DDetection of Callosobruchus maculatus F. with image processing and artificial neural network

Due to the high speed and accuracy of intelligent pest detection in warehouse products, in this study, the detection of chickpea four-point beetle pest was simulated by image processing technique using artificial neural networks. To prepare the images, a glass box was prepared and the chickpea seeds were placed in the center of the box. The light was then illuminated from all six sides and photographed with a digital camera from all sides. The image properties were then extracted by Wavelet Gabor using MATLAB software and applied to the ANN as training data. To train the network, 69 images of chickpeas damaged and 59 healthy chickpeas were used. Then, to evaluate the network, a set of data that did not play a role in network training as test data was applied to the network and its results were evaluated. In this study, Perceptron and Elman neural networks were used which had better results than Elman network. The proposed method was able to detect the high rate of damaged with 6.17% non-detection error and 4.86% error-detection error. After image processing by the neural network and detection of damage points, the amount of crop damage was also calculated. For this purpose, the level of detected damage was calculated and divided by the area of total area of chickpea seed and percentage of damage. After identifying the injury sites, the damage was estimated 2.3% in the studied images.