Internet of Things to Improve Agriculture in Sub Sahara Africa - A Case Study

Ishengoma, Farian S.; Athuman, Mgeni

doi:10.31695/ijasre.2018.32739

Cited by 7 publications

(5 citation statements)

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“…Thus, if limited efforts in research and developments that are related to ML solutions deployments for smart agricultural soil fertility management to deal with the low soil fertility characteristic problem, such as the one depicted in Figure 1 will be the fact to prevail, then it may become more difficult to have provisions for fine-tuned or site specific soil fertility restoration or preservation measures of fertilization, crop rotation, non-tillage farming, mixed planting, sowing green manure, mulching, and fallowing, this of which is necessary to increase field fertility [5]. Most critically, the infertility problem even cause catastrophic hunger condition(s) by the year 2050, when the world's population growth will reach the estimated 9.6 to 9.73 billion people(approximately 10 billion) from the current 7.3 billion [6][7][8][9]. Therefore, to achieve smart soil fertility management system as part of a sustainable smart global food production and supply system which is currently a major demand of the United Nations Food and Agriculture organization (FAO) is apparently imperative.…”

Section: Figurementioning

confidence: 99%

A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

Malamsha,

Dida,

Moebs

2023

Preprint

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The problem of low soil fertility and limited research in agricultural data driven tools, may lead to low crop productivity which makes it imperative to research in applications of high throughput computational algorithms such as of machine learning (ML) for effective soil analysis and fertility status prediction in order to assist in optimal soil fertility management decision-making activities. However, difficulties in the choice of the key soil properties parameters for use in reliable soil nutrients analysis and fertility prediction. Also, individual ML algorithms setbacks and modelling expert implementation procedures subjectivity, may lead to exploitation of worst fertility level targets and soil fertility status targets classification models performance reported variations. This paper surveys state-of-affair in ML for agricultural soil nutrients analysis and fertility status prediction. Prominent soil properties and widely used classical modelling algorithms and procedures are identified. Empirically exploited fertility status target classes are scrutinized, and reported soil fertility prediction model performances are depicted. The three pass method, with mixed method of qualitative content analysis and qualitative simple descriptive statistics were used in this survey. Observably, the frequently used soil nutrients and chemical properties were organic carbon, phosphorus, potassium, and potential Hydrogen, followed by iron, manganese, copper and zinc. Predominant algorithms included Random Forest, and Naïve Bayes, followed by Support Vector Machine. Model performances varied, with highest accuracy 98.93% and 98.15% achieved by ensemble methods, and the least being 60%. Interdisciplinary ML related researchers may consider using ensemble methods to develop high performance soil fertility status prediction models.

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Section: Figurementioning

confidence: 99%

A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

Malamsha,

Dida,

Moebs

2023

Preprint

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“…To date, inadequate information has been given about the extent of (un)sustainability of the current systems (Ishengoma and Athuman, 2018). Thus, the economic importance of sustainable cotton production necessitates the evaluation of its environmental impacts.…”

Section: Types Of Life Cycle Assessment (Lca)mentioning

confidence: 99%

“…Cotton is planted in over 100 countries. It accounts for more than 31 million hectares or 2.4% of the world's arable land, impacting approximately 20 million farmers who depend entirely on cotton output and another 30 million farmers who include cotton in their rotation scheme (Ishengoma and Athuman, 2018).…”

Section: Introductionmentioning

confidence: 99%

Environmental impact assessment and efficiency of cotton: the case of Northeast Iran

Mahdei

Esfahani²,

Lebailly

et al. 2022

Environ Dev Sustain

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Cotton is one of the important crops that play an important role in creating a livelihood for rural people in many parts of Iran. Cotton production necessitates a large amount of resources (e.g., fossil energy and agrochemicals, all of which have the potential to damage the environment in various ways). The purpose of the current study was to evaluate the environmental effects of cotton production in the South Khorasan Province of Iran. For this purpose, Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) techniques have been applied to investigate the environmental impacts of cotton production. LCA is a practical method to evaluate the environment on the product flow, in which all aspects of the product life cycle are examined by a comprehensive approach. Furthermore, combining the LCA method with other managerial strategies such as DEA could allow researchers to provide decision-makers with more practical and interpretable data. The findings of the efficiency test showed that the average technical efficiency, pure technical efficiency, and scale efficiency were 0.81, 0.92, and 0.87, respectively. Respiratory inorganics (i.e., respiratory effects resulting from winter smog caused by emissions of dust, sulfur, and nitrogen oxides to air) posed the greatest environmental burden in cotton production, followed by non-renewable energy, carcinogens, and global warming. In addition, the highest effects were on human health, and then, on resources and climate change. Energy, on-system pollution, and waste played a crucial role in the environmental impacts of cotton processing. This study suggests improving farmers' knowledge toward the optimum application of chemical fertilizers, or their substitution with green fertilizers, which reduces the environmental effect of growing cotton in the area.

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“…Areas, where IoT can play a significant role in increasing prosperity and reducing poverty in Africa, through enhancing basic services and sectors such as agriculture and healthcare, were discussed in [47]. The potential of IoT to reduce poverty in rural communities in South Africa and Zambia were investigated in [12] with emphasis on agriculture while some few areas where smart farming can be applied to create a direct impact on farmers in SSA region were evaluated in [11].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Computer Networks

Kuaban

Czekalski²,

Molua

et al. 2019

Communications in Computer and Information Science

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The Internet of Things is paving the way for the transition into the fourth industrial revolution with the mad rush of connecting physical devices and systems to the internet. IoT is a promising technology to drive the agricultural industry, which is the backbone for sustainable development especially in developing countries like those in Africa that are experiencing rapid population growth, stressed natural resources, reduced agricultural productivity due to climate change, and massive food wastage. In this paper, we assessed challenges in the adoption of IoT in developing countries in agriculture. We propose a cost effective, energy efficient, secure, reliable and heterogeneous (independent of the IoT protocol) three layer architecture for IoT driven agriculture. The first layer consists of IoT devices and it is made up of IoT driven agriculture systems such as smart poultry, smart irrigation, theft detection, pest detection, crop monitoring, food preservation, and food supply chain systems. The IoT devices are connected to the gateways by low power LoRaWAN network. The gateways and local processing servers co-located with the gateways create the second layer. The cloud layer is the third layer, which exploits the open source FIWARE platform to provide a set of public and free-to-use API specifications that come along with open source reference implementations.

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Internet of Things to Improve Agriculture in Sub Sahara Africa - A Case Study

Cited by 7 publications

References 0 publications

A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

Environmental impact assessment and efficiency of cotton: the case of Northeast Iran

Computer Networks

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