Plant Leaf Disease Detection and Classification Using Segmentation Encoder Techniques

Aims Agriculture is one of the fundamental elements of human civilization. Crops and plant leaves are susceptible to many illnesses when grown for agricultural purposes. There may be less possibility of further harm to the plants if the illnesses are identified and classified accurately and early on. Background Plant leaf diseases are typically predicted and classified by farmers tediously and inaccurately. Manual identification of diseases may take more time and may not accurately detect the disease. There could be a major drop in production if crop plants are destroyed due to slow detection and classification of plant illnesses. Radiologists used to segment leaf lesions manually, which takes a lot of time and work. Objective It is established that deep learning models are superior to human specialists in the diagnosis of lesions on plant leaves. Here, the “Deep Convolutional Neural Network (DCNN)” based encoder-decoder architecture is suggested for the semantic segmentation of leaf lesions. Methods A proposed semantic segmentation model is based on the Dense-Net encoder. The LinkNet-34 segmentation model performance is compared with two other models, SegNet and PSPNet. Additionally, the two encoders, ResNeXt and InceptionV3, have been compared to the performance of DenseNet-121, the encoder used in the LinkNet-34 model. After that, two different optimizers, such as Adam and Adamax, are used to optimize the proposed model. Results The DenseNet-121 encoder utilizing Adam optimizer has been outperformed by the LinkNet-34 model, with a dice coefficient of 95% and a Jaccard Index of 93.2% with a validation accuracy of 97.57%. Conclusion The detection and classification of leaf disease with deep learning models gives better results in comparison with other models.