A dubas detection approach for date palms using convolutional neural networks

The Dubas bug (Db) is a deadly pest that affects palm and agricultural crops; however, early automatic identification of frond diseases of this type can thus help reduce human effort and reduce economic losses. Traditional methods of detection rely on human vision and hands to spot and categorise the palms affected, while more recent research has begun applying deep learning networks due to their effectiveness in classification tasks. This paper presents a strategy of automatic recognition of Dubas blight based on identification of diseased palms. Data was collected in natural climatic conditions using a drone camera and several pre-processing steps were applied. In particular, pre-sets were created by slicing the images into small parts and using Lifting Wavelet transformations (LWT) to shrink the data in size; this was then used to train a convolutional neural network (CNN) with the assistance of pre-trained Xception, InceptionV3, DenseNet121, and ResNet101 models with different parameters. Dropout and data augmentation was used to avoid overfitting, and the highest classification accuracy was determined to be that offered by the Xception model, with experimental results suggesting that this developed more than 99% accuracy.