Aerial Multispectral Imaging Research Articles

UAV imaging for spectral characterization of Coffee Leaf Miner (Leucoptera coffeella) infestation in the Cerrado Mineiro region

Abstract. Brazil, the world's largest coffee producer, faces challenges managing the coffee leaf miner (Leucoptera coffeella), a significant pest. This study suggests remote sensing for pest control decisions. Two experimental areas in the Cerrado region of Minas Gerais State were analyzed to spectrally characterize infested plants and estimate the number of mines per plant. Results show the ability to differentiate infested plants with greater reflectance variance in the near infrared at 850nm. The performances of the three machine learning algorithms were compared. Determining the number of mines in the group of most infested plants demonstrated slightly higher precision, achieving an RMSE of 22.69% using the Support Vector Machine algorithm. Conversely, the group of least-infested plants obtained the best result with the Random Forest algorithm, achieving an RMSE of 32.47%. These promising results indicated that CLM can be detected using aerial multispectral imaging data.

Advanced Biomimetic Multispectral Curved Compound Eye Camera for Aerial Multispectral Imaging in a Large Field of View.

In this work, we demonstrated a new type of biomimetic multispectral curved compound eye camera (BM3C) inspired by insect compound eyes for aerial multispectral imaging in a large field of view. The proposed system exhibits a maximum field of view (FOV) of 120 degrees and seven-waveband multispectral images ranging from visible to near-infrared wavelengths. Pinhole imaging theory and the image registration method from feature detection are used to reconstruct the multispectral 3D data cube. An airborne imaging experiment is performed by assembling the BM3C on an unmanned aerial vehicle (UAV). As a result, radiation intensity curves of several objects are successfully obtained, and a land type classification is performed using the K-means method based on the aerial image as well. The developed BM3C is proven to have the capability for large FOV aerial multispectral imaging and shows great potential applications for distant detecting based on aerial imaging.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.

Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.

Vine water status mapping with multispectral UAV imagery and machine learning

Optimizing water management has become one of the biggest challenges for grapevine growers in California, especially during drought conditions. Monitoring grapevine water status and stress level across the whole vineyard is an essential step for precision irrigation management of vineyards to conserve water. We developed a unified machine learning model to map leaf water potential ({psi }_{mathrm{leaf}}), by combining high-resolution multispectral remote sensing imagery and weather data. We conducted six unmanned aerial vehicle (UAV) flights with a five-band multispectral camera from 2018 to 2020 over three commercial vineyards, concurrently with ground measurements of sampled vines. Using vegetation indices from the orthomosaiced UAV imagery and weather data as predictors, the random forest (RF) full model captured 77% of {psi }_{mathrm{leaf}} variance, with a root mean square error (RMSE) of 0.123 MPa, and a mean absolute error (MAE) of 0.100 MPa, based on the validation datasets. Air temperature, vapor pressure deficit, and red edge indices such as the normalized difference red edge index (NDRE) were found as the most important variables in estimating {psi }_{mathrm{leaf}} across space and time. The reduced RF models excluding weather and red edge indices explained 52–48% of {psi }_{mathrm{leaf}} variance, respectively. Maps of the estimated {psi }_{mathrm{leaf}} from the RF full model captured well the patterns of both within- and cross-field spatial variability and the temporal change of vine water status, consistent with irrigation management and patterns observed from the ground sampling. Our results demonstrated the utility of UAV-based aerial multispectral imaging for supplementing and scaling up the traditional point-based ground sampling of {psi }_{mathrm{leaf}}. The pre-trained machine learning model, driven by UAV imagery and weather data, provides a cost-effective and scalable tool to facilitate data-driven precision irrigation management at individual vine levels in vineyards.

Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS

Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure SHIs are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing solution (UMS) to estimate soil TN in an agricultural farm. TN is an important macro-nutrient or SHI that directly affects the crop health. Accurate prediction of soil TN can significantly increase crop yield through informed decision making on the timing of seed planting, and fertilizer quantity and timing. The ground-truth data required to train the AI approaches is generated via laser-induced breakdown spectroscopy (LIBS), which can be readily used to characterize soil samples, providing rapid chemical analysis of the samples and their constituents (e.g., nitrogen, potassium, phosphorus, calcium). Although LIBS was previously applied for soil nutrient detection, there is no existing study on the integration of LIBS with UAV multispectral imaging and AI. We train two machine learning (ML) models including multi-layer perceptron regression and support vector regression to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red (R), near-infrared, and green (G) spectral bands, computed vegetation indices (NDVI), and environmental variables including air temperature and relative humidity (RH). To generate the ground-truth data or the training data for the machine learning models, we determine the N spectrum of the soil samples (collected from a farm) using LIBS and develop a calibration model using the correlation between actual TN of the soil samples and the maximum intensity of N spectrum. In addition, we extract the features from the multispectral images captured while the UAV follows an autonomous flight plan, at different growth stages of the crops. The ML model’s performance is tested on a fixed configuration space for the hyper-parameters using various hyper-parameter optimization techniques at three different wavelengths of the N spectrum.

Analysis of Forest Vegetal Characteristics of Akure Forest Reserve from Optical Imageries and Unmanned Aerial Vehicle Data

Forest vegetal characteristics monitoring has a long tradition records with a success rate ranging from low to medium or high depends on the application at the hands. Details information about the indication of association of phenomena as forest indicators, such forest gap, estate and forest status, provides high spatial resolution images. The aim of this study focuses on combining unmanned Aerial Vehicles (UAVs) and satellite multispectral imaging along side by side to details forest parameter during the seasons. UAVs image at 0.15m appeared more detailed of having features such as rock, road, bare ground, riparian trees among others than that of Landsat OLI image, though the features such as rock, road, bare ground, and riparian forest were also seen on the image but it was poorly seen due to the coarse spatial resolution of 30 m. The 3-Dimensional of UAVs, relief pattern and contour from Shuttle Radar Topography Mission was also compared and this study further demonstrated on the advantages of Unmanned Aerial Vehicle data over established remotely sensed data which includes flying blow the cloud, high spatial resolution, flexibility, inexpensive of data acquisition, time effective, using video footage to detect human activities such as tree flora, burning and logging.

