Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

Oil Palm Detection via Deep Transfer Learning

This article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available online. Although being tailored to the palm detection problem, the latter has general validity and can be used for any classification application. Second, we designed and evaluated a state-of-the-art detection system, which uses a convolutional neural network to extract meaningful features, and a classifier trained with the images from the proposed dataset. Results show outstanding effectiveness with an accuracy peak of 99.5% and a precision of 99.8%. Using different images for validation taken from different altitudes the model reached an accuracy of 97.5% and a precision of 98.3%. Hence, the proposed approach is highly applicable in the field of precision agriculture

Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms

Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Oil Palm Plantations

Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed

Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.The vast size of oil palm (Elaeis guineensis) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 31 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and >75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (R2= 0.63) for 7 year old and 0.45 m (R2=0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (>75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.European CommissionMarie Skłodowska-Curi

Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales

Object detection for single tree species identification with high resolution aerial images

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesObject recognition is one of the computer vision tasks developing rapidly with the invention of Region-based Convolutional Neural Network (RCNN). This thesis contains a study conducted using RCNN base object detection technique to identify palm trees in three datasets having RGB images taken by Unnamed Aerial Vehicles (UAVs). The method was entirely implemented using TensorFlow object detection API to compare the performance of pre-trained faster RCNN object detection models. According to the results, best performance was recorded with the highest overall accuracy of 93.1 ± 4.5 % and the highest speed of 9m 57s from faster RCNN model which was having inceptionv2 as feature extractor. The poorest performance was recorded with the lowest overall accuracy of 65.2 ± 10.9% and the lowest speed of 5h 39m 15s from faster RCNN model which was having inception_resnetv2 as feature extractor