New Spectral Index and Machine Learning Models for Detecting Coffee Leaf Miner Infestation Using Sentinel-2 Multispectral Imagery

نویسندگان

چکیده

The coffee leaf miner (Leucoptera coffeella) is a key pest in Brazil that can cause severe defoliation and negative impact on the productivity. Thus, it essential to identify initial infestation for sake of appropriate time control avoid further economic damage crops. A fast non-destructive method an important tool be used monitor occurrence miner. present work aims through new vegetation index, using multispectral images from Sentinel-2 satellite Google Earth Engine platform. Coffee was measured field four cities state Minas Gerais. largest infestations occurred September, October, November but particularly October 2021, which rate reached 85%, followed by September 2020 with maximum 76%. calculation steps indices mappings were carried out cloud processing platform development script JavaScript programming language. Combinations two sensitive bands selected detect infestation, these, “Coffee-Leaf-Miner Index” developed, compared other existing terms their performance detection. combination NIR–BLUE NIR–RED more detection infestation; therefore, NIR, BLUE, RED develop index. presented best among those evaluated, coefficient determination about 0.87, root mean square error 4.92% accuracy 89.47%, kappa 95.39. R2 range spectral exist literature this study 0.017 0.867, ranged 4.996 13.582% infestation. machine learning then adopted supervised Random Forest Support Vector Machine algorithms recognize patterns field, only Coffee-Leaf-Miner Index identification test linear Kernel type applied establish discrimination model. number trees classifier 100. lower than algorithm, both above 80% user producer precision. Three (Blue, Red, NIR) creation showed capacity remote regional scale. thanks all complexity involved detecting pests via orbital sensing.

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ژورنال

عنوان ژورنال: Agriculture

سال: 2023

ISSN: ['2077-0472']

DOI: https://doi.org/10.3390/agriculture13020388