arXiv subjects

Hasan Mirzakhaninafchi

Publications and source records attributed to Hasan Mirzakhaninafchi.

3 recordsLinked to original sources

arXiv2026

AgriJetsonBench: External-Power-Referenced TensorRT Benchmarking of Agricultural Vision Models on Jetson Edge Platforms

Agricultural vision models are often selected from validation accuracy and reported frames per second, but deployment on embedded agricultural edge-GPU systems also depends on timing boundary, numeric precision, power mode, board-input energy, and sustained-run validity. We present AgriJetsonBench, a reproducible deployment benchmark for crop/weed detection and segmentation on NVIDIA Jetson AGX Orin 64GB and Jetson Orin Nano Super. Existing locked agricultural datasets were used as fixed deployment workloads, and seven model families were exported through ONNX and TensorRT. The benchmark combines pure TensorRT engine timing, external board-input power logging, internal Jetson telemetry, battery-continuation testing, run-validity screening, and archived reproducibility artifacts. Main energy tests used YOLO11s and SegFormer-B0 at batch size 1 and $640 \times 640$. Under matched 15 W INT8 operation, Orin Nano Super outperformed AGX Orin on both workloads: YOLO11s reached 145.82 FPS and 0.0767 J/inference versus 80.90 FPS and 0.1814 J/inference, and SegFormer-B0 reached 47.18 FPS and 0.2808 J/inference versus 31.95 FPS and 0.5013 J/inference. In native SegFormer-B0 FP16 operation, AGX Orin achieved higher throughput and lower p95 latency than Nano Super, 103.61 FPS and 9.441 ms versus 67.47 FPS and 15.020 ms, with similar energy per inference. Time-aligned analysis of 76,691 internal--external power samples showed high temporal association but a pooled internal-minus-external bias of $-1.988$ W. The results show that agricultural Jetson deployment decisions should be based on workload, precision, power mode, and measurement boundary rather than board class alone.

cs.PF↗

arXiv2025

Robotic System with AI for Real Time Weed Detection, Canopy Aware Spraying, and Droplet Pattern Evaluation

Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence of herbicide resistant weeds. To address these challenges, we developed a vision guided, AI-driven variable rate sprayer system capable of detecting weed presence, estimating canopy size, and dynamically adjusting nozzle activation in real time. The system integrates lightweight YOLO11n and YOLO11n-seg deep learning models, deployed on an NVIDIA Jetson Orin Nano for onboard inference, and uses an Arduino Uno-based relay interface to control solenoid actuated nozzles based on canopy segmentation results. Indoor trials were conducted using 15 potted Hibiscus rosa sinensis plants of varying canopy sizes to simulate a range of weed patch scenarios. The YOLO11n model achieved a mean average precision (mAP@50) of 0.98, with a precision of 0.99 and a recall close to 1.0. The YOLO11n-seg segmentation model achieved a mAP@50 of 0.48, precision of 0.55, and recall of 0.52. System performance was validated using water sensitive paper, which showed an average spray coverage of 24.22% in zones where canopy was present. An upward trend in mean spray coverage from 16.22% for small canopies to 21.46% and 21.65% for medium and large canopies, respectively, demonstrated the system's capability to adjust spray output based on canopy size in real time. These results highlight the potential of combining real time deep learning with low-cost embedded hardware for selective herbicide application. Future work will focus on expanding the detection capabilities to include three common weed species in South Dakota: water hemp (Amaranthus tuberculatus), kochia (Bassia scoparia), and foxtail (Setaria spp.), followed by further validation in both indoor and field trials within soybean and corn production systems.

cs.CV↗

arXiv2025

AI-driven Web Application for Early Detection of Sudden Death Syndrome (SDS) in Soybean Leaves Using Hyperspectral Images and Genetic Algorithm

Sudden Death Syndrome (SDS), caused by Fusarium virguliforme, poses a significant threat to soybean production. This study presents an AI-driven web application for early detection of SDS on soybean leaves using hyperspectral imaging, enabling diagnosis prior to visible symptom onset. Leaf samples from healthy and inoculated plants were scanned using a portable hyperspectral imaging system (398-1011 nm), and a Genetic Algorithm was employed to select five informative wavelengths (505.4, 563.7, 712.2, 812.9, and 908.4 nm) critical for discriminating infection status. These selected bands were fed into a lightweight Convolutional Neural Network (CNN) to extract spatial-spectral features, which were subsequently classified using ten classical machine learning models. Ensemble classifiers (Random Forest, AdaBoost), Linear SVM, and Neural Net achieved the highest accuracy (>98%) and minimal error across all folds, as confirmed by confusion matrices and cross-validation metrics. Poor performance by Gaussian Process and QDA highlighted their unsuitability for this dataset. The trained models were deployed within a web application that enables users to upload hyperspectral leaf images, visualize spectral profiles, and receive real-time classification results. This system supports rapid and accessible plant disease diagnostics, contributing to precision agriculture practices. Future work will expand the training dataset to encompass diverse genotypes, field conditions, and disease stages, and will extend the system for multiclass disease classification and broader crop applicability.

cs.CV↗