Fall Armyworm Damaged Maize Plant Identification using Digital Images

Abstract

The objectives of precision agriculture are profit maximisation, agricultural input rationalisation and environmental damage reduction, by adjusting the agricultural practices to the site demands. The fall armyworm ( Spodoptera frugiperda) is one of the most important maize pests in Brazil and the use of insecticide is the main control method. It is believed that site-specific control can be implemented by using a machine vision system. The objective of this work was to develop and evaluate an algorithm at simplified lighting conditions for identifying damaged maize plants by the fall armyworm using digital colour images. Images of damaged and non-damaged maize plants were taken in eight different stages and in three different light intensities. The proposed algorithm had two stages: the processing and the image analysis. During the first stage, the images were processed to create binary images where the leaves were segmented from the other pixels. At the second stage, the images were subdivided into blocks and classified as ‘damaged’ or ‘non-damaged’ depending on the number of objects found in each block. The algorithm correctly classified 94·72% of 720 images.