An Overview of Machine and Deep Learning Technologies Application in Agriculture: Opportunities and Challenges in Nigeria

Umar, Mohammed Aminu; Sani, B. M.; Suleiman, Usman

doi:10.56471/slujst.v4i.273

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Agribusiness Digital Wallet (ADW) system development which leverages blockchain to formalize the interactions and enable seamless data flow in small-scale farming ecosystem Small-scale farming formalization digital trust establishment among the agriculture stakeholders E. Omo Ojugo [45] Big The analysis of the status of digital agriculture across the five countries provides insights into each country's efforts in digital agriculture. There are many ongoing initiatives in these countries to develop the sector.…”

Section: Digital Technologies Survey In the Five West Africa Countriesmentioning

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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Section: Digital Technologies Survey In the Five West Africa Countriesmentioning

confidence: 99%

A Survey on Digital Agriculture in Five West African Countries

Dégila¹,

TOGNISSE²,

Honfoga³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It has also found a place in livestock management. By analyzing data from sensors and cameras to monitor animal behavior, health, and welfare, it can help farmers detect abnormalities, predict disease outbreaks, and optimize feeding and rearing practices (Umar et al, 2022). In general, deep learning has the potential to revolutionize agriculture by enabling data-driven decision-making, increasing efficiency and reducing environmental impact.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5 Model Application in Real-Time Robotic Eggplant Harvesting

Kahya,

Ozduven,

Aslan

2024

JAS

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Deep learning studies in agricultural automation have accelerated in recent years due to its benefits such as increasing product efficiency and reducing labor force. Deep learning is a powerful tool for automation in agriculture with applications ranging from disease identification and crop yield detection to fruit ripeness classification. It helps to automate various processes in agriculture and to perform time-consuming tasks in a shorter time. It quickly processes the data required for robotic harvesting systems and makes it available to the system. In this study, a machine learning study was carried out to be used in the robotic harvesting of eggplant fruit, which is a product that can take time to select and collect in the agricultural area where it is cultivated. YOLOv5 (nano-small-medium and large models) was used for the deep learning method. All training and test metric values of the models were analyzed. It was determined that the most successful model was the model trained with YOLOv5m algorithm on images of 640 × 640 size with 12 Batches and 110 Epochs. The results of the model values were analyzed as “metrics/precision”, “metrics/recall”, “metrics/mAP_0.5” and “metrics/mAP_0.5:0.95”. These are key metrics that measure the detection success of a model and indicate the performance of the relevant model on the verification dataset. It was determined that the metric data of the “YOLOv5 medium” model was higher compared to other models. The YOLOv5m model gave the highest score with F1 score of 85.66%, precision of 95.65%, recall of 96.15%, and mAP at 0.5:0.65 of 78.80%. Hence, it was understood that “Model 3” was the best detection model to be used in robotic eggplant harvesting to separate the eggplant from branch.

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A Survey on Digital Agriculture in Five West African Countries

et al. 2023

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This study focuses on agriculture, which is the main source of economic growth in many West African countries. In recent years, conventional agriculture has undergone a remarkable evolution and digital technologies are widely used for different purposes. While the world is rapidly using advanced digital technologies to grow their agriculture, Africa seems to be lagging behind, especially West Africa. To know how to contribute effectively, it is important to know what is being performed about this issue. The objective of this study is to examine the state of digital agriculture in five countries, namely, Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Nigeria. The study consisted of an analysis of the scientific contributions of these countries and the cases of actual deployment. This is carried out by means of a bibliometric study based on data collected from the Web of Science and a comparative review of the technologies used in the target countries using data from several sources, such as IEEE, Scopus, Science Direct, Google Scholar, etc. The bibliometric analysis based on 3249 publications revealed that research interests have increased significantly since 2014. Climate change, machine learning (ML), and adoption have been the hottest topics of discussion and most of the organizations working on the topic are academic bodies. Moreover, a considerable amount of the scientific input was obtained from Nigeria, which is the most populous of the five countries considered. The survey on digital farming showed that publications in Nigeria that address deployment cases were focused on the internet of things (IoT), wireless sensor networks, blockchain, and artificial intelligence (AI) technologies. In Ghana, practical cases of blockchain, AI, and big data deployment were observed, while Burkina Faso focused on IoT and AI. In Côte d’Ivoire and Benin, the deployment cases generally focused on AI.

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An Overview of Machine and Deep Learning Technologies Application in Agriculture: Opportunities and Challenges in Nigeria

Cited by 5 publications

References 5 publications

A Survey on Digital Agriculture in Five West African Countries

A Survey on Digital Agriculture in Five West African Countries

YOLOv5 Model Application in Real-Time Robotic Eggplant Harvesting

A Survey on Digital Agriculture in Five West African Countries

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