Multispectral UAV data for detection of weeds in a citrus farm using machine learning and Google Earth Engine: Case study of Morocco

Imanni, Hajar Saad El; Harti, Abderrazak El; Bachaoui, El Mostafa; Mouncif, Hicham; Eddassouqui, Fatine; Hasnai, Mohamed Achraf; Zinelabidine, Moulay Ismail

doi:10.1016/j.rsase.2023.100941

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Legend of classification results in the land cover mapping of Casablanca.This study used Landsat 8 OLI datasets and processing tools, including the Google Earth Engine (GEE) platform, to map land cover categories in Casablanca. The study used various classifiers, such as CART, RF, SVM, Gradient Tree Boost, DT, and MD, to delineate the territory into different zones, including Water, Forest, Built-up, Barren, and Cropped areas.Based on previous studies[9,14,16,[19][20][21][22][23][46][47][48][49][50][51][52] and our own research, we found that the methodology em-…”

mentioning

confidence: 74%

“…By incorporating these indices, the study aimed to improve the classification accuracy and achieve more precise identification of land cover categories. Based on previous studies [9,14,16,[19][20][21][22][23][46][47][48][49][50][51][52] and our own research, we found that the methodology em-ployed in our experiment can be adapted to map and evaluate different regions in various countries or cities, as this methodology is not oriented solely toward Casablanca city. The main contribution of this research has been the successful adaptation of the method to map and evaluate land use in other regions or countries.…”

Section: Supervised Learningmentioning

confidence: 87%

“…Based on previous studies [9,14,16,[19][20][21][22][23][46][47][48][49][50][51][52] and our own research, we foun methodology employed in our experiment can be adapted to map and evaluate different various countries or cities, as this methodology is not oriented solely toward Casablanc main contribution of this research has been the successful adaptation of the method to evaluate land use in other regions or countries. Based on the confusion matrix for each it was evident that the random forest classifier was the most effective, boasting coefficient of 0.94.…”

Section: Supervised Learningmentioning

confidence: 99%

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Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Ouchra,

Belangour,

Erraissi

2023

j. of environ. & earth. sci.

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Satellite image classification is crucial in various applications such as urban planning, environmental monitoring, and land use analysis. In this study, the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification, focusing on a case study in Casablanca using Landsat 8 imagery. This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment. The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city, the authors extract relevant features and partition them into training and test sets, and then use random forest (RF), SVM (support vector machine), classification, and regression tree (CART), gradient tree boost (GTB), decision tree (DT), and minimum distance (MD) algorithms. Through a series of experiments, the authors evaluate the performance of each machine learning method in terms of accuracy, and Kappa coefficient. This work shows that random forest is the best-performing algorithm, with an accuracy of 95.42% and 0.94 Kappa coefficient. The authors discuss the factors of their performance, including data characteristics, accurate selection, and model influencing.

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confidence: 74%

Section: Supervised Learningmentioning

confidence: 87%

Section: Supervised Learningmentioning

confidence: 99%

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Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Ouchra,

Belangour,

Erraissi

2023

j. of environ. & earth. sci.

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“…Subsequently, the strengths and limitations of each of these approaches will need to be considered before deciding which method to adopt. However, pixel-and object-based approaches have been shown to perform well for crop and weed mapping [38][39][40].…”

Section: Algorithms and Methodologiesmentioning

confidence: 99%

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

Gokool,

Mahomed,

Clulow

et al. 2024

Drones

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In light of a growing population and climate change compounding existing pressures on the agri-food system, there is a growing need to diversify agri-food systems and optimize the productivity and diversity of smallholder farming systems to enhance food and nutrition security under climate change. In this context, improving weed management takes on added significance, since weeds are among the primary factors contributing to crop yield losses for smallholder farmers. Adopting remote-sensing-based approaches to facilitate precision agricultural applications such as integrated weed management (IWM) has emerged as a potentially more effective alternative to conventional weed control approaches. However, given their unique socio-economic circumstances, there remains limited knowledge and understanding of how these technological advancements can be best utilized within smallholder farm settings. As such, this study used a systematic scoping review and attribute analysis to analyze 53 peer-reviewed articles from Scopus to gain further insight into remote-sensing-based IWM approaches and identify which are potentially best suited for smallholder farm applications. The findings of this review revealed that unmanned aerial vehicles (UAVs) are the most frequently utilized remote sensing platform for IWM applications and are also well suited for mapping and monitoring weeds within spatially heterogeneous areas such as smallholder farms. Despite the potential of these technologies for IWM, several obstacles to their operationalization within smallholder farm settings must be overcome, and careful consideration must be given on how best to maximize their potential before investing in these technologies.

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“…(d) Support Vector Machine (SVM) based algorithms: This group involves algorithms such as Margin SVM, Voting SVM, Pegasos, and IKPamir. SVM is a popular machine learning technique that utilizes geometric principles to separate and classify data [35][36][37]. Through the support of these machine learning algorithms, the GEE aims to enhance the capabilities of remote sensing data analysis and interpretation [28].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Kazemi Garajeh

2024

Pollutants

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Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin.

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Multispectral UAV data for detection of weeds in a citrus farm using machine learning and Google Earth Engine: Case study of Morocco

Cited by 7 publications

References 17 publications

Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

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