The purpose of the complete fully connected structure is to tune the weight parameters wa,b(q) to produce a stochastic likelihood representation of each class found on the activation maps created by the combination of convolutional, ReLU, and pooling layers. The repeated implementation of these two operations generates an output vector of class scores, which assists as the classification prediction. In addition, a cost function is employed to reduce the classification error. In this study, we applied a soft-max cost function in order to generate a probability output in the range of 0 to 1 that can automatically be converted to class values. We implemented training and testing of the V-CNN in Python, using the Keras, TensorFlow, NumPy, SciPy, and Scikit-learn Python packages [30–33]. The training data were partitioned into fixed batch sizes (10) of the input feature vectors. All the batches of input feature vectors were evaluated in 20 epochs, which means that the procedure ran 20 times on the entire training data set. The V-CNN was trained with the TensorFlow framework at a learning rate of 0.001, using the Adam optimizer for cross-entropy minimization. The scale of the samples were evaluated using train_test_split () function and random_state parameter with different seed values to ensure the reproducibility of the classification performance of the model. The accuracy and loss values were evaluated to show the fitness of the model. The performance of the proposed V-CNN model for classifying the colony quality was estimated using the morphological feature, textural feature, and combined morphological and textural features (hereafter referred simply to as “combined features”).