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In a country's economy, agriculture plays a very vital role. Agriculture's yield and production are
reduced by plant diseases, resulting in significant economic losses and instability in the food
market. In plants, the citrus fruit crop is one of the most important agricultural products in the
world, produced and grown in around 140 countries. It has a lot of nutrients, such as vitamin C.
However, due to pests and diseases, citrus cultivation is widely affected and suffers significant
losses in both yield and quality. The majority of plant diseases exhibit visible symptoms, and the
accepted method used today is for a skilled plant pathologist to detect the diseases by examining
affected plant leaves under a microscope, which is a costly and time-consuming method. During
the last decade, computer vision and machine learning have been widely adopted to detect and
classify plant diseases, providing opportunities for early disease detection and bringing
improvements to agricultural production. The early detection and accurate diagnosis of plant
diseases are essential for reducing their spread and damage to crops. In this work, we presented an
automatic system for early detection and recognition of citrus plant diseases based on a deep
learning (DL) model to improve accuracy and reduce computational complexity. The most recent
transfer learning-based models were applied to our dataset in order to increase classification
accuracy. In this work, we successfully proposed a CNN-based pre-trained model (EfficientNetB3,
ResNet50, MobiNetV2, (InceptionV3) for the identification and classification of citrus plant
diseases using transfer learning. In order to assess the performance of the model, we found that the
transfer of an EfficientNetb3 model led to the highest training, validating, and testing accuracies,
which were 99.43%, 99.48%, and 99.58%, respectively. The proposed CNN model exceeds other
cutting-edge CNN network architectures developed in earlier literature in the identification and
categorization of citrus plant diseases.