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

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

Assessment of Canopy Porosity in Avocado Trees as a Surrogate for Restricted Transpiration Emanating from Phytophthora Root Rot

Phytophthora root rot (PRR) disease is a major threat in avocado orchards, causing extensive production loss and tree death if left unmanaged. Regular assessment of tree health is required to enable implementation of the best agronomic management practices. Visual canopy appraisal methods such as the scoring of defoliation are subjective and subject to human error and inconsistency. Quantifying canopy porosity using red, green and blue (RGB) colour imagery offers an objective alternative. However, canopy defoliation, and porosity is considered a ‘lag indicator’ of PRR disease, which, through root damage, incurs water stress. Restricted transpiration is considered a ‘lead indicator’, and this study sought to compare measured canopy porosity with the restricted transpiration resulting from PRR disease, as indicated by canopy temperature. Canopy porosity was calculated from RGB imagery acquired by a smartphone and the restricted transpiration was estimated using thermal imagery acquired by a FLIR B250 hand-held thermal camera. A sample of 85 randomly selected trees were used to obtain RGB imagery from the shaded side of the canopy and thermal imagery from both shaded and sunlit segments of the canopy; the latter were used to derive the differential values of mean canopy temperature (Δ Tmean), crop water stress index (Δ CWSI), and stomatal conductance index (Δ Ig). Canopy porosity was observed to be exponentially, inversely correlated with Δ CWSI and Δ Ig (R2 > 90%). The nature of the relationship also points to the use of canopy porosity at early stages of canopy decline, where defoliation has only just commenced and detection is often beyond the capability of subjective human assessment

Remote Sensing techniques for managing Phytophthora root rot in avocado

The Australian avocado industry suffers millions of dollars of lost productivity each year as a result of Phytophthora root rot (PRR) (Australian Avocado Industry, 2016). Effective management of the disease has proved to be difficult due to the current inefficient and subjective methods of diagnosis that struggle to detect the disease at early stages. The University of New England’s (UNE) Precision Agriculture Research Group (PARG) has been investigating a range of ground and satellite based remote sensing technologies for their ability to more accurately and efficiently identify the presence of PRR, particularly in its early stages of infection

Exploring the Potential of High Resolution Satellite Imagery for Yield Prediction of Avocado and Mango Crops

Accurate pre-harvest yield estimation of high value fruit tree crops provides a range of benefits to industry and growers. Currently, yield estimation in Avocado (Persea americana) and Mango (Mangifera indica) orchards is undertaken by a visual count of a limited number of trees. However, this method is labour intensive and can be highly inaccurate if the sampled trees are not representative of the spatial variability occurring across the orchard. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of yield. A stratified sampling technique was applied in each block to measure relevant yield parameters from eighteen sample trees representing high, medium and low vigour zones (6 from each) based on classified normalised difference vegetation index (NDVI) maps. For avocado crops, principal component analysis (PCA) and non-linear regression analysis were applied to 18 derived vegetation indices (VIs) to determine the index with the strongest relationship to the measured yield parameters. For mango, an integrated approach of geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model produced more accurate predictions. The results demonstrate that accurate maps of yield variability and total orchard yield can be achieved from WV imagery and targeted sampling; whilst accurate maps of fruit size and the incidence of phytophthora can also be achieved in avocado. These outcomes offer improved forecasting than currently adopted practices and therefore offer great benefit to both the avocado and mango industries

Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models

White leaf disease (WLD) is an economically significant disease in the sugarcane industry. This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction. This study evaluated the performance of the existing DL models such as YOLOv5, YOLOR, DETR, and Faster R-CNN to recognize WLD in sugarcane crops. The experimental results indicate that the YOLOv5 network outperformed the other selected models, achieving a precision, recall, mean average [email protected] ([email protected]), and mean average [email protected] ([email protected]) metrics of 95%, 92%, 93%, and 79%, respectively. In contrast, DETR exhibited the weakest detection performance, achieving metrics values of 77%, 69%, 77%, and 41% for precision, recall, [email protected], and [email protected], respectively. YOLOv5 is selected as the recommended architecture to detect WLD using the UAV data not only because of its performance, but this was also determined because of its size (14 MB), which was the smallest one among the selected models. The proposed methodology provides technical guidelines to researchers and farmers for conduct the accurate detection and treatment of WLD in the sugarcane fields.</p

Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images

Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka. Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important, especially at the early pre-visual stage. This work presents the first approach on the preliminary detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana, Sri Lanka. The pixelwise segmented samples were classified as ground, shadow, healthy plant, early symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field. The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and quick method for detecting WLD.</p