Detection of Almond Leaf Scorch with Artificial Intelligence for the Agriculture Industry

Abstract

Almond Leaf Scorch Disease (ALSD) poses a signfficant threat to almond production worldwide. Deep learning algorithms have potential to enable growers of all scales to identify infected trees using photos captured with a smart camera. This approach diagnoses faster than humans without requiring expert knowledge, does not require third-party laboratory testing (PCR), and has higher accuracy than multispectral satillite imaging. Data was collected for this work by long-term observation of Prunus dulcis L. for symptoms and validated ALSD with PCR testing. 515 images were collected. We experimented with five pre-trained convolutional neural networks: DenseNet 201, Inception V3, ResNet 101 V2, VGG 19, and Xception. DenseNet201 demonstrates that it is possible to detect ALSD versus healthy control, Red Leaf Blotch, and various other diseases with an 88.72% accuracy. These results show promise for cheap and fast detection of the disease. Future work will focus on imaging and detection of the disease in vivo.