Unverified paper record
A Study on the Distribution Pattern of Banana Blood Diseases (BDB) and Fusarium Wilt Using Multispectral Aerial Photo and Handheld Spectrometer in Subang-Indonesia
12 Jul 2023 · 10.20944/preprints202307.0744.v1
Abstract
Knowledge on the health of banana trees is critical for farmers to profit from banana cultivation. Fusarium wilt and banana blood diseases (BDB), two significant diseases infecting banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have successfully caused a decline in crop yield as they destroy the trees, starting sequentially from the pseudostem to the fruits. The entire distribution of BDB and Fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using an unmanned aerial vehicle (UAV), combined with the reliable data field of infected trees. Vegetation and soil indices derived from a multispectral aerial photograph, such as normalized difference vegetation index, modified chlorophyll absorption ratio index, normalized difference water index (NDWI) and soil pH, may have to be relied on to explain the precise location of these two diseases. In this study, a random forest algorithm was used to handle a large dataset consisting of two models: the banana diseases multispectral model and the banana diseases spectral model. The results show that the soil indices, soil pH and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BDB is more extensive than that affected by Fusarium, if the variation of planted banana cultivars is not considered.
Plant phenotyping relevance
UAVマルチスペクトル画像と携帯型分光計、ランダムフォレストを組み合わせ、バナナ樹の病害状態の空間分布を推定する方法が研究の中心であるため、植物病害フェノタイピングとして収録する。
abstractThe entire distribution of BDB and Fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using an unmanned aerial vehicle (UAV), combined with the reliable data field of infected trees.
abstractIn this study, a random forest algorithm was used to handle a large dataset consisting of two models: the banana diseases multispectral model and the banana diseases spectral model.
Code and data availability
The paper describes UAV multispectral imagery, handheld spectrometer measurements, and a random forest analysis of banana disease distribution, but provides no public deposit of the phenotype data, aerial/spectral images, analysis code, or trained models. All URLs present are vendor equipment pages or third-party tool/
No evidence-backed public reproduction asset is currently recorded.
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.