Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons

Hudait, Manas; Patel, Priyank Pravin

doi:10.1016/j.ejrs.2022.01.004

Cited by 22 publications

(10 citation statements)

References 11 publications

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“…From 2017 there is a spike in the number of published articles, with a drop in 2021. The years 2020 [ 5 , 8 , 9 , 26 , 43 , 47 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] and 2022 (until 20 October) [ 2 , 6 , 18 , 28 , 39 , 42 , 44 , 49 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ] showed a higher number of articles published, 20 per year, which represents 16% of the total, 32% combined.…”

Section: Resultsmentioning

confidence: 99%

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Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

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Section: Resultsmentioning

confidence: 99%

“…The presence of ML is significantly increasing in mapping paddy crops [ 42 , 43 , 44 , 45 ] or classifying different types of land cover [ 28 , 46 ] and is expected to keep gaining popularity over traditional methods such as thresholding [ 13 , 23 , 47 , 48 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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“…Hudait et al [60] mapped the heterogeneous crop area according to the crop type in the Purba Medinipur District of West Bengal. They used Sentinel-2 multispectral imagery and two machine learning algorithms: KNN and RF.…”

Section: Related Workmentioning

confidence: 99%

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Pech-May

Aquino-Santos

Ríos-Toledo

et al. 2022

Sensors

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Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

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“…These techniques enable the automatic and efficient extraction of useful information and patterns from large amounts of image data. Particularly, ML-based image analysis has been applied in agriculture for crop mapping [28] , [29] , [30] , yield prediction [31] , [32] and pest and disease detection [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] . These techniques have given farmers and researchers valuable precision agriculture insights to help maximise crop yields and enhance decision-making throughout the crop production processes.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning for image-based analysis of sweetpotato root sensory attributes

Nakatumba-Nabende,

Babirye,

Tusubira

et al. 2023

Smart Agricultural Technology

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Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons

Cited by 22 publications

References 11 publications

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Using machine learning for image-based analysis of sweetpotato root sensory attributes

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