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 Monitoring of Rice Crop Variables using an UAV Robotic System

This paper presents the integration of an UAV for the autonomous monitoring of rice crops. The system integrates image processing and machine learning algorithms to analyze multispectral aerial imagery. Our approach calculates 8 vegetation indices from the images at each stage of rice growth: vegetative, reproductive and ripening. Multivariable regressions and artificial neural networks have been implemented to model the relationship of these vegetation indices against two crop variables: biomass accumulation and leaf nitrogen concentration. Comprehensive experimental tests have been conducted to validate the setup. The results indicate that our system is capable of estimating biomass and nitrogen with an average correlation of 80% and 78% respectively

Utilizing unmanned aircraft system (UAS) technology to collect early stand counts and to assess early plant vigor for use in early-season stress tolerance characterization of hybrid corn products

Early-season stress tolerance characterization of hybrid corn products relies heavily on early stand count and early vigor data from field trials in order to properly characterize products and to accurately assign stress emergence scores. The current manual collections of these data are labor-intensive, time-consuming, prone to human error, and in the case of vigor scoring, subjective. Unmanned aircraft systems (UAS) may provide a more accurate, rapid, objective, and efficient method for collecting stand count and vigor data resulting in higher quality products and overall cost-savings. The purpose of this study was to determine if UAS could be used for stand count and vigor data collection for the early-season stress tolerance characterization of hybrid corn products. The early-season stress tolerance characterization field trial was flown on 12 different dates during the spring of 2017 representing plant growth stages from VE to V5. Stand count and plot cover values were calculated from the UAS obtained images for the 12 flight dates using a 2017 and an updated 2018 software algorithm. It was determined that the best time to collect UAS stand count data occurred at the V2 plant growth stage before leaf overlapping occurred. An UAS derived plot cover normalization method was also developed for assigning plot vigor scores allowing for more objective, reproducible, and unbiased assessments of plot vigor

Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds

Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at -45⸰_ and horizontally at 0⸰_ (VA -45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Hyperspectral, thermal and LiDAR remote sensing for red band needle blight detection in pine plantation forests

PhD ThesisClimate change indirectly affects the distribution and abundance of forest insect pests and pathogens, as well as the severity of tree diseases. Red band needle blight is a disease which has a particularly significant economic impact on pine plantation forests worldwide, affecting diameter and height growth. Monitoring its spread and intensity is complicated by the fact that the diseased trees are often only visible from aircraft in the advanced stages of the epidemic. There is therefore a need for a more robust method to map the extent and severity of the disease. This thesis examined the use of a range of remote sensing techniques and instrumentation, including thermography, hyperspectral imaging and laser scanning, for the identification of tree stress symptoms caused by the onset of red band needle blight. Three study plots, located in a plantation forest within the Loch Lomond and the Trossachs National Park that exhibited a range of red band needle blight infection levels, were established and surveyed. Airborne hyperspectral and LiDAR data were acquired for two Lodgepole pine stands, whilst for one Scots pine stand, airborne LiDAR and Unmanned Aerial Vehicle-borne (UAV-borne) thermal imagery were acquired alongside leaf spectroscopic measurements. Analysis of the acquired data demonstrated the potential for the use of thermographic, hyperspectral and LiDAR sensors for detection of red band needle blight-induced changes in pine trees. The three datasets were sensitive to different disease symptoms, i.e. thermography to alterations in transpiration, LiDAR to defoliation, and hyperspectral imagery to changes in leaf biochemical properties. The combination of the sensors could therefore enhance the ability to diagnose the infection.Natural Environment Research Council (NERC) for funding this PhD program (studentship award 1368552) and providing access to specialist equipment through a Field Spectroscopy Facility loan (710.114). I would like to thank NERC Airborne Research Facility for providing airborne data (grant: GB 14-04) that made the PhD a challenge, to say the least. My sincere gratitude goes to the Douglas Bomford Trust for providing additional funds, which allowed for completion of the UAV-borne part of this research

Advancing American Chestnut (Castanea Dentata) Restoration Through Science, GIS And Partnerships

