A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition.However,it has limited interpretability for deep features.With the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant diseases.However,irrelevant and redundant features lead to high dimensionality.In this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease detection.SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features.To verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic algorithms.Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage.Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification.This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

DC2Net:An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning

Asian soybean rust(ASR)is one of the major diseases that causes serious yield loss worldwide,even up to 80%.Early and accurate detection of ASR is critical to reduce economic losses.Hyperspectral imaging,combined with deep learning,has already been proved as a powerful tool to detect crop diseases.However,current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels,leading to the fact that the detection accuracy of current models remains further improvement.