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

Filling the sensor gap: applying UAS technology to land-use research

Collecting data at ground level typically yields the most detailed information on a subject, however it is limited by the spatial extent that can be covered within a specific timeframe. Remote sensing from an aerial platform increases this spatial extent and the deployment of unmanned aircraft systems (UAS) can provide this ability directly to researchers at an affordable cost and at data resolutions that are very applicable for site specific surveys. Further to this, developments in photogrammetry software allow the creation of orthorectified spectral and structural data that can that can be classified via pixel or object-based analysis methods and applied to a wide variety of different land-use research areas. In this study a sensor package was created consisting of two off the shelf digital cameras, one un-modified and the other modified to be sensitive to near infra-red wavelengths of light. A multi-rotor aerial platform utilising an open source autopilot was assembled to enable data collection and a processing pipeline was devised to transform RAW camera imagery into georeferenced and orthorectified data, using computer vision software following the structure from motion (SfM) approach. This remote sensing tool was applied to a variety of land-use research study sites in central Scotland and Northern England with two main areas focused on. (1) The use of spectral and structural data for the detection of disease within a potato (Solanum tuberosum L.) crop revealed that UAS could be an effective tool for mapping the distribution of diseased plants. (2) Comparisons between aerial data and traditional manual assessments of a trial crop of potatoes revealed that the earliest stages of plant emergence could not be detected but later plant counts, and ground cover estimates correlated well, indicating that UAS could be an effective trials monitoring tool, giving extra structural data and potentially a more representative measure of canopy ground cover compared to traditional manual techniques. This study also showed results from experimental applications investigating the mapping of invasive non-native species and ways of enabling upscaling of greenhouse gas emissions from different land use types. Therefore, this study demonstrates that UAS equipped with basic imaging technology can be of use to a variety of land-use research areas and look set to become an invaluable remote sensing tool, which will improve further with the addition of calibrated multi-spectral sensor payloads, high precision global navigation satellite systems and relaxation in regulations governing their use

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Aerial Semantic Mapping for Precision Agriculture using Multispectral Imagery

Nowadays constant technological evolution cover several necessities and daily tasks in our society. In particular, drones usage, given its wide vision to capture the terrain surface images, allows to collect large amounts of information with high efficiency, performance and accuracy. This master dissertation’s main purpose is the analysis, classification and respective mapping of different terrain types and characteristics, using multispectral imagery. Solar radiation flow reflected on the surface is captured by the used multispectral camera’s different lenses (RedEdge-M, created by Micasense). Each one of these five lenses is able to capture different colour spectrums (i.e. Blue, Green, Red, Near-Infrared and RedEdge). It is possible to analyse the various spectrum indices from the collected imagery, according to the fusion of different combinations between coloured bands (e.g. NDVI, ENDVI, RDVI. . . ). This project engages a ROS (Robot Operating System) framework development, capable of correcting different captured imagery and, hence, calculating the implemented spectral indices. Several parametrizations of terrain analysis were carried throughout the project, and this information was represented in semantic maps by layers (e.g. vegetation, water, soil, rocks). The obtained experimental results were validated in the scope of several projects incorporated in PDR2020, with success rates between 70% and 90%. This framework can have multiple technical applications, not only in Precision Agriculture, but also in vehicles autonomous navigation and multi-robot cooperation

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.info:eu-repo/semantics/publishedVersio

Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing

This PhD project develops methods for the assessment of forest condition utilising modern remote sensing technologies, in particular optical imagery from unmanned aerial systems and with Structure from Motion photogrammetry. The research focuses on health threats to the UK’s native oak trees, specifically, Chronic Oak Decline (COD) and Acute Oak Decline (AOD). The data requirements and methods to identify these complex diseases are investigatedusing RGB and multispectral imagery with very high spatial resolution, as well as crown textural information. These image data are produced photogrammetrically from multitemporal unmanned aerial vehicle (UAV) flights, collected during different seasons to assess the influence of phenology on the ability to detect oak decline. Particular attention is given to the identification of declined oak health within the context of semi-natural forests and heterogenous stands. Semi-natural forest environments pose challenges regarding naturally occurring variability. The studies investigate the potential and practical implications of UAV remote sensing approaches for detection of oak decline under these conditions. COD is studied at Speculation Cannop, a section in the Forest of Dean, dominated by 200-year-old oaks, where decline symptoms have been present for the last decade. Monks Wood, a semi-natural woodland in Cambridgeshire, is the study site for AOD, where trees exhibit active decline symptoms. Field surveys at these sites are designed and carried out to produce highly-accurate differential GNSS positional information of symptomatic and control oak trees. This allows the UAV data to be related to COD or AOD symptoms and the validation of model predictions. Random Forest modelling is used to determine the explanatory value of remote sensing-derived metrics to distinguish trees affected by COD or AOD from control trees. Spectral and textural variables are extracted from the remote sensing data using an object-based approach, adopting circular plots around crown centres at individual tree level. Furthermore, acquired UAV imagery is applied to generate a species distribution map, improving on the number of detectable species and spatial resolution from a previous classification using multispectral data from a piloted aircraft. In the production of the map, parameters relevant for classification accuracy, and identification of oak in particular, are assessed. The effect of plot size, sample size and data combinations are studied. With optimised parameters for species classification, the updated species map is subsequently employed to perform a wall-to-wall prediction of individual oak tree condition, evaluating the potential of a full inventory detection of declined health. UAV-acquired data showed potential for discrimination of control trees and declined trees, in the case of COD and AOD. The greatest potential for detecting declined oak condition was demonstrated with narrowband multispectral imagery. Broadband RGB imagery was determined to be unsuitable for a robust distinction between declined and control trees. The greatest explanatory power was found in remotely-sensed spectra related to photosynthetic activity, indicated by the high feature importance of nearinfrared spectra and the vegetation indices NDRE and NDVI. High feature importance was also produced by texture metrics, that describe structural variations within the crown. The findings indicate that the remotely sensed explanatory variables hold significant information regarding changes in leaf chemistry and crown morphology that relate to chlorosis, defoliation and dieback occurring in the course of the decline. In the case of COD, a distinction of symptomatic from control trees was achieved with 75 % accuracy. Models developed for AOD detection yielded AUC scores up to 0.98,when validated on independent sample data. Classification of oak presence was achieved with a User’s accuracy of 97 % and the produced species map generated 95 % overall accuracy across the eight species within the study area in the north-east of Monks Wood. Despite these encouraging results, it was shown that the generalisation of models is unfeasible at this stage and many challenges remain. A wall-to-wall prediction of decline status confirmed the inability to generalise, yielding unrealistic results, with a high number of declined trees predicted. Identified weaknesses of the developed models indicate complexity related to the natural variability of heterogenous forests combined with the diverse symptoms of oak decline. Specific to the presented studies, additional limitations were attributed to limited ground truth, consequent overfitting,the binary classification of oak health status and uncertainty in UAV-acquired reflectance values. Suggestions for future work are given and involve the extension of field sampling with a non-binary dependent variable to reflect the severity of oak decline induced stress. Further technical research on the quality and reliability of UAV remote sensing data is also required

Investigating the Potential of UAV-Based Low-Cost Camera Imagery for Measuring Biophysical Variables in Maize

The potential for improved crop productivity is readily investigated in agronomic field experiments. Frequent measurements of biophysical crop variables are necessary to allow for confident statements on crop performance. Commonly, in-field measurements are tedious, labour-intensive, costly and spatially selective and therefore pose a challenge in field experiments. With the versatile, flexible employment of the platform and the high spatial and temporal resolution of the sensor data, Unmanned Aerial Vehicle (UAV)-based remote sensing offers the possibility to derive variables quickly, contactless and at low cost. This thesis examined if UAV-borne modified low-cost camera imagery allowed for remote estimation of the crop variables green leaf area index (gLAI) and radiation use efficiency (RUE) in a maize field trial under different management influences. For this, a field experiment was established at the university's research station Campus Klein-Altendorf southwest of Bonn in the years 2015 and 2016. In four treatments (two levels of nitrogen fertilisation and two levels of plant density) with five repetitions each, leaf growth of maize plants was supposed to occur differently. gLAI and biomass was measured destructively, UAV-based data was acquired in 14-day intervals over the entire experiment. Three studies were conducted and submitted for peer-review in international journals. In study I, three selected spectral vegetation indices (NDVI, GNDVI, 3BSI) were related to the gLAI measurements. Differing but definite relationships per treatment factor were found. gLAI estimation using the two-band indices (NDVI, GNDVI) yielded good results up to gLAI values of 3. The 3-bands approach (3BSI) did not provide improved accuracies. Comparing gLAI results to the spectral vegetation indices, it was determined that sole reliance on these was insufficient to draw the right conclusions on the impact of management factors on leaf area development in maize canopies. Study II evaluated parametric and non-parametric regression methods on their capability to estimate gLAI in maize, relying on UAV-based low-cost camera imagery with non-plants pixels (i.e. shaded and illuminated soil background) a) included in and b) excluded from the analysis. With regard to the parametric regression methods, all possible band combinations for a selected number of two- and three-band formulations as well as different fitting functions were tested. With regard to non-parametric methods, six regression algorithms (Random Forests Regression, Support Vector Regression, Relevance Vector Machines, Gaussian Process Regression, Kernel Regularized Least Squares, Extreme Learning Machine) were tested. It was found that all non-parametric methods performed better than the parametric methods, and that kernel-based algorithms outperformed the other tested algorithms. Excluding non-plant pixels from the analysis deteriorated models' performances. When using parametric regression methods, signal saturation occurred at gLAI values of about 3, and at values around 4 when employing non-parametric methods. Study III investigated if a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, b) those values were different from the ones previously reported in literature and c) there was a difference between RUEtotal and RUEgreen. Fractional cover and canopy reflectance was determined based on the RS imagery. Our study showed that RUEtotal ranges between 4.05 and 4.59, and RUEgreen between 4.11 and 4.65. These values were higher than those published in other research articles, but not outside the range of plausibility. The difference between RUEtotal and RUEgreen was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. In conclusion, UAV-based low-cost camera imagery allows for estimation of plant variables within a range of limitations