ReviewSensors (Basel, Switzerland)2021
Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 35 citations in OpenAlex.
- A review update on Ganoderma species: etiology and management approaches.Antonie van Leeuwenhoek · 2025Review
- Advancements in the nanodelivery of azole-based fungicides to control oil palm pathogenic fungi.Heliyon · 2024Review
- An In-Depth Study of PhytopathogenicJournal of fungi (Basel, Switzerland) · 2024Review
- Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy.Sensors (Basel, Switzerland) · 2023Article
- Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods.PeerJ. Computer science · 2023Article
- Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning.Journal of fungi (Basel, Switzerland) · 2022Article
- Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning.Plant methods · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, 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 review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an 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 imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.