Artificial intelligence technique in crop disease forecasting: a case study on potato late blight prediction

Crop diseases are strongly affected by weather and environmental factors. Weather fluctuations represent the main factors that lead to potential economic losses. The integration of forecasting models based on weather data can provide a framework for agricultural decision-making able to suggest key information for overcoming these problems. In the present work, we propose a new artificial intelligence approach to forecast potato late blight disease in the Sardinia region and a novel technique to express a crop disease risk. The experiments conducted are based on historical weather data as temperature, humidity, rainfall, speed wind, and solar radiation collected from several locations over 4 year (2016–2019). The tests were aimed to determine the usefulness of the support vector machine classifier to identify crop–weather–disease relations for potato crops and the relative possible outbreak. The results obtained show that temperature, humidity, and speed wind play a key role in the prediction.