THE POTENTIAL FUTURE OF AGRICULTURE FOR SMALL FARMS: SUPERVISED MACHINE-LEARNING SMART IRRIGATION CONCEPT FOR VEGETABLES
Noluthando Gamede, Arnesh Telukdarie, Inderasan Munien, Andre Vermeulen, Uche Onkonkwo
Sustainability is a crucial concept in agriculture and agricultural production. Since there is an intense competitiveness and risk in the sector, technical advancements are essential for improved development and sustainability. Small farms require cost effective solutions to match the standards of bigger producers, which essentially means best yields on crops, both in terms of quantity and quality. It is crucial to consider water crisis, climate change, and quality farm care when designing new solutions for agriculture. This study proposes the automation of farm irrigation systems based on a supervised machine learning model (SVM, Logistic Regression) that is cost-effective and precise response to farming demands. In order to show the practicality of this proposed new technology, an application was developed to represent the model results. The application created was for the user to be able to use the model to predict and control the valves in an irrigation system. This work is a potential solution for small farms in a country like South Africa, where sustainable farming is taking rise.
soil moisture; crops; irrigation; IOT; rainfall; Machine Learning; Logistic Regression; Support Vector Machine