The American chestnut (Castanea dentata) was once a prominent hardwood species of the eastern United States forests. From Maine to Alabama, the chestnut provided many ecosystem and economic services to wildlife and humans alike. After an accidental importation of chestnut blight (Cryphonectria parasitica) from Asia, billions of American chestnuts succumbed to the disease. Since the 1980s, researchers have been working to develop a fungal blight-tolerant chestnut in hopes of restoring the species. By the early 1990s, Dr. William Powell and his fellow scientists at the State University of New York College of Environmental Science and Forestry (SUNY-ESF) successfully transformed an American chestnut that codes for the enzyme oxalate oxidase (OxO), a gene expression that provides resistance to oxalic acid by an oxalate-producing fungi (Powel et al. 2019). Oxalate oxidase reduces the acidity, and therefore the deadliness of the fungus’s oxalic acid used to kill the American chestnut, thereby protecting the species against severe damage from blight infection. This genetically engineered tree has been rigorously studied in the laboratory and is now increasingly in the field and is poised to be approved by the federal government for widespread restoration. This thesis addresses three questions fundamental to using transgenic American Chestnut trees in restoration: First, how does the viability of transgenic pollen change over time? Secondly, how do transgenic American chestnut trees perform in a field setting compared to other types of chestnuts? Third, how can Geographic Information Systems (GIS) and aerial drone technology aid in conducting spatially explicit field experiments such as this one? Pollen from transgenic trees is critical to restoration. Transgenic pollen carries over the OxO enzyme gene from the father to their offspring during outcrossing. Blight tolerant offspring that inherit the OxO gene through Mendelian genetics are more likely to survive in the wild, as fungal blight is widespread in the environment. The OxO gene provides offspring with a natural chemical defense mechanism against threats posed by blight. Transgenic pollen produced in the laboratory was shipped throughout the country to produce blight resistant offspring during controlled outcrossing. I studied pollen viability in the laboratory and the field to help guide these efforts. Transgenic pollen was collected from greenhouse-grown transgenic trees, desiccated in granular desiccant at 4°C, and freezer stored at -80°C from 19 June, 2020 to 20 July, 2021. Pollen was used in controlled pollinations in Maine and shipped across the tree’s native range. Pilkey (2021) found that pollen stored at -80 °C remained viable for up to 8 months after collection. I tested pollen stored from 1 to 13 months to ascertain viability and source variability between 2020 and 2021. I tested pollen viability using a sucrose-based germination medium (as she did) and in controlled field pollinations. For pollen tube development (a viable pollen grain produces a tube), Pilkey (2021) set a ballpark estimate for successful results at 30%. Drawing from Pilkey (2021), I too consider pollen tube development near or exceeding the 30% level to be an adequate viability level. In my research, pollen viability varied substantially by age and source, but all ages and sources were shown to be effective at producing both pollen tubes and fertile nuts, even after 13 months in storage. As of 2021, a one-acre field in Cape Elizabeth, Maine was granted permission by the United States Department of Agriculture (USDA) to plant genetically engineered American chestnuts for the first time in New England. At this field site, I studied the height growth of transgenic chestnuts over their first growing season compared with their non-transgenic full siblings and other controls. In this orchard, the University of New England (UNE) transgenic chestnut team compares transgenic, non-transgenic full siblings, Chinese, f1 hybrid or first-generation Chinese American hybrid, and backcrossed advanced-generation hybrid trees (see Table 1.1). Seedlings were raised from seed in two greenhouses at UNE and planted in the field in a randomized design between 14 May to 18 May 2021, when trees were 5.5 to 6 months old. We examined the roles of the type of seedling, greenhouse conditions, and seedling conditions at planting on the growth rates of seedlings. Despite varying seedlings’ initial health conditions, they grew similar heights before being outplanted in the field. After the first field season, healthier seedlings depicted as pathogen – seedlings (refer to Chapter 3 Methods) grew statistically larger in height than sick seedlings depicted as pathogen + seedlings. Additionally, seedlings bred in more southern sites grew successfully in Maine during year one, suggesting growth in historically northern chestnut native range possible. Transgenic seedlings grew similarly to their non-transgenic full siblings, suggesting that, thus far, the inserted gene from wheat does not impede growth in the field. Lastly, offspring with some European sativa genes inherited from Maine mothers ULL, UNU, and USU supported strong height growth trends in Maine, like Ashdale seedlings in New York. The two greatest factors on height growth for seedlings after year one were the influence on sativa gene inheritance by offspring and seedlings’ initial growth in a pathogen – environment. Geographic Information Systems (GIS) is a tool that holds great promise for applications to spatially explicit problems. At the same time, drone-captured aerial imagery provides high resolution spatial data that can be used to advance research efforts. However, little work is being done combining drone-captured aerial images and ArcGIS. I incorporated drone-captured aerial images with ArcGIS Pro to map the seedlings’ performances for the Cape Elizabeth field site. I created two maps to help visualize the growth trends of seedlings after the first year’s field season and as a baseline for multi-year future analysis and model for the parallel SUNY-ESF orchard

Cage row arrangement affects the performance of laying hens in the hot humid tropics

Although the traditional cage system of housing laying hens is gradually being faced out due to welfare reasons, cages are still common in most developing tropical countries in different arrangements. In a 12-week experiment, the effects of a three cage row arrangement on hen-day production and egg qualities of Shaver Brown hens was studied. Data were collected from 2 layer sheds housing 9,000 hens in a 3-cage row arrangement (southern row, northern row and middle row) with 3,000 hens per row. Data were analysed for a randomized complete block design where cage rows were the treatments and weeks the blocks. Results showed no significant effects of cage row arrangement on feed intake, hen-day production, per cent yolk and Haugh unit (P>0.05). Egg weight, egg mass and per cent shell were significantly reduced and feed conversion ratio increased on the middle row (P<0.05). Egg weight, egg mass, per cent shell and feed conversion ratio did not differ between the side rows (P>0.05). These results suggest that battery cage row arrangement may not affect the rate of lay but egg weight, egg mass and efficiency of feed utilisation may be adversely affected in hens housed in the middle row. These findings have both economic and welfare implications