Abstract
Cadra (=Ephestia) cautella (Walker) is a moth that attacks dates from ripening stages while on tree, throughout storage, and until consumption, causing enormous qualitative and quantitative damages, resulting in economic losses. Image-processing algorithms were developed for detecting and differentiating between three Cadra egg categories based on the success of Trichogramma bourarachae (Pintureau and Babaul) parasitization. These categories were parasitized (black and dark red), unparasitized fertile unhatched (yellow), and unparasitized hatched (white) eggs. Color, light intensity, and shape information was used to develop detection algorithms. Two image processing methods were developed based on three randomly selected images and were tested on a larger validation image set of 40 images: (i) segmentation and extractions of color and morphological features followed by Watershed delineation, and is referred to as Algorithm 1 (ALGOL), (ii) finding circular objects by Hough Transformation followed by convolution filtering, and is referred to as Algorithm 2 (ALGO2). ALGOL and ALGO2 achieved correct classification rates (CCRs) for parasitized eggs of 92% and 96%, respectively. Their CCRs for unhatched eggs were 48% and 94%, and for hatched eggs were 42% and 73%, respectively. Regarding parasitized eggs, both methods performed satisfactorily, but, in general, ALGO2 outperformed ALGOL. These results ensure automatic evaluation of the efficiency of biological control of Cadra cautella by the egg parasitoid Trichogramma bourarachae by quantifying the rate of parasitization. The developed detection methods can be used by producers of biocontrol agents for online monitoring of Trichogramma and similar insect natural enemies during mass production and before release against crop pests. Moreover, with few adjustments these methods can be used in similar applications such as detecting plant diseases.