Analysis of Forest Vegetal Characteristics of Akure Forest Reserve from Optical Imageries and Unmanned Aerial Vehicle Data

Forest vegetal characteristics monitoring has a long tradition records with a success rate ranging from low to medium or high depends on the application at the hands. Details information about the indication of association of phenomena as forest indicators, such forest gap, estate and forest status, provides high spatial resolution images. The aim of this study focuses on combining unmanned Aerial Vehicles (UAVs) and satellite multispectral imaging along side by side to details forest parameter during the seasons. UAVs image at 0.15m appeared more detailed of having features such as rock, road, bare ground, riparian trees among others than that of Landsat OLI image, though the features such as rock, road, bare ground, and riparian forest were also seen on the image but it was poorly seen due to the coarse spatial resolution of 30 m. The 3-Dimensional of UAVs, relief pattern and contour from Shuttle Radar Topography Mission was also compared and this study further demonstrated on the advantages of Unmanned Aerial Vehicle data over established remotely sensed data which includes flying blow the cloud, high spatial resolution, flexibility, inexpensive of data acquisition, time effective, using video footage to detect human activities such as tree flora, burning and logging.

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.

Aerial multispectral imaging for crop hail damage assessment in potato

Crop loss assessment after an event of hailstorm can be inaccurate, subjective, and time consuming with the conventional method. Low-altitude, high-resolution aerial imaging using an unmanned aerial vehicle can be utilized for rapid assessment of target crops in a large scale, which can potentially improve the evaluation procedure. The goal of this study was to evaluate the feasibility of rapid and accurate assessment of crop damage due to simulated hailstorms using aerial multispectral imaging. Field experiments were conducted during two seasons in two potato varieties (Russet Norkotah, Ranger Russet) with three levels of mechanical defoliation (33%, 66%, and 99%) at three growth stages (tuber initiation, early bulk, and late bulk). All defoliation treatments were compared to the non-treated control plots (0% defoliation). Aerial multispectral images were collected between 77 and 108days after planting (0–60days after damage). Vegetation indices such as green normalized difference vegetation index (GNDVI), normalized difference vegetation index, and soil-adjusted vegetation index were calculated from replicate plots of different treatments. Results from two seasons showed similar trends in GNDVI values, with maximum effect of hail damage observed in early bulk stages. The mean GNDVI value was significantly lower in crops with the severe damage (99% defoliation) than others upon hail damage at the early bulk stage, with imaging after 10days after damage. The difference in GNDVI for crops with 33–66% damage could be detected within 10days after damage, and crop regrowth after that time period removed the effects of defoliation. The 99% defoliation damage at the early bulk stage also affected the crop yield significantly. Correlation analysis between vegetation indices and yield data indicated a strong relationship (r=0.77–0.90) for damage at the early bulk stage than other stages.

Vine vigour effects on leaf gas exchange and resource utilisation

Background and Aims: Variability in vine vigour is a commonly observed feature in vineyard blocks. Although this aspect is well recognised among viticulturalists, impacts of vigour variability on vine function and resource use efficiency are seldom considered in management decision-making. This study examined influences of vine vigour variability on vine physiological performance and resource use in a commercially managed vineyard block. Methods and Results: The vineyard block was divided into different vigour zones using plant cell density data derived from aerial multi-spectral imaging of the study block. Gas exchange and under-vine soil moisture status were measured in some of the vigour zones. Low vigour vines had consistently lower photosynthesis and stomatal conductance rates than high vigour vines. The differences were evident both during soil-drying and rewetting. Similarly, irrigation water utilisation was markedly lower in low than in high vigour vines. Vine fruit yield was linearly and positively related to vines' capacity to utilise applied water. Conclusions: Vigour has considerable influence on vine physiological performance and on resource utilisation capacity. Uniform irrigation application to a block with variable vigour (which is the norm currently) leads to spatially inefficient resource use, and consequently to a marked within-block variation in irrigation water productivity. Significance of the Study: The demonstration that variation in vigour has a corresponding effect on resource utilisation has direct relevance for managing vineyard inputs such as irrigation or fertilisers.

155 Mapping Floating Kelp Beds in Alaska With Remote Sensing

The commercial harvest of floating kelps has promise as a business opportunity that could be especially valuable for small communities in southeast Alaska. The giant kelp, Macrocystis, is harvested mainly for the commercial herring roe‐on‐kelp harvest in Alaska. In addition, in southeast Alaska the two other species of floating kelps, Nereocystis luetkeana and Alaria fistulosa, have been commercially harvested since 1992 for use as agrochemicals by the Alaska KelpCo. The continued existence of this company and others that may wish to harvest seaweeds on a commercial basis is threatened by the lack of a kelp harvest management system in Alaska. The objective of this research is to develop a reliable and cost effective method for estimating the aerial extent and the standing crop biomass of the floating kelps in SE Alaska. We are employing an aerial multispectral imaging system that can be flown at varying altitudes to achieve spatial resolutions ranging from 0.5 to 2 meters. We are investigating whether the three species have unique spectral or textural characteristics in the multispectral imagery that can be used to differentiate the populations. The end products for this project will be kelp resource maps that delineate the spatial extent and biomass of kelp populations within the beds and a methodology for assessing kelp beds with mixed species composition. These products will find use by resource managers such as the Alaska Department of Fish and Game and the Department of Natural Resources.