Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing

Purnamasari, Riska Ayu; Noguchi, Ryozo; Ahamed, Tofael

doi:10.1016/j.compag.2019.105018

Cited by 49 publications

(17 citation statements)

References 23 publications

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“…The NIR band was found to be best for studying grain yields, while the RE bands were highly correlated to nitrogen-related traits of plants. In a similar study, Ayu Purnamasari et al (2019) [105] studied yield prediction and land suitability for the cassava crop in Indonesia, revealing that maximum correlation can be found between vegetation indices and green-up, and that remote Sentinel-2 data also helped to predict yields up to five months in advance of the harvest season. This study also showed that the yield was highly correlated with vegetation indices such as NDVI, soil-adjusted vegetation index (SAVI) and the inverted red-edge chlorophyll index (IRECI), and to the leaf area index (LAI) and fraction of photosynthetically active radiation (fAPAR).…”

Section: Estimation Of Crop Yieldmentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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Section: Estimation Of Crop Yieldmentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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“…Following the systematic search, 101 papers were selected for further analysis (Supplementary information). From the articles reviewed, only five crops were regarded as NUS (sorghum [30][31][32], cassava [33,34], cowpea and pearl millet [35], and foxtail millet [36]) have been assessed and these were across 23 articles (Tables S2 and S3; Supplementary information). The majority of crop species were cereals, namely maize, rice and wheat.…”

Section: Results Of Literature Searchmentioning

confidence: 99%

Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review

Mugiyo

Chimonyo

Sibanda

et al. 2021

Land

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In agriculture, land use and land classification address questions such as “where”, “why” and “when” a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

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“…Prior studies in the literature reviewed included web-based irrigation with GIS [12-17] and geospatial crop production application and modeling [18][19][20][21][22]. Compared to those studies, the new contribution of FARMs is enabling crop simulation (1) with DSSAT, (2) for global-scale application, (3) in a web-based GIS environment, (4) with automatic weather, climate and soil support, and (5) with in-season decision-making support at the same time.…”

Section: Discussionmentioning

confidence: 99%

“…For example, there are web-based irrigation scheduling applications with GIS capabilities [12][13][14][15][16][17]. Furthermore, there are geospatial crop production applications or models, which has been recently developed [18][19][20][21][22]. However, it is hard to find a global scale web-based geospatial agricultural water management tool that considers the G × E × M × S dynamic interactions as well as environmental impacts such as nitrate leaching in a specific field.…”

Section: Introductionmentioning

confidence: 99%

FARMs: A Geospatial Crop Modeling and Agricultural Water Management System

Kim

Kisekka

2021

IJGI

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To ensure agricultural sustainability and desirable environmental outcomes, stakeholders need systems-based model-driven decision support tools. The objective of this study was to develop a global scale web-based geospatial crop modeling application called Food, Agriculture, and Resource Management system (FARMs), to simplify the application of the crop simulation model —Decision Support System for Agrotechnology Transfer (DSSAT) without requiring users to create input weather, climate, and soil files. FARMs was built based on open source Geographic Information System (GIS) technologies and DSSAT to allow for adaptive management through its ability to perform in-season yield predictions for alfalfa and maize, currently. Validation of FARMs against variety trial data in California was acceptable between measured and simulated yields for alfalfa. The work done in this study showed how a complex model like DSSAT can be translated into a useable web-based decision support tool for near-real-time simulation with the help of open-source GIS technologies.

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Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing

Cited by 49 publications

References 23 publications

Status of Phenological Research Using Sentinel-2 Data: A Review

Status of Phenological Research Using Sentinel-2 Data: A Review

Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review

FARMs: A Geospatial Crop Modeling and Agricultural Water Management System

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