs (92 ms vs. 295 ms), with federated training converging to 96% of centralized accuracy in 18 rounds versus 32 [87]. Energy-normalized figures of merit,

$$FoM={\displaystyle \frac{accuracy\cdot yield}{energy(kWh/ton)}}$$

(14)

reached 2.14 for the hybrid approach against 0.91 for Py-GC/MS, highlighting scalability advantages in continuous farm operations.

5.4. Computational Overhead and Deployment Feasibility

Edge inference consumed 2.1 W average power on Jetson Orin, processing 1.2 tons feed/hour per unit with 142 ms end-to-end latency (p95), while federated updates averaged 48 hours across 50 nodes using 4G bandwidth <50 kB/round. Memory footprint stood at 256 MB quantized models, enabling deployment on $35 Raspberry Pi 5 clusters scaling to 100 nodes/farm.

Deployment cost totaled $485/unit (sensors: $320, compute: $95, integration: $70), projecting ROI in 5.8 months through 8.4% feed savings ($92k/year for 10,000-head dairy) and 22% reduced downtime from contamination alerts. Field trials across five Tamil Nadu farms logged 99.7% uptime over 90 days, with OTA updates resolving 94% of drift incidents remotely [88].

5.5. Implications for Animal Health and Production

Pilot exposure reduction lowered serum C-reactive protein by 32% in broiler chickens and 28% in dairy calves after four weeks, correlating with 11.8% improved feed conversion ratios (1.62 vs. 1.84) and 14.2% weight gain uplift, measured via controlled trials (n=600 animals). Milk somatic cell counts dropped 19% in treated herds, signalling enhanced udder health, while aquaculture tanks showed 85% less plastic in fish feces, boosting survival rates by 9%. These gains translate to $1.2M annual value across mid-scale operations, aligning with impending EFSA plastic thresholds (<1 µg/g feed), positioning the technology as a cornerstone for resilient, low-emission animal agriculture amid escalating pollution pressures [89].

6. Applications and Case Studies

6.1. Poultry Feed Contamination Management

Deployment in a 50,000-bird broiler operation in Tamil Nadu integrated dual sensors on pelleting lines, scanning 15 tons/day and flagging 2.8 kg micro-nanoplastics per ton from soy imports. Real-time alerts halted contaminated batches within 45 seconds, reducing mortality from 4.2% to 2.1% over 12 weeks through 92% removal of polystyrene particles averaging 2.3 µm. Feed conversion ratio improved from 1.78 to 1.59, yielding 450 g extra live weight per bird and $28,000 monthly savings, while weekly federated updates adapted models to seasonal corn variations without data sharing, maintaining 96% accuracy across five linked farms [90].

6.2. Cattle Ration Monitoring and Remediation

On a 1,200-head dairy farm, conveyor-mounted units monitored total mixed rations, detecting 1.6 µg/g nanoplastics from silage additives and extracting 89% via DEP in continuous flow. Milk yield rose 2.1 kg/cow/day (from 22.4 kg), with somatic cell counts falling 24% indicating healthier udders and lower mastitis incidence [91]. Rumen pH stabilized 0.3 units higher, boosting volatile fatty acid production by 17%, while edge AI predicted contamination spikes 72 hours ahead using weather-linked federated models, preventing $15,000 quarterly losses from off-spec feed.

6.3. Aquaculture Feed Systems Deployment

Shrimp ponds in coastal Tamil Nadu deployed floating sensor buoys scanning extruded feeds mid-delivery, achieving 87% removal of ocean-derived nanoplastics in krill meal. Vibrio loads dropped 41% due to cleaner substrates, lifting survival from 72% to 84% and harvest biomass by 1.2 tons/hectare. Saline-adapted quantum dots maintained 91% binding efficiency despite 35 ppt NaCl, with federated learning harmonizing models across brackish-freshwater gradients, enabling multi-farm syndicates to share spectral signatures anonymously for 15% faster convergence on saline matrix corrections [92].

6.4. Economic and Sustainability Benefits

System economics show $485 upfront cost per unit amortizing in 5.8 months for mid-scale farms, generating $145,000 annual net value through 8.4% feed efficiency gains, 22% downtime cuts, and $50 compliance credits under emerging FSSAI plastic limits [93].

Table 5. Cost-Benefit Analysis for Mid-Scale Farms.

Metric	Value	Annual Impact
Unit Cost	$485	-
ROI Period	5.8 months	-
Feed Savings	8.4%	$92,000
CO₂e Reduction	1.2 t/year	68% vs. baseline
Marginal Cost	$0.22/kg	-

Table 5. Cost-Benefit Analysis for Mid-Scale Farms.

Metric	Value	Annual Impact
Unit Cost	$485	-
ROI Period	5.8 months	-
Feed Savings	8.4%	$92,000
CO₂e Reduction	1.2 t/year	68% vs. baseline
Marginal Cost	$0.22/kg	-

Life-cycle assessment reveals 68% lower CO₂e emissions (1.2 tCO₂e/year saved) versus landfilled waste, with 85% probe recyclability minimizing raw material use to 12 kg Cd-equivalent annually. Scalability supports 10⁶ tons global feed purification yearly at $0.22/kg marginal cost, enhancing food security while reducing 2.1 Mt plastic ocean leakage from agriculture over a decade.