From theory to deployed systems.

Abstract

Precision agriculture demands real-time, in-situ pathological screening without human fatigue. We developed a solar-powered, autonomous mobile robot capable of navigating agricultural rows, capturing high-resolution plant imagery, running localized deep-learning disease classification models on the edge, and streaming continuous environmental telemetry (soil moisture, temperature, ambient humidity) to a synchronized cloud dashboard with automated alert triggers.

Key Technical Contributions

• ◆Engineered autonomous row navigation and solar energy harvesting system for prolonged field deployment.
• ◆Integrated edge deep learning vision models for leaf pathology detection with instant on-device inference.
• ◆Synchronized real-time environmental telemetry (soil, temperature, humidity) with cloud alerts to dispatch preventative actions before disease propagation.

Abstract

Abstractive summarization in Arabic is historically hindered by morphological complexity, rich root-pattern systems, and dialetical variations. In this study, we fine-tuned Multilingual Text-to-Text Transfer Transformer (mT5) architectures on diverse Arabic textual corpora, implementing customized sub-word tokenization and length penalty schedules. Systematic evaluation demonstrated substantial improvements in ROUGE-1, ROUGE-2, and ROUGE-L metrics over traditional sequence-to-sequence recurrent and transformer baselines.

Key Technical Contributions

• ◆Fine-tuned pre-trained Multilingual T5 (mT5) models specifically optimized for complex Arabic grammatical syntax.
• ◆Evaluated generation quality on multiple benchmark Arabic datasets using standardized ROUGE-1/2/L and human coherence assessments.
• ◆Demonstrated statistically significant gains in semantic fidelity and fluency compared to standard seq2seq models.

Abstract

Extractive summarization approaches provide interpretable, low-latency document condensation for resource-constrained systems. This paper conducts a rigorous comparative analysis between three foundational paradigms: statistical word frequency weighting, centroid-based K-Means sentence clustering, and graph-centrality PageRank (LexRank/TextRank adaptations) on Arabic news corpora. We report trade-offs in computational latency, sentence diversity, redundancy elimination, and semantic coverage.

Key Technical Contributions

• ◆Systematically benchmarked frequency-based, cluster-based (K-Means), and graph-centrality (PageRank) extractive summarizers.
• ◆Quantified inference time, memory footprint, and ROUGE score distributions across varied Arabic article lengths.
• ◆Provided an empirical selection guideline for low-compute production environments requiring real-time document digests.