Internet of Things, Big Data Analytics, and Deep Learning for Sustainable Precision Agriculture

Micheni, Elyjoy Muthoni; Machii, Jackson Kipchirchir; Murumba, Julius

doi:10.23919/ist-africa56635.2022.9845510

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…Many studies have indicated that the deployment of I4 can reduce the costs of food production [ 26 , 79 , 141 ].…”

Section: Discussionmentioning

confidence: 99%

“…Before this review paper discusses the above topic of concern in Section 5 , the basic understanding of the agroecosystem [ 165 , 166 , 167 , 168 ], I4 for PA [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 ...…”

Section: Introductionmentioning

confidence: 99%

“…However, not all publications mention the use of I4 directly. However, the elements of I4 are proposed indirectly and are already implemented in PA’s sustainability [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , ...…”

Section: Introductionmentioning

confidence: 99%

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A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

Yap,

Al-Mutairi

2024

Foods

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With the expected colonization of human daily life by artificial intelligence, including in industry productivity, the deployment of Industry 4.0 (I4) in the food agriculture industry (FAI) is expected to revolutionize and galvanize food production to increase the efficiency of the industry’s production and to match, in tandem, a country’s gross domestic productivity. Based on a literature review, there have been almost no direct relationships between the I4—Food-Agriculture (I4FA) Nexus and the agroecosystem. This study aimed to evaluate the state-of-the-art relationships between the I4FA Nexus and the agroecosystem and to discuss the challenges in the sustainable FAI that can be assisted by the I4 technologies. This objective was fulfilled by (a) reviewing all the relevant publications and (b) drawing a conceptual relationship between the I4FA Nexus and the agroecosystem, in which the I4FA Nexus is categorized into socio-economic and environmental (SEE) perspectives. Four points are highlighted in the present review. First, I4 technology is projected to grow in the agricultural and food sectors today and in the future. Second, food agriculture output may benefit from I4 by considering the SEE benefits. Third, implementing I4 is a challenging journey for the sustainable FAI, especially for the small to medium enterprises (SMEs). Fourth, environmental, social, and governance (ESG) principles can help to manage I4’s implementation in agriculture and food. The advantages of I4 deployment include (a) social benefits like increased occupational safety, workers’ health, and food quality, security, and safety; (b) economic benefits, like using sensors to reduce agricultural food production costs, and the food supply chain; and (c) environmental benefits like reducing chemical leaching and fertilizer use. However, more studies are needed to address social adaptability, trust, privacy, and economic income uncertainty, especially in SMEs or in businesses or nations with lower resources; this will require time for adaptation to make the transition away from human ecology. For agriculture to be ESG-sustainable, the deployment of I4FA could be an answer with the support of an open-minded dialogue platform with ESG-minded leaders to complement sustainable agroecosystems on a global scale.

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“…Many studies have indicated that the deployment of I4 can reduce the costs of food production [ 26 , 79 , 141 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

Yap,

Al-Mutairi

2024

Foods

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“…These authors investigated the literature review on digital agriculture. They mainly focused their research on IoT, big data and deep learning techniques [20]. Application fields of each tech are studies.…”

Section: Figure 1 Roadmap Of Industrial and Agriculture Revolution [1...mentioning

confidence: 99%

A Survey on Digital Agriculture in Five West African Countries

Dégila¹,

TOGNISSE²,

Honfoga³

et al. 2023

Preprint

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Agriculture is the primary source of economic growth in many countries in West Africa. It provides food and livelihoods for the population. The purpose of this study is to review the digital agriculture status in five countries, namely Benin, Burkina Faso, Coˆte d’Ivoire, Ghana, and Nigeria. The study consisted of a bibliometric analysis using a database exported from the web of science through a well-defined search string. Additionally, the cases of digital technology deployment in the countries were assessed through a systematic review approach comparing the technologies used in the target countries. Representative articles from the bibliometric review published in the last five years were selected for the comparative analysis. The bibliometric analysis based on 3,249 publications revealed that the research interests have significantly increased since 2014. The top two countries in terms of authors’ nationality were Nigeria and Ghana, respectively. Climate change, Machine Learning (ML) and adoption were the hot topics of discussion. The digital agriculture survey showed that publications in Nigeria were focused on Internet of Things (IoT), Wireless Sensors Networks, blockchain, and Artificial Intelligence (IA) technologies. Ghana also has a strong interest in blockchain, AI, and big data, while Burkina Faso focused on IoT and AI. Cote d’Ivoire and Benin focused only on AI.

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“…Ponnusamy and Natarajan [23] illustrate the use of modern technologies for smart farming. Michele E. et al [24] evaluate smart technologies to determine their impact on agriculture in terms of efficient use of resources, sustainability, and cost reduction. The study also explores the impacts of IoT, Artificial Intelligence (AI), and data analytics in precision agriculture.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

et al. 2022

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An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, timeconsuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and -0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.INDEX TERMS Internet of Things (IoT), machine learning, soil fertility mapping, fertilizer recommendation, support vector machine (SVM), Gaussian Naïve Bayes (GNB), logistic regression (LR), k-nearest neighbor (KNN).

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Internet of Things, Big Data Analytics, and Deep Learning for Sustainable Precision Agriculture

Cited by 11 publications

References 37 publications

A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

A Survey on Digital Agriculture in Five West African Countries

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

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