Pest Early Detection in Greenhouse Using Machine Learning

Greenhouses are considered to be a favorable artificial environment separated from the outside. However, pests can still exist by the same plant sources that bring the pathogen. The conditions and abundant food in a greenhouse provide a stable environment for the pest development. Normally, the natural enemies that serve to keep pests under control outside are not present in the greenhouse, pest situations often develop in this indoor environment more rapidly and with greater severity than outdoors. Early detection and diagnosis of pests and diseases are key to managing greenhouse pests as well as selecting and applying appropriate pesticides when needed. The aim of this invention is to develop an intelligent pest early detection system using a convolutional neural network in the greenhouse. By using a pre-trained disease recognition model, we were able to perform deep transfer learning to produce a network that can predict with the precision above 90%.