Detecting and Classifying Olive Leaf Pests and Diseases Using Optimal Deep Learning Techniques
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Olive Tree Pests, Olive Tree Diseases, Deep Learning, Artificial intelligence, Artificial Convolutional Networks
Abstract
Agricultural advancements in technology and AI are transforming disease and pest control, boosting crop productivity. Classifying olive tree ailments is a challenge. Traditional methods are insufficient, requiring farmers and even experts to invest significant time and effort in manual identification. This paper explores CNNs for olive disease and pest classification. We experimented on two datasets: a local one from Libya with healthy leaves, olive fly, and jasmine moth (4,170 samples) and a public GitHub dataset with healthy leaves, rust mite blight, and peacock eye disease (6,961 samples). We compared pre-trained and untrained CNN models, finding the pre-trained Xception model achieved the highest local data accuracy (99%). Interestingly, the best-untrained model also excelled on local data (95%). The study further explored the impact of optimization algorithms (Adam and SGD), with Adam consistently achieving superior accuracy on both datasets.
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