Identifying Agricultural Systems Using SVM Classification Approach Based on Phenological Metrics in a Semi-arid Region of Morocco

Lebrini, Youssef; Boudhar, Abdelghani; Hadria, Rachid; Lionboui, Hayat; Elmansouri, Loubna; Arrach, R.; Ceccato, Pietro; Benabdelouahab, Tarik

doi:10.1007/s41748-019-00106-z

Cited by 33 publications

(22 citation statements)

References 53 publications

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“…Debatably the most popular machine learning algorithms of the past decade, Support Vector Machine (SVM) has been widely used in many fields, including agriculture. In several works, SVM was applied to classify satellite images, becoming a standard choice for visual recognition in agriculture [63][64][65][66]. At the same time, k Nearest Neighbors (KNN) algorithm was an alternative learning-based approach, used in various soil classification tasks [67][68][69][70].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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Section: Machine Learning Methodsmentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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“…SVM is a supervised ML model that works very well for many classification tasks ( Lebrini et al, 2019 ). Once SVM is fed with sets of labeled training data for each class, they can be categorized into new samples.…”

Section: Proposed Methodologymentioning

confidence: 99%

IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage

Bhoi¹,

Nayak²,

Bhoi

et al. 2021

PeerJ Computer Science

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In the traditional irrigation process, a huge amount of water consumption is required which leads to water wastage. To reduce the wasting of water for this tedious task, an intelligent irrigation system is urgently needed. The era of machine learning (ML) and the Internet of Things (IoT) brings it is a great advantage of building an intelligent system that performs this task automatically with minimal human effort. In this study, an IoT enabled ML-trained recommendation system is proposed for efficient water usage with the nominal intervention of farmers. IoT devices are deployed in the crop field to precisely collect the ground and environmental details. The gathered data are forwarded and stored in a cloud-based server, which applies ML approaches to analyze data and suggest irrigation to the farmer. To make the system robust and adaptive, an inbuilt feedback mechanism is added to this recommendation system. The experimentation, reveals that the proposed system performs quite well on our own collected dataset and National Institute of Technology (NIT) Raipur crop dataset.

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“…During recent decades, the scientific community has investigated the impact of land management on specific indices (e.g., normalized difference vegetation index, NDVI) in order to link vegetation changes to anthropogenic actions [22]. With this in mind, many researchers went further in studying changes in vegetation cover, based on satellite-derived products, by developing techniques and tools for extracting and analyzing PhM, especially for monitoring farming systems [5,[23][24][25]. They demonstrated the ability of these metrics to monitor and differentiate vegetation cover based on contrasted phenological profiles [5,[26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

et al. 2021

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Changing land use patterns is of great importance in environmental studies and critical for land use management decision making over farming systems in arid and semi-arid regions. Unfortunately, ground data scarcity or inadequacy in many regions can cause large uncertainties in the characterization of phenological changes in arid and semi-arid regions, which can hamper tailored decision making towards best agricultural management practices. Alternatively, state-of-the-art methods for phenological metrics’ extraction and long time-series analysis techniques of multispectral remote sensing imagery provide a viable solution. In this context, this study aims to characterize the changes over farming systems through trend analysis. To this end, four farming systems (fallow, rainfed, irrigated annual, and irrigated perennial) in arid areas of Morocco were studied based on four phenological metrics (PhM) (i.e., great integral, start, end, and length of the season). These were derived from large Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series using both a machine learning algorithm and a pixel-based change analysis method. Results showed that during the last twenty-year period (i.e., 2000–2019), a significant dynamism of the plant cover was linked to the behavior of farmers who tend to cultivate intensively and to invest in high-income crops. More specifically, a relevant variability in fallow and rainfed areas, closely linked to the weather conditions, was found. In addition, significant lag trends of the start (−6 days) and end (+3 days) were found, which indicate that the length of the season was related to the spatiotemporal variability of rainfall. This study has also highlighted the potential of multitemporal moderate spatial resolution data to accurately monitor agriculture and better manage land resources. In the meantime, for operationally implementing the use of such work in the field, we believe that it is essential consider the perceptions, opinions, and mutual benefits of farmers and stakeholders to improve strategies and synergies whilst ensuring food, welfare, and sustainability.

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Identifying Agricultural Systems Using SVM Classification Approach Based on Phenological Metrics in a Semi-arid Region of Morocco

Cited by 33 publications

References 53 publications

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

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