Format:
1 Online-Ressource (12 p)
Content:
World and African agricultural production, in particular, are of increasing concern to the major international organizations in charge of nutrition. The World Food Programme has reported that high population growth worldwide and in Africa in particular in recent years is leading to increased food security. Moreover, farmers and agricultural decision-makers need advanced tools to help them make quick decisions that will impact the quality of agricultural yields. A great phenomenon of climate change has been observed in the last decades around the world. An impact of this climate change has been observed on the quality of agricultural production. The arrival of big data technology has led to new powerful analytical tools like machine learning which has proven itself in many areas such as medicine, finance, and biology. In this work, we propose a prediction system based on machine learning to predict 6 crops yield namely: Rice, Maize, Cassava, Seed cotton, Yams, and Bananas at the country-level in the area of West African countries throughout the year. We combined climates data, weather data, agricultural yields, and chemical data to help decision-making and farmers to predict the annual crop yields in their country. We used a decision tree, multivariate logistic regression, and k-nearest neighbor models to build our system. We had promising results with both models when using three machine learning models. we found that decision tree model(𝑅2 = 95,3%) performs better than the K-Nearest Neighbor model(𝑅2 = 93, 15%) and the logistic regression(𝑅2 = 89, 78%)
Language:
English
DOI:
10.2139/ssrn.4003105
Bookmarklink
