Detecting clustered fruits using a hybrid of convolutional neural networks and machine learning classifiers – Case study

Abstract

The last and most important procedure during fruit or vegetable cultivation is harvesting. One of the basic chal-lenges during grape growing is the use of agriculture 4.0 machines (including robots) during harvesting which is associated with the need for quick identification of berries or grape clusters. In this work, a convolutional neural network (CNN) and a machine learning classifier were suggested for the identification (detection) of individual grapes. A free data set (Iceland) was used, which included two classes with different lighting conditions and berry sizes. The integrated method included two types of deep learning models, i.e. CNN (AlexNet and GoogleNet). CNN models were used to obtain discriminative deep features from different layers. The combination of two models AlexNet-Fc6 and SVM-Cubic yielded the highest accuracy, sensitivity and precision (mean ± standard deviation)% of 99.4 ± 0.13, 99.2 ± 0.14 and 99.49 ± 0.19, respectively. The developed grape detector can be used for practical applications requiring high accuracy, e.g. in the process of yield estimation or detection of grape diseases.