Efficient Super-Resolution for Resource-Constrained Precision Agriculture: A Loss Function Optimization Approach

Efficient Super-Resolution for Resource-Constrained Precision Agriculture: A Loss Function Optimization Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Efficient Super-Resolution for Resource-Constrained Precision Agriculture: A Loss Function Optimization Approach Mhd. Idham Khalif, Tjhwa Endang Djuana, Richard Antonius Rambung, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9340242/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In smart agriculture, vision-based crop monitoring systems require high-quality imagery along with efficient computation and low power consumption to support accurate analysis and real-time decision-making in resource-constrained edge environments. However, in digital image processing, image quality and computational performance present a trade-off, where increasing reconstruction quality typically increases model complexity and resource requirements. This study addresses this challenge by proposing a lightweight super-resolution (SR) approach optimized for real-world edge applications in agriculture. Unlike existing SR methods that rely on complex architectures or synthetic datasets, this work focuses on loss function-level optimization to improve perceptual image quality without increasing computational cost and power consumption, making it applicable to support resource-constrained precision smart agriculture. ESPCN was chosen as the baseline due to its efficiency, and was further optimized by replacing the conventional Mean Squared Error (MSE) loss with L1 loss to improve robustness to noise and lighting variations. Experimental results on a real-world lettuce dataset captured using ESP32-CAM show that the proposed method produces better texture quality in both day and night conditions. Structural evaluation using Laplacian-based edge density and sharpness, supported by statistical analysis, confirmed improved preservation of high-frequency details without additional computational or energy costs. These findings demonstrate that lightweight loss function optimization provides a practical and energy-efficient solution for improving image quality in edge-based agricultural monitoring systems. edge AI super-resolution ESPCN L1 Loss MSE precision agriculture Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9340242","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620144180,"identity":"25db33cb-6139-4ffe-a89e-a950f3f95f95","order_by":0,"name":"Mhd. 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