Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 or relative humidity approximately 90%, mold-contaminated peanuts have considerable likelihood be infected Aflatoxins. Aflatoxins are known highly carcinogenic, posing danger humans livestock. this work, we proposed new approach detection at an early stage. The employs the optical coherence tomography (OCT) imaging technique error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using pre-trained Deep Convolutional Neural Network (DCNN). To end, uncontaminated were scanned create data set OCT images used training evaluation ECOC-SVM model. Results showed that capable detecting respective accuracies 85% 96% after incubation periods 48 96 h.