Identification of olive leaf disease through optimized deep learning approach

The production of olives in Saudi Arabia, which accounts for around 6% of worldwide output, is regarded as one of the best in the world. Because olive trees are rain-fed and produced using conventional methods, yields vary greatly each year, which is made worse by viral illnesses and climate change. Therefore, it is necessary to identify plant illnesses early on. Farmers diagnose plant illnesses using conventional visual assessment or laboratory analysis. Diagnosing illnesses affecting olive leaves has been improved with deep learning (DL). To identify and categorize plant illnesses, this research introduces an Optimized Artificial Neural Network (ANN) that analyses the plant's leaf. Data is first integrated for preprocessing, relevant features are extracted, and the Whale Optimization Algorithm (WOA) is used to select necessary features. Then the data is classified using ANN. The ANN classification approach utilizes the feed-forward neural network method (FFNN). ANN is a highly adaptable technology being utilized widely to address various problems. This study applies categorization to exclude possibilities throughout each stage, improving prediction accuracy. Compared to the current model employed for plant disease detection, the suggested model showed a considerable performance increase in Precision, Recall, Accuracy, and F1-measure.