Performance of UAV-derived Normalized Difference Vegetation Index (NDVI) for Early Estimation of Rice Yield

Traditionally, the rice yield is measured after the harvest, which is too late then to revert any agronomic practices to improve yield. The aerial spectral reflectance images of crops captured by a multispectral camera mounted in an Unmanned Aerial Vehicle (UAV) are capable of quantitatively measuring the internal physiological condition of crops directly associated with their health status. The aim of this study was to evaluate the performance of UAV-multispectral image derived-NDVI as a method for estimating rice yield. Multispectral drone images including single band images were acquired from a controlled rice field at Rice Research and Development Institute (RRDI), Sri Lanka. Rice variety Bg 300 was cultivated in the Yala season under four levels of Nitrogen (N) fertilizer treatment plots. According to the regression co-relation analysis the derived NDVI values at 15 and 25 m flying heights from the rice crops at the booting stage were moderately (R2=0.65 and R2=0.67, p<0.05, respectively) associated with the actual yield. The derived NDVI values indicated the rice crop vigor at the booting stage is a useful indicator for early estimation of the rice yield prior to actual harvest

Toward an integrated approach to crop production and pollination ecology through the application of remote sensing

Insect pollinators provide an essential ecosystem service by transferring pollen to crops and native vegetation. The extent to which pollinator communities vary both spatially and temporally has important implications for ecology, conservation and agricultural production. However, understanding the complex interactions that determine pollination service provisioning and production measures over space and time has remained a major challenge. Remote sensing technologies (RST), including satellite, airborne and ground based sensors, are effective tools for measuring the spatial and temporal variability of vegetation health, diversity and productivity within natural and modified systems. Yet while there are synergies between remote sensing science, pollination ecology and agricultural production, research communities have only recently begun to actively connect these research areas. Here, we review the utility of RST in advancing crop pollination research and highlight knowledge gaps and future research priorities. We found that RST are currently used across many different research fields to assess changes in plant health and production (agricultural production) and to monitor and evaluate changes in biodiversity across multiple landscape types (ecology and conservation). In crop pollination research, the use of RST are limited and largely restricted to quantifying remnant habitat use by pollinators by ascertaining the proportion of, and/or isolation from, a given land use type or local variable. Synchronization between research fields is essential to better understand the spatial and temporal variability in pollinator dependent crop production. RST enable these applications to be scaled across much larger areas than is possible with field-based methods and will facilitate large scale ecological changes to be detected and monitored. We advocate greater use of RST to better understand interactions between pollination, plant health and yield spatial variation in pollinator dependent crops. This more holistic approach is necessary for decision-makers to improve strategies toward managing multiple land use types and ecosystem services

Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2 ) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2 ) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2 ) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content

An anomaly detection model utilizing attributes of low powered networks, IEEE 802.15.4e/TSCH and machine learning methods

The rapid growth in sensors, low-power integrated circuits, and wireless communication standards has enabled a new generation of applications based on ultra-low powered wireless sensor networks. These are employed in many environments including health-care, industrial automation, smart building and environmental monitoring. According to industry experts, by the year 2020, over 20 billion low powered, sensor devices will be deployed and an innumerable number of data objects will be created. The objective of this work is to investigate the feasibility and analyze optimal methods of using low powered wireless characteristics, attributes of communication protocols and machine learning techniques to determine traffic anomalies in low powered networks. Traffic anomalies can be used to detect security violations as well as network performance issues. Both live and simulated data have been used with four machine learning methods, to examine the relationship between performance and the various factors and methods. Several factors including the number of nodes, sample size, noise influence, model aging process and classification algorithm are investigated against performance accuracy using data collected from an operational wireless network, comprising more than one hundred nodes, during a six-month period. An important attribute of this work is that the proposed model is able to implement in any low powered network, regardless of the software and hardware architecture of individual nodes (as long as the network complies with an open standard communication mechanism). Furthermore, the experiment portion of this work includes over 80 independent experiments to evaluate the behaviour of various attributes of low powered networks. Machine learning models trained using carefully selected input features and other factors including adequate training samples and classification algorithm are able to detect traffic anomalies of low powered wireless networks with over 95% accuracy. Furthermore, in this work, a framework for an aggregated classification model has been evaluated and the experiment results confirm a further improvement of the prediction accuracy and a reduction of both false positive and negative rates in comparison to basic classification models.University of Ontario Institute of Technolog

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

Phytophthora root rot (PRR) infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, canopy decline, defoliation, and, eventually, tree mortality. Typically, the severity of PRR disease (proportion of canopy decline) is assessed by visually comparing the canopy health of infected trees to a standardised set of photographs and a corresponding disease rating. Although this visual method provides some indication of the spatial variability of PRR disease across orchards, the accuracy and repeatability of the ranking is influenced by the experience of the assessor, the visibility of tree canopies, and the timing of the assessment. This study evaluates two image analysis methods that may serve as surrogates to the visual assessment of canopy decline in large avocado orchards. A smartphone camera was used to collect red, green, and blue (RGB) colour images of individual trees with varying degrees of canopy decline, with the digital photographs then analysed to derive a canopy porosity percentage using a combination of ‘Canny edge detection’ and ‘Otsu’s’ methods. Coinciding with the on-ground measure of canopy porosity, the canopy reflectance characteristics of the sampled trees measured by high resolution Worldview-3 (WV-3) satellite imagery was also correlated against the observed disease severity rankings. Canopy porosity values (ranging from 20–70%) derived from RGB images were found to be significantly different for most disease rankings (p < 0.05) and correlated well (R2 = 0.89) with the differentiation of three disease severity levels identified to be optimal. From the WV-3 imagery, a multivariate stepwise regression of 18 structural and pigment-based vegetation indices found the simplified ratio vegetation index (SRVI) to be strongly correlated (R2 = 0.96) with the disease rankings of PRR disease severity, with the differentiation of four levels of severity found to be optimal

Evaluating Remote Sensing Techniques for Assessing Phytophthora Root Rote Induced Canopy Decline Symptoms in Avocado Orchards - Dataset

Phytophthora root rot disease (PRR) is a major threat in avocado orchards. To identify a potential alternative to the current methods of assessing PRR in avocado for example visual assessment of canopy decline by human eyes, data has been collected from number of novel remote sensing technologies for measuring PRR induced canopy decline in avocado. Included Red Green and Blue (RGB) imagery acquired from a smartphone; thermal imagery from a hand-held camera and hyperspectral data acquired with a hand-held FieldSpec® 3 spectroradiomete