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

Perspectives on Pathogenic Plant Virus Control with Essential Oils for Sustainability of Agriculture 4.0

The outbreaks of plant pathogenic viruses and insect pests affect agricultural product supply chain systems. Environmentally friendly innovative technologies are provided accurate, practical, and acceptable means for surveillance by farmers. The bioactive compound applications are derived from plant essential oils with antiviral activities as well as integrating insect pest control and management are useful choices. Successful comprehensive planning, including material production systems, extraction techniques, quality testing, and product creation are essential for strategic and operational decision-making under current operation management trends of Agriculture 4.0. This information can potentially be used to impel today agriculture and set the directions for supports. The role of management and data analysis will meet the challenges of increasing populations and food security with the ultimate goal to achieve efficient and sustainable effectiveness for all participants in directing the world agricultural systems

Detection of grapevine viral diseases in Australian vineyards using remote sensing and hyperspectral technology

Grapevine viral diseases cause substantial productivity and economic losses in the Australian viticulture industry. Two economically significant grapevine viral diseases - Grapevine Leafroll Disease (GLD) and Shiraz Disease (SD) - affect numerous vineyards across major wine regions in Australia. Accurate and quick diagnosis of the virus infection would greatly assist disease management for growers. Current detection methods include visual assessment and laboratory-based tests that are expensive and labour-intensive. Low-cost and rapid alternative methods are desirable in the industry. Recent advances in low-altitude remote sensing platforms such as unmanned aerial vehicles (UAVs or “drones”) in conjunction with high-resolution multiand hyper-spectral cameras now enable large spatial-scale surveillance of plant stresses. My thesis therefore focuses on developing fast and reliable methods for GLD and SD detection on a vineyard scale using optical sensors including RGB and hyperspectral and low-altitude remote sensing technology. The thesis is constituted by a review article and three result parts, it begins with a general introduction for the background and is followed by the research goals and significance of the project that is described in Chapter 1. In order to be familiar with all possible technologies that can be potentially used for GLD and SD detection, Chapter 2 includes a comprehensive overview of methodologies for the detection of any plant viruses reviewed from laboratory-based, destructive molecular and serological assays, to state-of-the-art non-destructive methods using optical sensors and machine vision, including use of hyperspectral cameras. A key contribution of the review is that, for the first time, a detailed economic analysis or cost comparison of the various detection methodologies for plant viruses is provided. In my research, various detection methods with different degrees of complexity were attempted for GLD and SD detection. Firstly, a simple and novel detection method using the projected leaf area (PLA) calculated from UAV RGB images is proposed in Chapter 3 for the disease symptom that alters the growth of the vine such as SD in Shiraz. The PLA is closely related to the canopy size. There are significant differences in PLA between healthy and SD-infected vines in spring due to retarded growth caused by SD, which offers a simple, rapid and practical method to detect SD in Shiraz vineyards. However, for diseases that cannot be easily detected by RGB images such as GLD in the white grape cultivars, different approaches are needed. Hyperspectral technology provides a wide spectrum of light with hundreds of narrow bands compared to RGB sensors. The advanced technology can detect imperceptible spectral changes from the disease and is particularly valuable for asymptomatic disease detection. A new approach using proximal hyperspectral sensing is described in Chapter 4. Using a handheld passive (sunlight is the radiation source) hyperspectral sensor to detect GLD in the vineyard presents a simple and rapid measurement method to detect the diseases using the spectral information from the canopy. An assessment was done for the disease's spectral reflectance throughout the grape growing season for both red and white cultivars. The partial least squares-discriminant analysis (PLS-DA) was used to build a classification model to predict the disease. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. The proximal hyperspectral sensing technique is readily applicable to a low-altitude remote sensing method to capture high-resolution hyperspectral images for large-scale viral disease surveillance in vineyards. The subsequent study in Chapter 5 presents an advanced method to quickly detect disease using an UAV carried hyperspectral sensor. The study evaluated the feasibility of UAV-based hyperspectral sensing in the visible and near-infrared (VNIR) spectral bands to detect GLD and SD in four popular wine grapevine cultivars in Australian vineyards. The method combined the spectral and spatial analysis to classify disease for individual pixels from the hyperspectral image. The model predictions for red- and white-berried grapevine cultivars achieved accuracies of 98% and 75%, respectively. For each viral disease, unique spectral regions and optimal detection times during the growing season were identified. The spectral difference between virus-infected and healthy vines closely matched the spectral signal from the proximal sensing method in Chapter 4, which demonstrated the reliability of the low-altitude hyperspectral sensing for grapevine disease detection. Lastly, a summary of the outcomes and remaining challenges and limitations of the existing technology is discussed in Chapter 6, followed by suggestions for further research for further improvement.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food & Wine, 202

Automated early plant disease detection and grading system: Development and implementation

