Sustainable Plant Disease Management with Real-Time Crop Optimization

N. Sridhar; Radhika Peeriga; Swapna Thouti; G. Sravanthi; V. Sangeetha; Mohamed Ghatasheh; Habib Kraiem; Aymen Flah

doi:10.48084/etasr.12354

Authors

N. Sridhar Department of IT, Malla Reddy (MR) Deemed to be University, Secunderabad, Telangana, India
Radhika Peeriga Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddewaram, Andhrapradesh, India
Swapna Thouti Department of ECE, CVR College of Engineering, Ibrahimpatnam, Hyderabad, Telangana, India
G. Sravanthi Department of CSE, Malla Reddy College of Engineering, Secunderabad, Telangana, India
V. Sangeetha Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India
Mohamed Ghatasheh Department of Basic Sciences, Middle East University, Amman, Jordan
Habib Kraiem Higher Institute of Industrial Systems in Gables, University of Gabes, Tunisia
Aymen Flah Jadara University Research Center, Jadara University, Jordan | Applied Science Research Center, Applied Science Private University, Amman, Jordan

Volume: 15 | Issue: 6 | Pages: 30580-30585 | December 2025 | https://doi.org/10.48084/etasr.12354

Received: 24 May 2025 | Revised: 16 June 2025 and 25 June 2025 | Accepted: 6 July 2025 | Online: 16 November 2025

Corresponding author: Habib Kraiem

Abstract

Plant diseases significantly threaten global food security, often leading to severe yield losses and unsustainable reliance on chemical usage and pesticides. This paper presents an integrated, real-time system for sustainable plant disease management using Internet of Things (IoT) sensors, deep learning models, and cloud-edge computing. The proposed framework enables early disease detection and adaptive crop optimization by fusing environmental telemetry with AI-driven image diagnostics. Using the PlantVillage dataset and real-world sensor data, the system achieves 99.1% disease detection accuracy, a 27% reduction in pesticide usage, and a 22% improvement in crop yield, a critical metric in assessing the broader effectiveness of plant disease management strategies compared to leading benchmarks. Field trials confirm its efficacy in enhancing farm productivity while minimizing environmental impact. This work demonstrates a practical, scalable solution for precision agriculture that aligns with the principles of sustainability, resilience, and data-driven decision-making.

Keywords:

smart farming, plant disease detection, Internet of Things (IoT), deep learning, real-time crop monitoring, sustainable agriculture

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