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

Abstract

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

Use of Images Obtained by Remotely Piloted Aircraft and Random Forest for the Detection of Leaf Miner (Leucoptera coffeella) in Newly Planted Coffee Trees

“…In this study, it was observed that healthy plants had a higher mean reflectance of the EDGE, G, NIR and R bands than infested plants. This result is consistent with the studies conducted by Vilela et al [17] in which the authors observed that the spectral response in the NIR region of a coffee crop with a higher percentage of leaf miner infestation was lower than that of a crop with a lower percentage of infestation. That is, the greater the reflectance, especially in the NIR region, the more this spectral behaviour is related to vegetative vigour.…”

“…These mines cause leaf necrosis, contributing to a lower photosynthetic rate [4] and, therefore, lower vigour and lower spectral response in the NIR range. Thus, the infestations of pests that affect the internal structures of the leaves can be evaluated using the NIR band, confirming the results of this study and some previous studies [17,40] and highlighting the importance of spectral bands in RF classifications, as demonstrated by the Gini index (Figure 5).…”

“…A study of the application of Sentinel-2 satellite images to identify coffee leaf miner infestation levels in coffee plantations was conducted by Vilela et al [17]. The study considered bands of the Sentinel-2 satellite with a spatial resolution of 10 m and a revisit period of approximately five days.…”

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

“…The prior studies have demonstrated that incorporating vegetation features into the extraction of crop distribution information from remote sensing imagery can result in improved discrimination between vegetation and non-vegetation, as well as different types of vegetation, compared to relying solely on the original four spectral bands [36][37][38]. This study employed prior knowledge to extract four additional vegetation indices from the original four spectral bands, namely the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Optimal Soil Adjusted Vegetation Index (OSAVI), and Canola Index (CI) [17,39].…”

Remote Monitoring of Coffee Leaf Miner Infestation Using Machine Learning

“…The Green-Red Normalized Difference Vegetation Index (GRNDVI) was the most effective factor in differentiating between infected and healthy leaves, with average values of 0.32 for healthy leaves and 0.06 for infected leaves. In another study [7], the authors developed a specific vegetation index to estimate coffee leaf miner infestation using Sentinel-2 multispectral images called the Coffee-Leaf-Miner Index (CLMI). The authors employed machine learning (ML) techniques to recognize infestation patterns in coffee plantations.…”