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Cotton growth is severely threatened by Verticillium wilt, which has a considerable negative effect on yield. For this reason, prompt identification of stress-related disease is critically important. The current work explored how Verticillium wilt stress influences leaf microstructure and physiological metrics (SOD, POD, CAT, MDA, Chla, Chlb, Chlab, Car) in cotton and tested whether hyperspectral imaging could be used to estimate those leaf physiological metrics. Results revealed that disease-induced stress altered the cellular architecture of cotton leaves, leading to the breakdown and disintegration of chloroplasts and mitochondria. Moreover, infected leaves showed significantly higher SOD and POD activities, as well as elevated MDA levels, relative to their healthy counterparts, while chlorophyll and carotenoid concentrations dropped markedly. Furthermore, rapid-assessment models for cotton physiological metrics were built, with the strongest models attaining Rp values spanning 0.809 to 0.975. These models were then employed to generate visual maps of the distribution of physiological signatures over cotton leaf surfaces. The outcomes indicate that the physiological phenotype of cotton leaves can be reliably captured using hyperspectral imaging, providing a solid theoretical foundation for rapid identification of Verticillium wilt stress.

As global food requirements intensify and farming moves decisively toward sustainable and efficient models, establishing precise, forward-looking strategies to protect livestock health is increasingly critical. Within the egg and hatchery domain, hyperspectral imaging (HSI) has established itself as a sophisticated, non-invasive modality for swift, reliable egg quality evaluation, including the detection of embryonic death in chicks. Yet, steep costs and operational barriers relative to conventional RGB approaches present considerable barriers to HSI’s wide-scale uptake. Circumventing these constraints and enabling the broader promise of HSI, the reconstruction of hyperspectral data from ordinary RGB captures is a compelling answer. The current work focuses on producing hyperspectral imagery from RGB inputs to achieve non-destructive, early-stage identification of chick embryo mortality. An initial comparison was made among several image reconstruction frameworks—HRNET, MST++, Restormer, and EDSR—for generating hyperspectral representations of eggs at an early incubation phase. Following this, the reconstructed spectral information was used as input for XGBoost and Random Forest models to separate eggs with living embryos from those with deceased embryos. HRNET outperformed the alternatives in reconstruction quality, recording an MRAE of 0.0955, an RMSE of 0.0159, and a PSNR of 36.79 dB. This work promotes the concept that integrating imaging platforms with intelligent sensing and analytical processing can raise automation levels, strengthen biosecurity measures, and streamline resource use in pursuit of sustainable agriculture 4.0.

Forest health is under mounting pressure from diseases driven by invasive pathogens, and the ability to detect these diseases early and precisely is vital for targeted, prompt forest management. Breakthroughs in spectral imaging and artificial intelligence have paved the way for novel approaches to plant disease identification in both crop and tree species. The present study employed the Dutch elm disease (DED; caused by Ophiostoma novo-ulmi) and American elm (Ulmus americana) pathosystem as a model to test the efficacy of two custom-fabricated, high-precision, portable hyper- and multispectral leaf scanners, paired with machine learning, as innovative instruments for forest disease surveillance. Hyper- and multispectral imagery was collected from leaves of American elm genotypes with varying degrees of disease susceptibility following mock inoculation and inoculation with O. novo-ulmi in controlled greenhouse conditions. Both classical machine learning and cutting-edge deep learning architectures were trained on extracted spectra and directly on spectral image volumes. Deep learning frameworks that leverage combined spectral and spatial information from high-definition spectral leaf images surpass classical machine learning approaches that rely exclusively on spectral data for DED identification. Within the deep learning framework, leaf margins and symptomatic lesions were accentuated as discriminative spatial cues for separating leaves originating from inoculated versus mock-inoculated specimens. Furthermore, the spectral and spatial signature profiles uncovered by the machine learning-based models corresponded to the DED susceptibility levels of the elm genotypes. While supplementary investigations are warranted to gauge transferability across other pathosystems, hyper- and multispectral leaf scanners integrated with machine learning show considerable promise as emerging tools for disease phenotyping in tree species.