As the agriculture industry grows, many attempts have been made to ensure high quality of produce. Diseases and defects found in plants and crops, affect the agriculture industry greatly. Hence, many techniques and technologies have been developed to help solving or reducing the impact of plant diseases. Imagining analysis tools, and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and Volatile Organic Compound (VOC) profiling techniques to detect early symptoms of diseases and defects of plants, fruits and vegetative produce. These disease detection techniques can be further categorized into two main groups; preharvest disease detection and postharvest disease detection techniques. This thesis aims to introduce the available disease detection techniques and to compare it with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this thesis incorporates the use of image analysis tools and k-means segmentation to implement a preharvest Offline and Online disease detection system. The Offline system to be used by pathologists and agriculturists to measure plant leaf disease severity levels. K-means segmentation and triangle thresholding techniques are used together to achieve good background segmentation of leaf images. Moreover, a Mamdani-Type Fuzzy Logic classification technique is used to accurately categorize leaf disease severity level. Leaf images taken from a real field with varying resolutions were tested using the implemented system to observe its effect on disease grade classification. Background segmentation using k-means clustering and triangle thresholding proved to be effective, even in non-uniform lighting conditions. Integration of a Fuzzy Logic system for leaf disease severity level classification yielded in classification accuracies of 98%. Furthermore, a robot is designed and implemented as a robotized Online system to provide field based analysis of plant health using visible and near infrared spectroscopy. Fusion of visible and near infrared images are used to calculate the Normalized Deference Vegetative Index (NDVI) to measure and monitor plant health. The robot is designed to have the functionality of moving across a specified path within an agriculture field and provide health information of leaves as well as position data. The system was tested in a tomato greenhouse under real field conditions. The developed system proved effective in accurately classifying plant health into one of 3 classes; underdeveloped, unhealthy, and healthy with an accuracy of 83%. A map with plant health and locations is produced for farmers and agriculturists to monitor the plant health across different areas. This system has the capability of providing early vital health analysis of plants for immediate action and possible selective pesticide spraying

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Applicability of smart tools in vegetable disease diagnostics

Various diseases and pests cause serious damage to vegetable crops during the growing season and after harvesting. Growers attempt to minimize losses by protecting their crops, starting with seed and seedling treatments and followed by monitoring their stands. In many cases, synthetic pesticide treatments are applied. Integrated pest management is currently being employed to minimize the impact of pesticides upon human health and the environment. Over the last few years, “smart” approaches have been developed and adopted in practice to predict, detect, and quantify phytopathogen occurrence and contamination. Our review assesses the currently available ready-to-use tools and methodologies that operate via visual estimation, the detection of proteins and DNA/RNA sequences, and the utilization of brand-new innovative approaches, highlighting the availability of solutions that can be used by growers during the process of diagnosing pathogens

Volatile organic compounds associated with neonectria ditissima infection in apples (Malus pumila cv Gala)

Postharvest diseases in apples during long term storage result in loss and waste. This is mainly caused by fungal pathogens. Fungal contamination and rot can change some of the volatile organic compounds (VOC) emitted by apple fruits. In this study, disease free Gala apples were inoculated with Neonectria ditissima. The aim was to identify VOCs associated with N. ditissima infection in gala apples. The inoculated apples were placed in 5L glass flask, sealed, and incubated at 20oC for one hour after which a charcoal filtered airflow of 1 L/min was maintained for one hour through the Volatile Capture Trap (VCT) with volatile emissions captured on a porapak-Q absorbent filter. Captured volatiles were eluted using 1 mL of dichloromethane (DCM) into a standard Agilent 1.5 mL HPLC vial. Eluted volatiles were analysed using Gas Chromatography coupled with Mass Spectrometry (GC/MS). Volatiles were capture in three replicates for both inoculated and healthy control groups at 2 days, 8 days, 14 days, 21 days, 28 days, 35 days, and 42 days post-inoculation. The N. ditissima discriminatory volatile were identified/discriminated qualitatively based on the unique volatile compounds detected and quantitatively based on variation in peak area of certain combinations of volatile compounds. Some of the discriminatory volatiles such as dodecyl hexanoate, 9-decen-1-yl hexanoate, hexyl butanoate and pentyl acetate were detected in the early stages of the infection. Styrene, terpinene-4-ol, ethyl hexanoate, ethyl butanoate, ethyl pentanoate and 2-methylpentyl formate constituted the main VOCs emitted during apple fruit decay. Other compounds such as alpha Farnesene and hexyl acetate were common to both healthy and inoculated apples but the peak areas in the healthy apples were well above the peak areas in the inoculated apples. However, these compounds expressed a decline in peak area over time. Apples are stored commercially in sealed stores for months making visual observation for early detection of disease almost impossible. Disease of stored apples are most times only detected at advanced stages when it has become nearly impossible to prevent losses. These discriminatory volatile metabolites detected at early stages of infection are important for early non-visual detection of N. ditissima in stored apples. Further research is recommended to the use of these compounds in early detection of the disease caused by N. ditissima

Model for detection of Xanthomonas campestris applying machine learning techniques improved by optimization algorithms.

