An application of machine learning technique in forecasting crop disease

In the recent years, Big Data Analytics and Machine Learning techniques are playing an increasingly key role in the agriculture sector in order to tackle the increasing challenges due to the climate changes which are causing serious damage production. The analysis of environmental, climatic and cultural factors allows to establish the irrigation and nutritional needs of crops, forecast crop disease, improve crop yield, as well as improve the quantity and the quality of agricultural output while using less input. Potato late blight is considered one of the most devasting disease world over, including Sardinia. Unexpected epidemics can result in significant economic and yield losses. In this paper, we describe the test conducted using the DSS LANDS in order to predict potato late blight disease in Sardinia. The object of the study was to investigate if regional weather variables could be used to predict potato late blight risk in southern Sardinia using a Machine Learning approach. The disease severity is predicted using Feed-forward Neural Network and Support Vector Machine Classification based on meteorological parameters provided by ARPAS weather stations. The prediction accuracy for ANN was 96% and for SVM Classification was 98%.