Bloomed or non-bloomed fruit tree classification with transfer learning - Tas, Goksu; Bal, Cafer (Taylor & Francis Ltd, 2023)
Agriculture is one of the oldest and most important production sectors in the history of mankind. Agricultural producers suffer losses every year due to the difficulties arising from seasonal conditions. In this study, a deep learning network has been developed to detect bloomed/non-bloomed trees that will support the use of pesticides in order to protect farmers from the damage of frost. For this purpose, a new data set including bloomed/non-bloomed was created over two years. Bloomed or non-bloomed tree detection performances were evaluated using different convolutional neural network (CNN) models with transfer learning on this new data set. Accuracy comparisons are given by including deep learning structures as NASNetMobile, MobileNetV2, ResNet50V2, VGG-16, VGG-19, and InceptionV3, and CNN from Scratch in the evaluation. The results are presented by changing different epochs and optimizer hyperparameter values with different learning rates. In addition, the runtime and parameter values of the examined CNN models were also compared. For the CNN from Scratch model, six different models were obtained by changing the number of convolution blocks and epochs. It was observed that the ResNet50V2 model made the best prediction with 98.65% accuracy after RMSprop optimizer, 10-4 learning rate and 20 epochs of training.
Keywords: Convolutional neural network; deep learning; agriculture; mechatronics; transfer learning