Machine learning applications for sustainable durian disease management

Ahmad Azmi, Nur Sabrina and Zainuddin, Zarina and Samsulrizal, Nurul Hidayah and Noor Hashim, Noor Haslinda and Kuswandi, Paramita Cahyaningrum and Rakhmawati, Anna and Ariyanti, Nur Aeni (2026) Machine learning applications for sustainable durian disease management. Plant Science Today, 13 (2). pp. 1-6. E-ISSN 2348-1900

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Abstract

Durian, the beloved "King of Fruits," serves as an economic lifeline for Southeast Asia, yet it faces constant threats from fungal pathogens that can destroy up to 40 % of a harvest. While traditional visual checks are often subjective and laboratory tests like polymerase chain reaction (PCR) are too complex for daily field use, machine Learning (ML) offers a more supportive, practical path forward. By using convolutional neural networks (CNNs) on mobile devices, farmers can now identify diseases with 90 % accuracy directly in the orchard. Advanced tools like thermal imaging and biosensors act as an early-warning system, detecting hidden stress before symptoms ever appear. This shift toward predictive farming empowers growers to protect their livelihoods more sustainably. Techniques like transfer learning bridge the gap between high-tech science and daily labour, ensuring that even with limited data, farmers have a dependable companion in the grove. Ultimately, integrating ML and internet of things (IoT) minimizes chemical reliance, safeguarding both the environment and the long-term health of the durian industry.

Item Type:	Article (Journal)
Uncontrolled Keywords:	biosensor; durian; internet of things; machine learning; neural network; plant disease detection; sustainable agriculture
Subjects:	S Agriculture > S Agriculture (General) S Agriculture > S Agriculture (General) > S494.5.S86 Sustainable agriculture
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science Kulliyyah of Science > Department of Plant Science
Depositing User:	Dr Nur Sabrina Ahmad Azmi
Date Deposited:	28 Apr 2026 08:37
Last Modified:	28 Apr 2026 08:37
Queue Number:	2026-04-Q3037
URI:	http://irep.iium.edu.my/id/eprint/128612

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