Advanced Disease Monitoring and Severity Quantification for Greenhouse Management Using Multispectral Imaging

Abstract

This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.

Plant phenotyping relevance

トマト葉の病害症状と重症度を、画像・疑似ハイパースペクトル・深層学習で直接推定する方法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper uses two publicly available tomato leaf disease image datasets (Kaggle tomatoleaf; Google Drive dataset) as phenotyping inputs and provides the authors' analysis code (RGB-to-SHSI conversion, VGG-16 feature extraction, GMM/K-means severity pipeline) via a public Colab notebook listed in the Data Availability.