Deep Learning-Based Bayoud Disease Recognition Using Palm Fronds Database From Biskra Region, Algeria

Early Recognition of plant diseases is required to provide appropriate treatments and protect agricultural production. Automated image detection techniques can provide early, fast, accurate, and inexpensive detection results. However, scientific research on date palm diseases is even rare due to the limited number of databases, including the Bayoud disease. In this paper, a new database, Biskra Algeria Palm Fronds Database (BAPFD), containing 400 images of date palm fronds is proposed, it includes images of both healthy and Bayoud disease-affected fronds. The Deep Learning (DL) technique is then used to classify our database and recognize Bayoud disease in date palm fronds. The performance of our approach is measured in terms of accuracy, loss, precision, and recall. The mentioned experimental results show that the proposed approach based on DL achieved high performance in recognizing Bayoud disease in date palm fronds, up to 100% for precision and recall and in order of 4×10
^–4
for loss. The results show also that the model is capable to accurately classify infected and not infected fronds images even with a small percentage of bayoud. This demonstrates that our database contains significant information concerning bayoud diseases, which enables DL to design an accurate model that aids in correctly classifying palm fronds and identifying Bayoud disease. We further acknowledge the effectiveness of the DL approach in the recognition of healthy and not healthy fronds.