Sensor based pre-symptomatic detection of pests and pathogens for precision scheduling of crop protection products

Skolik, Paul and McAinsh, Martin and Martin, Frank (2020) Sensor based pre-symptomatic detection of pests and pathogens for precision scheduling of crop protection products. PhD thesis, Lancaster University.

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Abstract

Providing global food security requires a better understanding of how plants function and how their products, including important crops are influenced by environmental factors. Prominent biological factors influencing food security are pests and pathogens of plants and crops. Traditional pest control, however, has involved chemicals that are harmful to the environment and human health, leading to a focus on sustainability and prevention with regards to modern crop protection. A variety of physical and chemical analytical tools is available to study the structure and function of plants at the whole-plant, organ, tissue, cellular, and biochemical levels, while acting as sensors for decision making in the applied crop sciences. Vibrational spectroscopy, among them mid-infrared and Raman spectroscopy in biology, known as biospectroscopy are well-established label-free, nondestructive, and environmentally friendly analytical methods that generate a spectral “signature” of samples using mid-infrared radiation. The generated wavenumber spectrum containing hundreds of variables as unique as a biochemical “fingerprint”, and represents biomolecules (proteins, lipids, carbohydrates, nucleic acids) within biological samples. Spectral “biomarkers” generated by biospectroscopy is useful for the discrimination of distinct as well as closely related biomaterials, for various applications. Applications within the plant and crop sciences has been limited to date, especially for the investigation of dynamic biological processes in intact plant tissues. Even more scarce is the application of biospectroscopy to plant interactions with pests and pathogens. To adequately probe in vivo plant-environment interactions, surface structures of intact plant tissues such as leaves, and fruit need to be characterized. Infrared light energy can measure plant epidermal structures including the cuticle and cell wall for chemical profiling of different varieties and cultivars, as well as physiological applications such as plant health monitoring and disease detection. A review of the application of biospectroscopy to study plant and crop biology reveals the potential of biospectroscopy as a prominent technology for fundamental plant research and applied crop science. The application of biospectroscopy for in vivo plant analysis, to elucidate spectral alterations indicative of pest and pathogen effects, may therefore be highly beneficial to crop protection. Highlighting the in vivo analysis capability and portability of modern biospectroscopy, ATR-FTIR provided an invaluable tool for a thorough spectrochemical investigation of intact tomato fruit during development and ripening. This contributes novel spectral biomarkers, distinct for each development and ripening stage to indicate healthy development. Concurrently, this approach demonstrates the effectiveness of using spectral data for machine learning, indicated by classifier results, which may be applied to crop biology. Complementary to monitoring healthy growth and development of plants and crops, is the detection of threats to plant products that compromise yield or quality. This includes physical damage and accelerated decay caused by pests and pathogens. Biochemical changes detected by ATR-FTIR using principal component analysis and linear discriminant analysis (PCA–LDA), for damage-induced pathogen infection of cherry tomato (cv. Piccolo), showed subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato. Sour rot fungus Geotrichum candidum was detected in vivo and characterized based on spectral features distinct from tomato fruit providing biochemical insight and detection potential for intact plant–pathogen systems. Pre-harvest detection of pests and pathogens in growing plants is paramount for crop protection and for effective use of crop protection products. Established previously as an exceptionally versatile bioanalytical sensor, for post-harvest applications, biospectroscopy was applied for the pre-harvest detection of microscopic pathogen Botrytis cinerea fungus infecting developing tomato plants. Compact MIR spectroscopy using ATR mode was adapted for the biochemical investigation of the plant-microbe interaction S. lycopersicum and B. cinerea, on the whole-plant level. Chemometric modeling including principal component analysis, and linear discriminant analysis were applied. Fingerprint spectra (1800-900 cm-1) were excellent discriminators of plant disease in pre-symptomatic as well as symptomatic plants. Spectral alterations in leaf tissue caused by infection are discussed. Potential for automatic decision-making is shown by high accuracy rates of 100% for detecting plant disease at various stages of progression. Similar accuracy rates using similar chemometric models are obtained for fruit development and ripening also. Overall, this research showcases the biospectroscopy potential for development monitoring and ripening of fruit crops, damage and infection induced decay of fruit in horticultural systems post-harvest, complemented by pre-harvest detection of microscopic pathogens. Based on the results from experiments performed under semi-controlled conditions, biospectroscopy is ready for field applications directed at pest and pathogen detection for improved crop production through the mitigation of crop loss.