Grape disease detection using dual channel Convolution Neural Network method

Grapes are one type of fruit that is usually used to make grape juice, jelly, grapes, seed oil and raisins, or be eaten directly. So far, checking for disease in grapes still done manually, by the leaves experts. This method certainly takes a long time considering extent vineyards must evaluated. To solve this problem, it necessary apply detecting disease, so can help common people detect disease. research will use Dual-Channel Convolutional Neural Network method. The process using DCCNN begin with extraction from input image Gabor Filter After that, Segmentation Based Fractal Co-Occurrence Texture Analysis extract features, color, texture extracted leaves. result number datasets affect accuracy results identification However, more cause execution take longer. Changes angle frequency values at testing reduce test results. conclusion study leaf