Unravelling Citrus Huanglongbing Disease

Huanglongbing (HLB) or citrus greening is a disease caused by the unculturable, fastidious, phloem-restrictive, Gram-negative bacterium Candidatus Liberibacter spp. Currently, there are three species linked to the disease. The Asian form associated with Candidatus Liberibacter asiaticus (CLas) is heat-tolerant and can survive well above 30°C. The African (Candidatus Liberibacter africanus) and American forms are heat-sensitive and develop between 22 and 25°C (Candidatus Liberibacter americanus) (Bové, 2006). Huanglongbing is vector-transmitted mainly by the African citrus psyllid Trioza erytreae Del Guercio (Hemiptera: Triozidae) and the Asian citrus psyllid (ACP) Diaphorina citri Kuwayama (Hemiptera: Psyllidae), with two other psyllids also reported as vectors, D. communis Mathur and Cacopsilla citrisuga (Yang & Li) (Hemiptera: Pysllidae). The disease was first described in 1929 and reported in China in 1943. The African variation was reported in South Africa in 1947. The disease was reported in Brazil (São Paulo) in 2004 and the United States (Florida) in 2005. More than 20% of citrus trees in Brazil and 90% in Florida are currently affected, with symptomatic trees present in Texas and California. Huanglongbing is present and affects several citrus-producing countries of Asia, sub-Saharan Africa, and America (except for Bolivia, Chile, Perú, and Uruguay). The Mediterranean Basin and Australia are still free of HLB. The threat to HLB-free countries is constant due to the proximity of the disease and its vectors and the unstoppable increase in international trade. Candidatus Liberibacter asiaticus can infect most citrus species, cultivars, and hybrids. Leaves of infected trees develop a blotchy mottle symptom (yellowing vein and an asymmetrical chlorosis). Infected branches suffer substantial leaf drop, resulting in severe canopy thinning. Fibrous root density decreases nearly 30%, directly affecting water and nutrient uptake, severely reducing fruit yield, and demanding more frequent irrigation and improved mineral nutrition practices. Fruit from HLB-affected trees are often lopsided, poorly colored, and contain aborted seeds, with low commercial value due to small size and quality. The juice from affected fruit present low soluble solids content, high acidity, and bitterness. There is no cure for the disease yet. Current management strategies focus on either delaying infection or managing infected trees. Methods of delaying infection include removal of symptomatic trees, planting and resetting using HLB-free nursery trees, protection of grove edges and intensive monitoring and control of the vectors mainly using physical, chemical, and biological control methods. Management of infected trees includes adjusting soil pH, enhancing nutritional programs, and improving irrigation management based on altered tree capacities and needs when affected by HLB. Research has evolved rapidly to address this devastating challenging, and several recent alternatives based on psyllid management, bactericides, cultural practices (thermotherapy and vector exclusion using netting), and genetic transformation have been tested. While most attempts at management have focused on a single component of the disease pyramid, most do not explicitly consider multiple elements at the same time. This Research Topic is a collection of 9 articles from 49 co-authors and present the latest advances in managing the HLB pathosystem, focusing on assessments of near-term feasible practices in the context of the vector, pathogen, host plant, and environment

Laser-induced breakdown spectroscopy applied to pasture, titanium, and bioplastics

Precision agriculture is a farming practice that makes production more efficient. Farmers are able to treat infield variability optimising efficiency, growth, and yield by tailoring the time, rate, and type of fertilizer that is applied. This reduces costs, waste, and environmental side effects such as runoff and leaching caused by overfertilization. Precision agriculture technology measures the nutritional status of crops to inform what, and where, nutrients are needed. The sensors need to be precise, discriminative, and work in real time to ensure that optimal windows for nutrition are not missed. These sensor systems provide aerial imaging, and crop, or soil, colour index maps. A technology that has proven effective on some agricultural specimens is laserinduced breakdown spectroscopy (LIBS). LIBS is an optical emission technique that utilizes a high-powered pulsed laser to create a plasma on the sample surface. As the plasma cools, photons are emitted at distinct wavelengths corresponding to the elemental composition in the plasma, which should represent the sample. This thesis investigates using LIBS as a sensor for precision agriculture. Multiple chemometric methods have been used on the pasture spectra to build calibration models. There are large deviations between spectra belonging to a single sample. This is due to surface inhomogeneity, particle size, lens-to-sample distance, temperature fluctuations between plasmas, and other causes. Temperature corrections were investigated using Boltzmann plots, Saha-Boltzmann plots, and intensity ratios. With limited success in mitigating the variations in pasture spectra, LIBS was used to investigate non-aqueous systems. The ability to selectively sinter the surface of injection moulded titanium was examined. Titanium metal injection moulding allows the creation of complex metal parts that are lightweight, biocompatible, and costs less than machining. Multiple LIBS pulses produced sintering in the ablation crater of injection moulded titanium by sufficiently heating the titanium particles so that fusion occurred. The spectra from LIBS can be used to monitor the extent to which the surface is sintered by measuring the reduction in carbon emissions. An autofocus system, based on the triangulation method, was used to minimise variations caused by lens-to-sample distance (LTSD). With the success of sintering titanium, LIBS was used to investigate non-aqueous organic systems. Employing LIBS to discriminate bioplastics from regular plastics was explored in recycle waste streams. If bioplastics are present in the recovery process of regular plastics the resulting product contains impurities. This study was undertaken to determine the feasibility of incorporating bioplastics in the curbside pickup of recyclables in New Zealand. The common recyclables are plastics, glass, tin cans, and aluminium cans. The setup was designed to emulate a one-shot LIBS detection system in a recycling plant. Models were created using k nearest neighbours and soft independent modelling class analogy from the spectra. 100 % discrimination between bioplastics and regular plastics was achieved. An autofocus system, combining dual lasers, was used to overcome the occlusions produced by sample geometry

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy