Near-Infrared Spectroscopy for Ganoderma Boninense Detection: An Outlook

Abstract

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causing a serious threat to the palm oil industry. This catastrophic disease ultimately destructs the basal tissues of oil palm that causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This mini-review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS) towards early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and hyperspectral in reacting with organic tissues, (ii) NIR spectrum is more precise and sensitive to particular diseases include G. boninense compared to visible light, (iii) hand-held NIRS for in situ measurement is to explore the efficacy for early detection system in real-time using machine learning (ML) classifier algorithms and predictive analytics model. This non-destructive, environmentally friendly (no chemical involved), mobile and sensitive leads the integrated hand-held NIRS with ML, and predictive analytics has significant potential as a platform towards early detection of G. boninense in the future.