Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape

Wan, Lijuan; Li, Yijian; Cen, Haiyan; Zhu, Jiangpeng; Yin, Wenxin; Wu, Weikang; Zhu, Hongyan; Sun, Dawei; Zhou, Weijun; He, Yong

doi:10.3390/rs10091484

Cited by 98 publications

(73 citation statements)

References 53 publications

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“…Vegetation biomass [22,103] nitrogen status [22,99,103,110] moisture content [109,110] vegetation color [49,54] spectral behavior of chlorophyll [64,99] temperature [64,69] spatial position of an object [32,106] size and shape of different elements and plants vegetation indices [54][55][56] Soil moisture content [109,112] temperature [66,69] electrical conductivity [66] With the use of specialized sensors, UAVs can acquire information for various features of the cultivated field. However, as mentioned above, there is still no standardized workflow or well established techniques to follow for analyzing and visualizing the information acquired.…”

Section: Crop Featuresmentioning

confidence: 99%

“…Classification methods are also very commonly used for weed mapping [18,33,44,73,106,111] and disease detection [46,77,97]. The most popular and precise classification techniques are the Artificial Neural Networks (ANNs) family [18,44,73,104] and the Random Forest algorithm [22,38,49]. These algorithms directly use the RGB colors, the intensity, spectral information or other features derived from the image acquired.…”

Section: Using Machine Learningmentioning

confidence: 99%

“…The visible channel that is no longer captured by the modified RGB sensor is often captured by using another embedded RGB sensor. The use of both multispectral and visible sensors was observed in many cases [22,23,27,31,33,49,50,55,56,61,65,69,72,87,103,107,113,117].…”

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confidence: 99%

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A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Section: Crop Featuresmentioning

confidence: 99%

Section: Using Machine Learningmentioning

confidence: 99%

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A Review on UAV-Based Applications for Precision Agriculture

2019

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“…Considering remotely sensed variables, traditional spectral indices such as VIs or CIs have been widely used in UAV RS-based crop biomass estimation [57], flower count [58], vegetation detection [59], yield prediction [19,48], and other applications of precision agriculture [60]. On the other hand, other studies have also used spatial information derived from UAV-based imagery, such as digital crop surface models (CSMs) [36], degree of canopy cover [61], plant height [36], and textures [49] for crop growth monitoring.…”

Section: Potential Of Consumer-grade Uav-based Digital Imagery For Crmentioning

confidence: 99%

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

et al. 2019

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Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

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“…The technology of RPASs for areas such as precision agriculture is drawing increasing attention from different sectors interested in seeking real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Converting a Fixed-Wing Internal Combustion Engine RPAS into an Electric Lithium-Ion Battery-Driven RPAS

et al. 2020

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It is well proved that remotely piloted aircraft systems (RPASs) are very useful systems for remote sensing in precision agricultural labors. INTA (National Institute for Aerospace Applications) and the University of Huelva are involved in Tecnolivo Project that proposes the development of a marketable and easy-to-use technological solution that allows integrated, ecological, and optimized management of the olive grove through non-invasive monitoring of key agronomic parameters using RPASs. The information collected by the RPAS in regards to the state of the vegetation, such as hydric stress levels, plague detection, or maturation of the fruit, are very interesting for farmers when it comes to make decisions about their crops. Current RPAS applications for precision agriculture are mainly developed for small- to medium-sized crops using rotary-wing RPASs with small range and endurance operation, leaving aside large-sized crops. This work shows the conversion of a fully declassified and obsolete fixed-wing internal combustion engine (ICE) remotely piloted aircraft (RPA), used as aerial target for military applications and in reconnaissance and surveillance missions at low cost, into an electric lithium polymer (LiPo) battery-driven RPA that will be used for precision agriculture in large-sized crop applications, as well as other applications for tracking and monitoring of endangered animal species in national parks. This RPA, being over twenty years old, has undergone a deep change. The applied methodology consisted of the design of a new propulsion system, based on an electric motor and batteries, maintaining the main airworthiness characteristics of the aircraft. Some other novelties achieved in this study were: (1) Change to a more efficient engine, less heavy and bulky, with a greater ratio of torque vs. size. Modernization of the fly control system and geolocation system. (2) Modification of the type and material of the propeller, reaching a higher performance. (3) Replacement of a polluting fuel, such as gasoline, with electricity from renewable sources. (4) Development of a new control software, etc. Preliminary results indicate that the endurance achieved with the new energy and propulsion systems and the payload weight available in the RPA meet the expectations of the use of this type of RPAS in the study of large areas of crops and surveillance.

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Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape

Cited by 98 publications

References 53 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

Converting a Fixed-Wing Internal Combustion Engine RPAS into an Electric Lithium-Ion Battery-Driven RPAS

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