Using information from images for plantation monitoring: A review of solutions for smallholders

Putra, Bayu Taruna Widjaja; Soni, P.; Marhaenanto, Bambang; Pujiyanto, Pujiyanto; Harsono, Soni Sisbudi; Fountas, Spyros

doi:10.1016/j.inpa.2019.04.005

Cited by 20 publications

(16 citation statements)

References 56 publications

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“…Digital images provide information about the crop that enables growers to monitor phenology, pests, and diseases; provides information about plant nutrition; and allows growers to estimate produce yield (Putra, Soni, Marhaenanto, Pujiyanto Harsono, & Fountas, 2020). According to López et al.…”

Section: Introductionmentioning

confidence: 99%

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

et al. 2021

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The current challenge of corn (Zea mays L.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super-traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha -1 and high, 180 kg ha -1 ). At full bloom, the spectral variables green (550 nm), red (660 nm), red-edge (735 nm), and near-infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil-adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red-edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield. INTRODUCTIONThe world demand for corn (Zea mays L.) consumption has increased, which has led to the need for increased production.To meet this growing demand, it is necessary to invest in crop

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Section: Introductionmentioning

confidence: 99%

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

et al. 2021

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“…Tranformasi NDVI dilakukan untuk mengetahui sebaran vegetasi. Transformasi NDVI dilakukan menggunakan software Q-GIS 3.2.0. dengan algoritma (Putra, Soni, Marhaenanto, Harsono, & Fountas, 2019): (Clerici dkk., 2017;Jaya & Surati, 2010).…”

Section: Tabel 1 Sumber Data Citra Sentinel-2unclassified

Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

2020

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Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values = 66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

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“…In Southeast Asia, smallholder forest plantations have gained popularity and are replacing degraded or unproductive crop land, offsetting the demand on primary forests (Binkley, 2003;Okarda et al, 2018;Putra et al, 2019;Puyravaud et al, 2010;Roy et al, 2015;Rudel, 2009;Sharma et al, 2018). Clonal plantations are common in planted forests to genetically improve the growing stock and produce a fast, high yield stock of species such as Eucalyptus globus and Casuarina spp.…”

Section: Introductionmentioning

confidence: 99%

Mapping smallholder forest plantations in Andhra Pradesh, India using multitemporal harmonized landsat sentinel‐2 S10 data

et al. 2021

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Cloud-free satellite data are not available during the monsoon season (July to September) in this coastal region. In situ data on forest plantations, provided by collaborators, was supplemented with additional training data representing other land cover subclasses in the region: agriculture, water, aquaculture, mangrove, palm, forest plantation, ground, natural forest, shrub/scrub, sand, and urban, with a total sample size of 2,230. These high-quality samples were then aggregated into three land use classes: non-forest, natural forest, and forest plantations. Image classification used random forests within the Julia Decision Tree package on a thirty-band stack that was comprised of the VNIR bands and NDVI images for all dates. The median classification accuracy from the 5-fold cross validation was 94.3%. Our results, predicated on high quality training data, demonstrate that (mostly smallholder) forest plantations can be separated from natural forests even using only the Sentinel 2 VNIR bands when multitemporal data (across both years and seasons) are used.

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Using information from images for plantation monitoring: A review of solutions for smallholders

Cited by 20 publications

References 56 publications

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

Mapping smallholder forest plantations in Andhra Pradesh, India using multitemporal harmonized landsat sentinel‐2 S10 data

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