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.
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机译:灵芝Boninense(G. Boninense)感染降低了油棕榈树的生产力,对棕榈油工业造成严重威胁。这种灾难性的疾病最终破坏了油棕的基底组织,导致手掌的最终死亡。 G. Boninense的早期检测至关重要,因为没有有效的治疗以阻止疾病的持续扩散。该审查描述了近红外光谱(NIRS),用于预测分析的机器学习分类和朝向早期G. Boninense检测系统的信号处理的过去和将来的综合研究前景。这项努力可以降低种植园管理的成本,避免生产损失。值得注意的是,(i)光谱技术比其他检测技术更可靠,与有机组织的反应中的血清学,分子,生物标记的传感器和成像技术更可靠,(ii)NIR光谱更精确,对特定疾病敏感,包括G 。与可见光(III)用于原位测量的可见光和(III)的博彩喇叭用于使用ML分类器算法和预测分析模型实时探索早期检测系统的功效。无损,环保(没有化学品),移动和敏感,导致甚至未来为早期检测G. Boninense的高度检测的重要平台。
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