Detection and mapping of trees infected with citrus gummosis using UAV hyperspectral data

Moriya, Érika Akemi Saito; Imai, Nilton Nobuhiro; Tommaselli, Antônio Maria Garcia; Berveglieri, Adilson; Santos, Guilherme Henrique; Soares, Márcio Augusto; Marino, Marcelo; Reis, Thiago Tiedtke dos

doi:10.1016/j.compag.2021.106298

Cited by 27 publications

(11 citation statements)

References 15 publications

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“…More broadly, the most economically important tree crop diseases, such as wilt, rust, powdery mildew and anthracnose, exhibit consistent, identifiable phenotypic changes in canopy shape or colour in their early stages on pine, rubber, chocolate, cacao and palms before defoliation. A combination of high‐resolution LiDAR data, CNN training and ground‐survey data collection hold promise for identifying these diseases at large scales (Abdulridha et al., 2020; Liu et al., 2021; Moriya et al., 2021; Yu et al., 2021; Zeng et al., 2022).…”

Section: Discussionmentioning

confidence: 99%

Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR

Wang,

Huben,

Boucher

et al. 2024

Methods Ecol Evol

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Ecologists, foresters and conservation practitioners need ‘biodiversity scanners’ to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using remote sensing methods remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these ‘extended phenotypes’ could serve as environmental landmarks of insect presence in remotely sensed data. To test the feasibility of this approach, we studied symbiotic ants that alter the canopy shape of whistling thorn acacias (Acacia [syn. Vachellia] drepanolobium), a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol for using light detection and ranging (LiDAR) data to collect, prepare (including a customizable tree‐segmentation algorithm) and apply a convolutional neural network‐based classification for the detection of ant‐inhabited acacia tree phenotypic variations. Applying this protocol enabled us to effectively detect intra‐specific tree phenotypic variation induced by insects. Surveying ant occupancy across 16 ha and 9680 acacia trees took 1000 work hours, whereas surveyed patterns of ant distribution were replicated by our trained classifier using only an hour‐long airborne LiDAR collection time. We suggest that large‐scale surveys of insect occupancy (including insect‐vectored disease) can be automated through a combination of airborne LiDAR and machine learning.

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Section: Discussionmentioning

confidence: 99%

Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR

Wang,

Huben,

Boucher

et al. 2024

Methods Ecol Evol

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“…In recent years, the utilization of UAV remote sensing technology has gained significant attention in a variety of fields, such as agriculture [80][81][82][83], forestry [84][85][86], environmental monitoring [87][88][89], and infrastructure inspection [55,67,90]. UAVs provide high-resolution and accurate data that can facilitate informed decision-making and support diverse applications.…”

Section: Uav Remote Sensing Applicationmentioning

confidence: 99%

“…Deng et al [127] proposed a multi-feature fusion citrus yellow dragon disease recognition method based on hyper-spectral camera data, and the recognition accuracy on the verification dataset reached 99.73%. Moriya et al [82] examined the spectral band that is most easily distinguished for HLB disease using hyper-spectral camera data. The authors found that the reflected energy of the diseased strain was significantly stronger than that of the normal strain at 460 nm.…”

Section: Remote Sensing For Precision Agriculturementioning

confidence: 99%

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

Zhang

Zhu

2023

Drones

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In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.

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“…P. nicotianae), P. citrophthora, and P. palmivora (Graham and Timmer 1994;Graham et al 2013;Sawake et al 2022). PFRD is present in all citrus-growing regions worldwide and causes substantial losses, estimated at 10-30% each year (Gade and Lad 2019; Moriya et al 2021; Kaur et al 2021). PFRD symptoms are visible at the graft line, or directly on the neck of ungrafted citrus trees close to the ground (Asim et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Structural characteristics of cocoa-based agroforestry systems can mitigate citrus Phytophthora foot rot disease intensity

MVONDO

NOMO

AMBANG

et al. 2023

Preprint

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Tropical agroforestry systems are recognized as an attractive way to reconcile productivity and ecosystem services without compromising the agrosystem. However, to optimize ecosystem services, the structure of existing agroecosystems must be improved to enhance interactions within them. In this study, we evaluated the combined effect of shade rate and spatial structure of citrus trees in cocoa-based agro-forestry systems (CBAS) on Phytophthora foot rot disease (PFRD). A set of 33 CBAS of 2500 m2 was established in five citrus production basins in the humid forest zones of Cameroon. The citrus spatial structure was analyzed by the Ripley method. Field data, including the specific composition of the experimental plots, the tree’s geographical position, as well as their structural characteristics, were used to reconstruct CBAS in 3D. Subsequently, citrus trees were divided into 9 treatments based on the amount of shade they received (dense shade, light shade, or no shade) and their spatial structure (regular, aggregated, or random) in the CBAS. PFRD intensity was calculated by measuring the extent of the PFRD lesion relative to the circumference of the citrus crown. Citrus trees with a regular spatial structure, and located under dense or light shade showed low PFRD intensity. Citrus trees with an aggregated spatial structure, regardless of their shade situation, as well as those with a random spatial structure and no shade, showed high PFRD intensity compared to all other treatments. These results show that appropriate management of shade rate and spatial structure in CBAS can significantly contribute to the control of PFRD and thus promote citrus production in complex agroforests. For the first time, it is demonstrated that optimizing the combined effect of shade rate and spatial structure of associated trees would allow the design of optimal models of resilient agroforestry systems, capable of self-regulating pests and reducing chemical pesticide use.

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Detection and mapping of trees infected with citrus gummosis using UAV hyperspectral data

Cited by 27 publications

References 15 publications

Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR

Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

Structural characteristics of cocoa-based agroforestry systems can mitigate citrus Phytophthora foot rot disease intensity

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