Increasing agricultural efficiency through machine learning

Machine learning applied to agriculture optimises processes and land use, analyses harvest data and improves the precision of agricultural machinery. And that's just the beginning.

Jerome Treboux, a doctoral student at the Institute of Information Systems at the HES-SO Valais-Wallis in Sierre (Switzerland), presented his work in this field at this year's GIoTS conference, which was held virtually.

His paper "Towards Retraining of Machine Learning Algorithms: An Efficiency Analysis Applied to Smart Agriculture" compares the efficiency of machine learning algorithms to detect an object in an image. The experimental set-up shows that a properly tuned Random Forest is equal to or better than the Deep Learning approach and increases the speed of the relearning process by a factor of about 400.