Vinasetan Ratheil HOUNDJI


E-Agriculture

Sorghum Yield Prediction using Drones and Machine Learning

Reference paper: Sorghum Yield Prediction using Machine Learning, J. G. N. Zannou, V. R. Houndji. International Conference on Bio-engineering for Smart Technologies (BioSMART'2019), Paris, France.

   

Agriculture is one of our greatest assets. Estimation of a future agricultural production is an important challenge for farmers. In this work, we propose a system based on artificial intelligence to estimate farm yields. We use drones and Artificial Intelligence (in particular Machine Learning algorithms) to estimate an agricultural yield production. For the first experiments, we focus on the Sorghum. Sorghum is the fifth largest cereal with regards to volume of production, after maize, rice, wheat and barley. It is the main cereal for many low-income populations living in the semi-arid tropics of Africa and Asia. We propose and experiment an approach with Machine Learning to estimate the agricultural yield of a farmland of Sorghum before the harvest. These algorithms allow us 1) to detect the different ears of Sorghum on an image and 2) to estimate their weight. On our dataset, we obtain an average accuracy of 74,5% for the detection of sorghum.



Academic performance prediction

AmonAI : a students academic performances prediction system using machine learning algorithms

...

   

To ensure a great training of the students, it is important for them to receive adequate support to improve and succeed in their studies. Unfortunately several conditions, the highest number of students mainly, make more difficult to monitor students. To reduce the failure rate of students we iniate this project. It will then dive into the application of machine learning techniques on the available data to make the prediction of academic performances of the students in the LMD system. We work on AmonAI, a system that allows to anticipate their results in order to reduce their failure as much as possible by taking appropriate decision. The system makes the prediction of the students academic performances through classification and regression in each teaching unit of a semester and provide visualizations based on these predictions.



E-Health

Coming soon...

...

   

Coming soon...



Natural Language Processing

Coming soon...

...

   

Coming soon...