Abstract
Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food security and the incomes of farmers. Citrus plants, recognized for their nutritional benefits and economic significance, are especially vulnerable to diseases such as citrus greening, Black spot, and Citrus canker. Due to technological advancements, image processing and Deep learning algorithms can now detect and classify plant diseases early on, which assists in preserving crop health and productivity. The proposed work enables farmers to identify and visualize multiple diseases affecting citrus plants. This study proposes an efficient model to detect multiple citrus diseases (canker, black spot, and greening) that may co-occur on the same leaf. It is achieved using the RSL (Residual Squeeze & Excitation LeakyRelu) Linked-TransNet multiclass segmentation model. The proposed model stands out in its ability to address major limitations in existing models, including spatial inconsistency, loss of fine disease boundaries, and inadequate feature representation. The significance of this proposed RSL Linked-Transnet model lies in its integration of hierarchical feature extraction, global context modeling via transformers, and precise feature reconstruction, ensuring superior segmentation accuracy and robustness. The results of the proposed RSL Linked-TransNet architecture reveal average values of 0.9755 for accuracy, 0.0660 for loss, 0.9779 for precision, 0.9738 for recall, and 0.9308 for IoU. Additionally, the model achieves a mean F1 score of 0.7173 and a mean IoU of 0.7567 for each disease class in images from the test dataset. The segmentation results are further utilized to identify the prime disease affecting the leaves and evaluate disease severity using the prime disease classification and severity detection algorithm.