A review of neural networks in plant disease detection using hyperspectral data

© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data

Predicting plant environmental exposure using remote sensing

Wheat is one of the most important crops globally with 776.4 million tonnes produced in 2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend £0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase which makes it difficult to detect before symptoms develop, after which point fungicide intervention is ineffective. In the second chapter of my thesis I use hyperspectral sensing and imaging techniques, analysed with machine learning to detect and predict symptomatic Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of symptomatic Z. tritici infection and could facilitate precision agriculture methods, to use in the subsequent growing season, that optimise fungicide use and increase yield. In the third chapter of my thesis, I develop a multispectral imaging system which can detect and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the nitrogen source applied. Currently, plants are treated with nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing, multispectral imaging, and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. This suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in plant leaf reflectance detectable via machine learning of multispectral data but not the naked eye. These results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture. In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral imaging and machine learning image analysis developed in the third chapter to detect asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of all stages of Z. tritici infection and could facilitate precision agriculture methods to be used during the current growing season that optimise fungicide use and increase yield.Open Acces

The impact of the spectral dimension of hyperspectral datasets on plant disease detection

Precision Agriculture as an information based approach requires explicit spatial information about the within field heterogeneities for site-specific applications. Thus, the usage of cost-intensive agrochemicals and the impact on the environment can be significantly reduced. Spectroscopic approaches are thereby a promising tool for providing fast and precise information on a local to regional level. In this thesis, the impact of hyperspectral near-range and remote sensing data for crop stress detection will be observed since spectroscopic approaches are of great interest for Precision Agriculture. Two greenhouse experiments and three field experiments were conducted with spectroscopic measurements to examine possibilities and limitations of hyperspectral data. The data were acquired using a near-range non-imaging spectrometer (ASD Fieldspec 3) and a near-range imaging spectrometer (ImSpec V10E) in the greenhouse, or were acquired by the airborne sensor systems HyMapTM, ROSIS or AISA for the field experiments. The methodical foci thereby are the improvement of binary detection approaches, discriminating 'vital' and 'infected' wheat stands or parts of wheat stands, and quantification approaches to estimate disease severities at canopy level. This thesis examines the spectral dimension of hyperspectral data for crop stress detection by assessing data redundancy and defining spectral necessities. Different feature selection methods were tested for their suitability in reducing the high amount of spectral data without losing significant information. Conventional classification approaches and recent developments, such as support vector machines for classification (SVM), were thereby tested based on the identified spectral subsets to assess the status of different wheat stands. By focusing on phenomenon-specific spectral bands, stressed wheat stands could successfully be identified with high accuracies. Using optimal band combinations could even increase classification accuracies. The results showed that not the entire spectrum of hyperspectral data is necessary for the detection of fungal infections in wheat. These findings are particularly interesting for future spectral sensor design and remote sensing missions that are aiming at the provision of spatial information for agricultural practice. The ability of hyperspectral data in quantifying the severity of fungal diseases was observed. Site-specific fungicide treatments based on application maps are technically possible and doses can be adjusted if the maps provide information about the health status of the crops. Crop growth anomalies caused by fungal infections were observed, which differed significantly within one field. The derivation of disease severities based on hyperspectral near-range and remote sensing data were examined using classification approaches and support vector machines for regression (SVR). Fungal infections of wheat stands in the greenhouse and wheat stands in the field could be quantified with a high level of certainty. The results are very promising and the findings may be of great interest for agricultural questionnaires and automatic phenotyping approaches, since the presented approaches are fast and non-destructive. Spatial maps with continual disease severity data could be derived, which can be used to generate application maps for agricultural practice. Since the study shows that a reduction of hyperspectral data to a few but specifically selected spectral bands can improve the classification accuracies or regression analyses, a preliminary feature selection should be considered when working with hyperspectral remote sensing data. Agricultural and geographical approaches that are based on spatial-spectral information may thus profit from a faster and more reliable extraction of information. The study shows great advantages of the usage of hyperspectral imaging data but also the necessity of advanced and innovative analyzing methods

28th Fungal Genetics Conference

Full abstracts from the 28th Fungal Genetics Conference Asilomar, March 17-22, 2015

Feeling the Heat: Investigating the dual assault of Zymoseptoria tritici and Heat Stress on Wheat (Triticum aestivum)

As a result of climate change, field conditions are increasingly challenging for crops. Research has shown how elevated temperatures affect crop performance, yet the impact of temperature on host-pathogen relationships remains unknown. Understanding the effects of combined abiotic and biotic stresses on crop plants and the plant-microbial interaction is crucial in developing strategies to improve crop stress tolerance and manage diseases effectively. Lipids sense, signal, and mitigate temperature elevation effects, and lipid remodelling plays a key role in the plant and fungal response to heat stress. Our study uses a systems approach to examine the Z. tritici wheat model system, combining transcriptomics, lipidomics, and phenotyping to decipher the impact of high-temperature stress on the plant-pathogen interaction. Microscopy in vivo and RNA-Seq analyses confirmed that Z. tritici responds to high-temperature treatments with morphological and transcriptomic changes. Temperature-related configuration of the transcriptome was associated with the accessory chromosomes and expression of ‘accessory’ pan-genome-derived genes. Metabolism-related gene expression predominated, indicated by GO enrichment and analysis of KOG classes, and large-scale lipid remodelling was likely given the proportion of lipid transport and metabolism-related expression changes in response to temperature. Changes in lipid content and composition were then validated by LC-MS analysis. Heat-responsive fungal genes and pathways, including scramblase family genes, are being tested by reverse genetics to ascertain their importance for fungal adaption to elevated temperatures. Elevated temperature schemes were applied to wheat to study the impact of combined stress on the plant-pathogen interaction, based on long-term climate data from Rothamsted Research, using transcriptomic, lipidomic and phenotypic analyses. Comparing non-infected and infected wheat plants under typical and elevated temperatures. Our initial analysis of the transcriptomic data indicates a delay in the development of Z. tritici, followed by its adaptation to the warmer environment. Once the infection was established, the fungus exhibited resilience to the impact of higher external temperatures. Our results indicate that temperature elevations associated with climate change directly impact plant-pathogen interactions. Furthermore, the study demonstrates a need for further detailed understanding to sustain crop resilience

27th Fungal Genetics Conference

Program and abstracts from the 27th Fungal Genetics Conference Asilomar, March 12-17, 2013

27th Fungal Genetics Conference

Program and abstracts from the 27th Fungal Genetics Conference Asilomar, March 12-17, 2013