The CSSO algorithm is designed by the integration of Competitive Swarm Optimizer (CSO) and Shuffled Shepherd Optimization algorithm (SSOA) for selecting the optimal path. The Leaf disease detection process is p
This inefficiency not only restricts the practical applicability of these models but also significantly hampers the deployment of disease detection systems in large-scale agricultural environments. To overcome this challenge, further optimization of the algorithm structure and improvements in model inference ...
10. This figure demonstrates that the proposed algorithm accurately detects and identifies diseased leaves by constructing a perfect bounding box. Deep Learning is gaining popularity among researchers for precision agriculture applications such as disease detection, weed control, fruit recognition, etc45,46...
YOLO is an algorithm of object detection that partitions images into a grid structure. Every grid cell has the responsibility of identifying objects. As a result, we decided to convey the current work using the YOLOv8. As far as we are aware, no study has used YOLOv8 to detect disease ...
algorithm for segmentation efficiency. The framework utilized an enhanced base network, specifically a pre-trained INC-VGGN model, for precise disease detection and classification. Pre-trained weights and features were moved to the newly developed neural network for the task of plant disease detection...
Tomato disease damage Full size image In recent years, machine learning techniques have been widely utilized to improve the accuracy of vegetable disease detection. For instance, Madhav et al. utilized support vector machine (SVM) algorithms to identify fruit diseases and reduce pesticide usage [4]...
In this case, the ground truth box refers to the boxes annotated around each instance of tea leaf disease in the training dataset. During the training, the YOLOv7 algorithm used these ground truth boxes to learn how to detect objects of similar classes in new images. The bulk of the ...
Therefore, this paper introduces a novel method for the detection of plant leaf diseases. The method is divided into two parts: image segmentation and image classification. First, a hue, saturation and intensity-based and LAB-based hybrid segmentation algorithm is proposed and used for the disease...
In contrast, deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks. This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection, such as ...
Our study attempts to duplicate the algorithm that offers the most precise forecasts for the detection of plant leaf diseases. The results are expected to be utilized to determine the optimal method for creating a smart system that can detect leaf illnesses. The main contribution in this work is...