LWDN: lightweight DenseNet model for plant disease diagnosis

Consumers’ Perceptions for an Outdoor Ornamental Plant: Exploring the Influence of Novel Plant Diseases Diagnostics and Sustainable Nurseries Cultivation Management

“…In terms of plant diagnostics, Ali et al [14] provided valuable insights into the use of biosensors (i.e., isothermal amplification, nanomaterials, robotics, lab-on-a-chip devices) as effective emerging tools for early detection of plant pathogens in agriculture, while the conventional laboratory-based methods are costly, time-consuming, and require specialized skills. In addition, Dheeraj et al [15] proposed a novel appropriate technique in their study (i.e., a lightweight dense net model, LWDN), based on the Dense Net 121 architecture, for real-time plant diagnosis on portable and mobile devices with limited computational resources, contributing to sustainable agriculture and food security. In their paper, Sharma et al [16] proposed also a new smart plant leaf disease detection technique (i.e., a deeper lightweight convolutional neural network architecture, DLMC-Net) for several crops for real-time agricultural use on simple leaf images of both healthy and diseased plants, contributing to effective disease management in agriculture.…”