Enhancing Farmers Productivity Through IoT and Machine Learning: A State-of-the-Art Review of Recent Trends in Africa
| Autor: | Assitan Traore, Awa Diattara, Chimango Nyasulu, Cheikh Ba |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Estimation
business.industry Computer science media_common.quotation_subject Yield (finance) Machine learning computer.software_genre ComputingMilieux_GENERAL Agriculture Sustainability Quality (business) Artificial intelligence Precision agriculture Agricultural productivity business Productivity computer media_common |
| Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030905552 |
| DOI: | 10.1007/978-3-030-90556-9_10 |
| Popis: | Agriculture is considered as the main source of food, employment and economic development in most African countries and beyond. In agricultural production, increasing quality and quantity of yield while reducing operating costs is key. To safeguard sustainability of the agricultural sector in Africa and globally, farmers need to overcome different challenges faced and efficiently use the available limited resources. Use of technology has proved to help farmers find solutions for different challenges and make maximum use of the available limited resources. Internet of Things and Machine Learning innovations are benefiting farmers to overcome different challenges and make good use of resources. In this paper, we present a wide-ranging review of recent studies devoted to applications of Internet of Things and Machine Learning in agricultural production in Africa. The studies reviewed focus on precision farming, animal and environmental condition monitoring, pests and crop disease detection and prediction, weather forecasting and classification, and prediction and estimation of soil properties. |
| Databáze: | OpenAIRE |
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