Determining apple fruit harvest time using color images and deep learning

Identifying the growth stages of fruits in orchards is an important factor in improving the quantity and quality of the final product. Having such information helps the growers to apply the appropriate treatment for each growth stage and also to get a proper understanding of the fruit harvesting time, which may change due to changing weather conditions. Therefore, in the present study, color images of golden apples were used to estimate the weeks remaining to the harvest time. The EfficientNetB1 model was used to classify images taken from different weeks of apple fruit development using deep learning technology and convolutional neural networks. The data were divided into three categories: training (60%), validation (20%), and test (20%). Also, two pre-processing processes, i.e. data normalization and data augmentation, were used to obtain better results. Finally, Nadam optimizer and categorical_crossentropy cost function were considered in creating the model. The results showed that the developed model would have a good ability to classify input images. The value of the correlation coefficient (R) for training, validation, and test data was 0.86, 0.88, and 0.87 respectively. Also, the ability of the model to classify different classes was presented using precision, recall, and f1-score parameters for each class, according to which some classes achieved 100% accuracy. Consequently, the obtained results can be used as a platform for the development of harvesting robots, mobile apps, as well as aerial imagery systems using drones, etc. to fulfill various purposes in precision agriculture, and in particular, precision horticulture.