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Plant Science Today (PST)

Review Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

Advances in IoT-enabled smart diagnostics for early detection of fruit crop diseases under climate change

DOI
https://doi.org/10.14719/pst.9796
Submitted
3 June 2025
Published
10-10-2025

Abstract

In perennial fruit crops, climate conditions play a crucial role in influencing phenological stages, fruit quality traits and particularly the occurrence and severity of pests and diseases. The predominant cause of fruit contamination is attributed to fungal and bacterial pathogens, which significantly affect crop health, yield and quality. Mitigating these impacts at an early stage requires advanced methodologies for accurate disease identification and detection. However, a major challenge in precision and smart agriculture lies in the development and availability of reliable image datasets for automated detection, visualization and classification of plant diseases. The growing adoption of the Internet of Things (IoT) has created opportunities for robust systems capable of managing large volumes of agricultural data through efficient processing, storage and transmission. The integration of image processing techniques with intelligent algorithms within IoT frameworks has emerged as a transformative approach, delivering enhanced precision and accuracy in disease monitoring and management across fruit crops. IoT-enabled smart diagnostic systems not only reduce production costs and resource wastage but also revolutionize horticultural practices through automation, leading to improved quality and yield. This review highlights advances in IoT-enabled smart diagnostics for detecting fruit crop diseases under climate conditions. The scope includes examining the interaction between climate variability and disease dynamics, analysing IoT-based frameworks for real-time monitoring and diagnostics and identifying current challenges and future opportunities for sustainable disease management in perennial fruit crops.

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