arXiv:2510.23124v1 Announce Type: new 
Abstract: Soil salinization poses a significant threat to both ecosystems and agriculture because it limits plants' ability to absorb water and, in doing so, reduces crop productivity. This phenomenon alters the soil's spectral properties, creating a measurable relationship between salinity and light reflectance that enables remote monitoring. While laboratory spectroscopy provides precise measurements, its reliance on in-situ sampling limits scalability to regional or global levels. Conversely, hyperspectral satellite imagery enables wide-area observation but lacks the fine-grained interpretability of laboratory instruments. To bridge this gap, we introduce DeepSalt, a deep-learning-based spectral transfer framework that leverages knowledge distillation and a novel Spectral Adaptation Unit to transfer high-resolution spectral insights from laboratory-based spectroscopy to satellite-based hyperspectral sensing. Our approach eliminates the need for extensive ground sampling while enabling accurate, large-scale salinity estimation, as demonstrated through comprehensive empirical benchmarks. DeepSalt achieves significant performance gains over methods without explicit domain adaptation, underscoring the impact of the proposed Spectral Adaptation Unit and the knowledge distillation strategy. The model also effectively generalized to unseen geographic regions, explaining a substantial portion of the salinity variance.

ديب سالت هو نهج مبتكر يجمع بين بيانات المختبر والأقمار الصناعية لتحسين تقدير ملوحة التربة، وهو أمر حاسم للزراعة وصحة النظم البيئية. من خلال استخدام التكيف مع المجال وتقطير المعرفة، تعزز هذه الطريقة دقة المراقبة عن بُعد، مما يسمح بإدارة أفضل لمشكلات الملوحة التي تهدد إنتاجية المحاصيل. لا تعالج هذه الابتكار تحديًا بيئيًا ملحًا فحسب، بل تدعم أيضًا ممارسات الزراعة المستدامة، مما يجعلها تقدمًا كبيرًا في تكنولوجيا الزراعة.

DeepSalt es un enfoque innovador que combina datos de laboratorio y satélite para mejorar la estimación de la salinidad del suelo, lo cual es crucial para la agricultura y la salud de los ecosistemas. Al utilizar adaptación de dominio y destilación de conocimiento, este método aumenta la precisión del monitoreo remoto, permitiendo una mejor gestión de los problemas de salinización que amenazan la productividad de los cultivos. Esta innovación no solo aborda un desafío ambiental urgente, sino que también apoya prácticas agrícolas sostenibles, convirtiéndose en un avance significativo en la tecnología agrícola.

DeepSalt est une approche révolutionnaire qui combine les données de laboratoire et de satellite pour améliorer l'estimation de la salinité du sol, essentielle pour l'agriculture et la santé des écosystèmes. En utilisant l'adaptation de domaine et la distillation des connaissances, cette méthode améliore la précision de la surveillance à distance, permettant une meilleure gestion des problèmes de salinisation qui menacent la productivité des cultures. Cette innovation répond non seulement à un défi environnemental pressant, mais soutient également des pratiques agricoles durables, ce qui en fait une avancée significative dans la technologie agricole.

DeepSalt is a groundbreaking approach that combines laboratory and satellite data to improve soil salinity estimation, which is crucial for agriculture and ecosystem health. By using domain adaptation and knowledge distillation, this method enhances the accuracy of remote monitoring, allowing for better management of salinization issues that threaten crop productivity. This innovation not only addresses a pressing environmental challenge but also supports sustainable farming practices, making it a significant advancement in agricultural technology.

DeepSalt: Bridging Laboratory and Satellite Spectra through Domain Adaptation and Knowledge Distillation for Large-Scale Soil Salinity Estimation

arXiv:2511.19103v1 Announce Type: new 
Abstract: The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is particularly problematic in resource-constrained and remote environments where bandwidth is limited, and battery-dependent devices further emphasize the problem. Moreover, in domains such as agriculture, consecutive sensor readings often have minimal variation, making continuous data transmission inefficient and unnecessarily resource intensive. To overcome these challenges, we propose an analytical prediction algorithm designed for edge computing environments and validated through simulation. The proposed solution utilizes a predictive filter at the network edge that forecasts the next sensor data point and triggers data transmission only when the deviation from the predicted value exceeds a predefined tolerance. A complementary cloud-based model ensures data integrity and overall system consistency. This dual-model strategy effectively reduces communication overhead and demonstrates potential for improving energy efficiency by minimizing redundant transmissions. In addition to reducing communication load, our approach leverages both in situ and satellite observations from the same locations to enhance model robustness. It also supports cross-site generalization, enabling models trained in one region to be effectively deployed elsewhere without retraining. This makes our solution highly scalable, energy-aware, and well-suited for optimizing sensor data transmission in remote and bandwidth-constrained IoT environments.

تم اقتراح خوارزمية جديدة للتنبؤ التحليلي لبيئات الحوسبة الطرفية في الزراعة، تهدف إلى تقليل النقل المفرط لبيانات المستشعرات الناتجة عن أجهزة إنترنت الأشياء. تستخدم هذه الطريقة فلترًا تنبؤيًا لتوقع قراءات المستشعر، مما يؤدي إلى تفعيل نقل البيانات فقط عند حدوث انحرافات كبيرة، وبالتالي معالجة مشكلات ازدحام الشبكة واستهلاك الطاقة في البيئات ذات الموارد المحدودة.

Se ha propuesto un nuevo algoritmo de predicción analítica para entornos de computación en el borde en la agricultura, con el objetivo de reducir la transmisión excesiva de datos de sensores generados por dispositivos IoT. Este enfoque utiliza un filtro predictivo para prever las lecturas de los sensores, activando la transmisión de datos solo cuando ocurren desviaciones significativas, abordando así problemas de congestión de red y consumo de energía en entornos con recursos limitados.

Un nouvel algorithme de prédiction analytique a été proposé pour les environnements de calcul en périphérie dans l'agriculture, visant à réduire la transmission excessive des données des capteurs générées par les dispositifs IoT. Cette approche utilise un filtre prédictif pour prévoir les lectures des capteurs, déclenchant la transmission des données uniquement lorsque des écarts significatifs se produisent, abordant ainsi les problèmes de congestion du réseau et de consommation d'énergie dans des environnements à ressources limitées.

A new analytical prediction algorithm has been proposed for edge computing environments in agriculture, aimed at reducing the excessive transmission of sensor data generated by IoT devices. This approach utilizes a predictive filter to forecast sensor readings, triggering data transmission only when significant deviations occur, thereby addressing issues of network congestion and energy consumption in resource-constrained settings.

DeepSalt: Bridging Laboratory and Satellite Spectra through Domain Adaptation and Knowledge Distillation for Large-Scale Soil Salinity Estimation

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