  Fruit ripeness estimation models have for decades depended on spectral index
features or colour-based features, such as mean, standard deviation, skewness,
colour moments, and/or histograms for learning traits of fruit ripeness.
Recently, few studies have explored the use of deep learning techniques to
extract features from images of fruits with visible ripeness cues. However, the
blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible
traits of ripeness when mature and therefore poses great difficulty to fruit
pickers. The mature blackberry, to the human eye, is black before, during, and
post-ripening. To address this engineering application challenge, this paper
proposes a novel multi-input convolutional neural network (CNN) ensemble
classifier for detecting subtle traits of ripeness in blackberry fruits. The
multi-input CNN was created from a pre-trained visual geometry group 16-layer
deep convolutional network (VGG16) model trained on the ImageNet dataset. The
fully connected layers were optimized for learning traits of ripeness of mature
blackberry fruits. The resulting model served as the base for building
homogeneous ensemble learners that were ensemble using the stack generalization
ensemble (SGE) framework. The input to the network is images acquired with a
stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at
wavelengths of 700 nm and 770 nm. Through experiments, the proposed model
achieved 95.1% accuracy on unseen sets and 90.2% accuracy with in-field
conditions. Further experiments reveal that machine sensory is highly and
positively correlated to human sensory over blackberry fruit skin texture.
