Geographic information systems and remote sensing: Innovative tools for plant health (original) (raw)
Related papers
Precision Agriculture Technologies for Management of Plant Diseases
Plant diseases contribute 10-16% losses in global harvests each year, costing an estimated US$ 220 billion. Abundant use of chemicals such as bacteri-cides, fungicides, and nematicides to control plant diseases are causing adverse effects to many agroecosystems. Precision plant protection offers a non-destructive means of managing plant diseases based on the concept of spatio-temporal variability. Global Navigation Satellite System (GNSS) and Geographic Information System (GIS) allow for assessment of field heterogeneity due to disease problems and can enable site-specific intervention. Similarly, hyperspectral remote sensing is a cutting-edge spectral approach for plant diseases detection. The main aim of precision plant protection is to significantly reduce the injudicious use of chemical inputs and hence the adverse impact of chemicals to the environment. This chapter provides some insights into the deployment of site-and time-specific approaches to manage plant disease problems in a balanced and optimized manner.
Crop Type Discrimination and Health Assessment using Hyperspectral Imaging
Current Science
Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%-94% for AVIRI-NG and 42%-55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (C ab) using AVIRIS-NG narrow bands. The retrieved LAI and C ab showed 19%-27% and 23%-29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662-702 and 2155-2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.
International Journal of Applied Earth Observation and Geoinformation, 2003
Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4-2.5 m was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7-1.3 m, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants' spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification.
Phytopathology Research
The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improv...
2012
The objectives of this study were to reveal the presence of viral infections in two varieties of tobacco plants (Nicotiana tabacum L.) as well as to discriminate the levels of the disease using hyperspectral leaf reflectance. Data sets were collected from two tobacco cultivars, Xanthi and Rustica, known as most widespread in Bulgaria. Experimental plants were grown in a greenhouse under controlled conditions. At growth stage 4-6 expanded leaf plants of cultivar Xanthi were inoculated with Potato virus Y (PVY) while the Rustica plants were inoculated with Tomato spotted wilt virus (TSWV). These two viruses are worldwide distributed and cause significant yield losses in many economically important crops. In the course of time after inoculation the concentration of the viruses in plant leaves was assessed by serological analysis via DAS-ELISA and RT-PCR techniques. Hyperspectral reflectance data were collected by a portable fibreoptics spectrometer in the visible and near-infrared spectral ranges (450-850 nm). As control plants healthy untreated tobacco plants were used. The significance of the differences between reflectance spectra of control and infected leaves was analyzed by means of Student's t-criterion at p<0.05. The analyses were performed at ten wavebands selected to cover the green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm) spectral ranges. Changes in SRC were found for both viral treatments and comparative analysis showed that the influence of PVY was stronger. The discrimination of disease intensity was achieved by derivative analysis of the red edge position. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 11/25/2012 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 8531 85311H-4 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 11/25/2012 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 8531 85311H-9 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 11/25/2012 Terms of Use: http://spiedl.org/terms View publication stats View publication stats
Review artcile, 2023
Abstract: The key elements that underpin food security require the adaptation of agricultural systems to support productivity increases while minimizing inputs and the adverse effects of climate change. The advances in precision agriculture over the past few years have substantially enhanced the efficiency of applying spatially variable agronomic inputs for irrigation, such as fertilizers, pesticides, seeds, and water, and we can attribute them to the increasing number of innovations that utilize new technologies that are capable of monitoring field crops for varying spatial and temporal changes. Remote sensing technology is the primary driver of success in precision agriculture, along with other technologies, such as the Internet of Things (IoT), robotic systems, weather forecasting technology, and global positioning systems (GPSs). More specifically, multispectral imaging (MSI) and hyperspectral imaging (HSI) have made the monitoring of the field crop health to aid decision making and the application of spatially and temporally variable agronomic inputs possible. Furthermore, the fusion of remotely sensed multisource data—for instance, HSI and LiDAR (light detection and ranging) data fusion—has even made it possible to monitor the changes in different parts of an individual plant. To the best of our knowledge, in most reviews on this topic, the authors focus on specific methods and/or technologies, with few or no comprehensive reviews that expose researchers, and especially students, to the vast possible range of remote sensing technologies used in agriculture. In this article, we describe/evaluate the remote sensing (RS) technologies for field crop monitoring using spectral imaging, and we provide a thorough and discipline-specific starting point for researchers of different levels by supplying sufficient details and references. We also high light strengths and drawbacks of each technology, which will help readers select the most appropriate method for their intended uses.
Geocarto International, 2017
Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop Abstract Development of techniques for early detection of maize grey leaf spot (GLS) infection is valuable in preventing crop damage and minimising yield loss. In this study, we tested whether GLS field symptoms on maize can be detected using hyperspectral field spectra resampled to different sensor resolutions. First, field spectra were acquired from healthy, moderate and severely infected maize leaves during the 2013 and 2014 growing seasons. The spectra were then resampled to four sensor spectral resolutions-WorldView-2, Quickbird, RapidEye, and Sentinel-2. In each case, the Random Forest algorithm was used to classify the 2013 resampled spectra to represent the three identified disease severity categories. Classification accuracy was evaluated using an independent test dataset obtained during the 2014 growing season. Results showed that Sentinel-2, with 13 spectral bands, achieved the highest overall accuracy and kappa value of 84% and 0.76, respectively while the WorldView-2, with 8 spectral bands, yielded the second highest overall accuracy and kappa value of 82% and 0.73, respectively. Results also showed that the 705 and 710nm red edge bands were the most valuable in detecting the GLS for Sentinel-2 and RapidEye, respectively. On the resampled WorldView 2 and Quickbird sensor resolutions, the respective 608 nm and 660 nm in the yellow and red bands were identified as the most valuable for discriminating all categories of infection. Overall, our results imply that opportunities exist for developing operational remote sensing systems based on multispectral sensors, especially Sentinel-2 and WorldView-2 for early detection of GLS. Adoption of such datasets is particularly valuable for minimizing crop damage and improving yield.
Real time Crop health monitoring using Remote Sensing and ancillary information using GIS
2018
Word Limit of the Paper should not be more than 3000 Words = 7/8 Pages) Abstract: To meet the ever increasing demand of food production it is expedient to increase the quality and quantity of produce with optimum input as per the crop demand. So, it is of great significance to monitor the crop health and record information from growing season to harvesting season. Chlorophyll is an excellent indicator of crop health since it allows plants to absorb light and directly reflects the photosynthesis activity. Therefore in this study various chlorophyll indices along with modified vegetation index have been used for assessment of crop health condition and monitoring over a period of time. Satellite has been a paradigm in the field of remote sensing applications such as agriculture, forestry and environment. Data from Landsat-8 with 30m resolution and sentinel-2 images with 10 and 20 meter resolution was acquired for the same period of time. Pre and post processing of images such as classi...