Leveraging CNN and LSTM for Identifying Citrus Leaf Disorders

Citrus plants cannot develop quickly due to illnesses of the fruits and foliage. Therefore, creating a computer-based citrus fruit/leaf disease detection system is crucial. Artificial Intelligence (AI)-based computerized citrus fruit/leaf recognition is now possible because of Machine Learning (ML) and Deep Learning (DL) algorithms. Timely issue detection is essential for growers of citrus plants to avoid impeding their development or even preventing citrus plant mortality. It will be easier for agricultural workers to quickly remove citrus plant illnesses if the software is developed that autonomously recognizes them. In this study, a CNN-LSTM is used to create a detection model of citrus plant illnesses that divides images of diseased citrus leaves into two categories: unhealthy and healthy. Further, hyperparameters like optimizers and Learning rates are adjusted to get maximum accuracy. The results are analyzed, interpreted, and tabulated in this work. The maximum accuracy of the CNN-LSTM model is obtained at a learning rate of 0.001 and by using the Adam optimizer. The maximum accuracy achieved by CNN-LSTM is 96.8% which is better than state-of-the-art approaches. Finally, it is concluded that the CNN-LSTM model is better at identifying citrus leaf diseases efficiently. The CNN-LSTM model is a successful method for differentiating between healthy and diseased citrus leaf images in terms of accuracy findings.