Esta propuesta se centra en la elaboración de un modelo que permita la detección temprana de la enfermedad Xanthomonas Campestris aplicando técnicas de Machine Learning, caracterizadas por su alta interpretabilidad, mejoradas mediante algoritmos de optimización, permitiendo identificar de manera precisa el estado de una planta (Sana o enferma), con el objeto de que los agricultores puedan tomar acciones tempranas reduciendo el impacto que genera la enfermedad en la presentación y rendimiento del cultivo.This proposal focuses on the elaboration of a model that allows the early detection of the Xanthomonas Campestris disease by applying Machine Learning techniques, characterized by their high interpretability, improved by means of optimization algorithms, allowing to accurately identify the state of a plant (Healthy or diseased), so that farmers can take early action reducing the impact generated by the disease in the presentation and yield of the crop

Integrated nematode management

This book outlines the economic importance of specific plant parasitic nematode problems on the major food and industrial crops and presents the state-of-the-art management strategies that have been developed to reduce specific nematode impacts and outlines their limitations. Case studies to illustrate nematode impact in the field are presented and future changes in nematode disease pressure that might develop as a result of climate change and new cropping systems are discussed.illustrato

CONTRIBUTIONS ON ADVANCED AUTOMATION FOR SELECTIVE PROTECTION TREATMENTS ON SPECIALTY CROPS

Food security and food safety are the main global objectives of today\u2019s agriculture. Within this framework, the recent growing sensibility of both policy makers and consumers for food safety themes appear to be a hopeful sign for the introduction of new strategies and technological systems in the next future\u2019s agriculture. A particularly challenging issue for current crops management is the control of plant\u2019s diseases while avoiding environmental pollution. Precision pest management techniques \u2013an emerging subset of precision agriculture suite- aim at facing this challenge by means of: i) sensing technologies for the early detection and localization of diseased areas in the canopy, and ii) variable rate technologies for the selective application of crop protection treatments on target areas. In this dissertation, two innovative methodologies for hyperspectral crop\u2019s disease detection are presented. The measurements were acquired by means of a hyperspectral camera mounted onto a robotic manipulator which allowed to compose the subsequent hyperspectral scans (1 spatial dimension x 1 spectral dimension) into an hypercube (2D spatial x 1D spectral) of the imaged plant. The first disease detection method is based on the combinatorial selection of the most significant wavelengths from the hypercube data by applying linear discriminant analysis, and the classification power of the optimal selected combination is then evaluated by applying a principal component analysis. The second method is based on a new spatial filter approach, acting along the different channels of the hypercube. The two methods of detection are applied by discussing two case studies of diseases, both on cucumber plants. A first set of experiments was conducted on plants artificially inoculated with powdery mildew. A second and more extensive set of experiments was conducted on plants infected by the cucumber green mottle mosaic virus (CGMMV), which is nowadays considered one of the most dangerous diseases for the Cucurbitaceae family. The application of the two methodologies was successful in identifying the major symptoms of the diseases considered, and specifically the spatial filtering approach enable to detect the subtle morphological modifications in the plant tissue at rather early stage of CGMMV infection. Due to the high cost and complexity of the technologies adopted in the disease detection and of precision spraying equipment, the second part of the thesis applies the classical methods of mechanization cost-analysis to investigate what are the economic thresholds, which may enable the introduction of new precision pest management technologies. To this aim, the analysis is focused on vineyard and apple orchard that represent a favourable case for introducing these kind of innovations, due to the high protection treatments costs typical for these specialty crops. Starting from the results obtained in research on precision spraying in speciality crops, the technical-economic analysis considers on three different technological levels of precision spraying equipment, associated with increasing levels of reduction of the distributed amount of pesticide. This reduction is assumed to be linked to the improved accuracy in targeting the application without affecting the biological efficiency of the treatment, and hence generating a net cost benefit for the farmer. To gain insights into evaluating this benefit is of primary interest, since the profitability of precision spraying technologies will be a major driver for their adoption in speciality crops. Therefore, this study aims at: a) assessing the total costs associated to spraying equipment at the different technological levels considered; b) evaluating weather more advanced equipment can be profitable compared to current conventional sprayers. Furthermore, this analysis was extended to a high-precision, robotic spraying platform, here considered as a perspective scenario for precision spraying technologies. For this specific case, the study aimed at assessing the maximum allowed cost for such a robotic platform, which could generate positive net benefits for the farmer thanks to the envisaged pesticide reduction