Comparing the effectiveness of hyperspectral and multispectral data in detecting citrus nitrogen and water stresses

Bhandari, Subodh; Raheja, Amar; Chaichi, M. R.; Pham, Frank; Sherman, Tristan; Dohlen, Matthew; Khan, Sharafat U.

doi:10.1117/12.2518822

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…It is 5.5 feet long and 2.5 feet high, and can be flown at a speed of 3.3 to 16.4 ft/sec for data collection of good quality. The UAV is equipped with an inertial measurement unit (IMU) and GPS receiver for autonomous flight [11]. Figure 2 shows the UAV equipped with the hyperspectral sensor.…”

Section: Uav and Hyperspectral Sensormentioning

confidence: 99%

“…A high-performance inertial measurement unit (IMU) and GPS receiver are integrated into the sensor. The IMU and GPS data are used for the orthorectification [11,12]. A Velodyne LiDAR is also integrated into the sensor.…”

Section: Uav and Hyperspectral Sensormentioning

confidence: 99%

“…The leaf chlorophyll is measured using a SPAD chlorophyll meter [11]. It is a non-destructive device that takes a reading of units of chlorophyll per area.…”

Section: Proximal Sensor and Ground-truth Data Collectionmentioning

confidence: 99%

“…Using a shortest distance algorithm, these coordinates are linked to the nearest bounding box, with index values averaged across the box [10]. This process is repeated for all the vegetation indices such as NDVI and MCARI [11], resulting in a single value per plant location. These indices map each section of the vineyard to a categorical measurement.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the potential of UAV-based hyperspectral sensor in detecting powdery mildew in grapes

Acosta,

Pena,

Sherafat

et al. 2024

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

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Traditional methods of powdery mildew (PM) detection involve visual inspection. However, the PM symptoms must already be visible when the damage is already done and disease is already spreading. Laboratory tests are more accurate than visual inspection, but are time consuming and cannot provide information for immediate decision making. Near infrared (NIR) and shortwave infrared (SWIR) sensors can see the reflected light in 700 nm to 2500 nm spectral range, thereby helping early detection of PM and other diseases. When subjected to PM stress, grapes undergo changes in spectral reflectance due to physiological and biochemical alterations in their leaves, such as decreased chlorophyll content, destroyed cell structure, or water stress. This paper presents an investigation on the potential of hyperspectral data acquired from vineyards using unmanned aerial vehicles (UAVs) in detecting powdery mildew in grapes. A UAV equipped with a hyperspectral sensor has been flown over a Cal Poly Pomona vineyard. The hyperspectral data is used to determine various vegetation indices including normalized difference texture index (NDTI), powdery mildew index (PMI), and normalized difference water index (NDWI) that can provide information on the presence of the disease and plant stresses due to the disease. These indices are compared with the ground-truth data that include visual inspection data and proximal sensor data such as chlorophyll meter and NDVI meter.

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Section: Uav and Hyperspectral Sensormentioning

confidence: 99%

Section: Uav and Hyperspectral Sensormentioning

confidence: 99%

“…The leaf chlorophyll is measured using a SPAD chlorophyll meter [11]. It is a non-destructive device that takes a reading of units of chlorophyll per area.…”

Section: Proximal Sensor and Ground-truth Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the potential of UAV-based hyperspectral sensor in detecting powdery mildew in grapes

Acosta,

Pena,

Sherafat

et al. 2024

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

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“…A comparison of the effectiveness of hyperspectral and multispectral data in detecting citrus nitrogen and water stresses can be found in [50]. The multispectral and/or hyperspectral data are used for determining water band index (WBI) as well as other vegetation indices.…”

Section: Water Stress Analysismentioning

confidence: 99%

Unmanned Aerial Vehicles in Agriculture: A Survey

et al. 2021

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The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts with a historical analysis of the use of aircrafts in agriculture, as pioneers of their use in modern precision agriculture techniques, currently applied by a high number of users. This survey has been carried out by providing a classification of the vehicles according to their typology and main sensorial and performance features. An extensive review of the most common applications and the advantages of using unmanned aerial vehicles is the core of the work. Finally, a brief summary of the key points of the legislation applicable to civil drones that could affect to agricultural applications is analyzed.

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Employing Simulators for Collision-Free Autonomous UAV Navigation

Blaga

Nedevschi

2022

2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)

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Comparing the effectiveness of hyperspectral and multispectral data in detecting citrus nitrogen and water stresses

Cited by 4 publications

References 11 publications

Investigating the potential of UAV-based hyperspectral sensor in detecting powdery mildew in grapes

Investigating the potential of UAV-based hyperspectral sensor in detecting powdery mildew in grapes

Unmanned Aerial Vehicles in Agriculture: A Survey

Employing Simulators for Collision-Free Autonomous UAV Navigation

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