Sensors for measuring plant phenotyping: A review

Qiu, Ruicheng; Wei, Shuang; Zhang, Man; Li, Han; Sun, Han‐Dong; Liu, Gang; Li, Minzan

doi:10.25165/j.ijabe.20181102.2696

Cited by 60 publications

(67 citation statements)

References 150 publications

(213 reference statements)

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“…In this context, phenotyping focuses on the characterization of morphological as well as physiological crop traits. Morphological parameters, such as plant height, stem diameter, leaf area or leaf area index (LAI), leaf angle, stalk length, and in-plant space [1], can be determined with LiDAR (light detection and ranging). Research on phenotyping using LiDAR often focusses on one specific crop, for example, wheat [2,3] or cotton [4].…”

Section: Introductionmentioning

confidence: 99%

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

2019

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Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m 2 . The 3DPI algorithm proved to estimate biomass well for sugar beet (R 2 = 0.68, RMSE = 17.47 g/m 2 ) and winter wheat (R 2 = 0.82, RMSE = 13.94 g/m 2 ). Also, the height estimates worked well for sugar beet (R 2 = 0.70, RMSE = 7.4 cm) and wheat (R 2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R 2 = 0.50, RMSE = 12 cm) and biomass estimation (R 2 = 0.24, RMSE = 22.09 g/m 2 ), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly. good correlation with in situ field measurements of plant height. Sun et al. [4] published an R 2 of 0.98 for cotton plants, [2] published an R 2 of 0.99 for wheat, and [7] showed an R 2 of 0.90 for wheat. These studies show the capability of LiDAR to measure basic phenotypes such as plant height. However, LiDAR systems mounted on tractors can be unsuitable for labour-intensive crops such as rice or in orchards [8], for example, due to compaction of the soil [9]. A UAV equipped with a LiDAR system can overcome those limitations.Earlier research on the relation between plant height and biomass was based on varying approaches for plant height measurements. Madec et al. [7] found an R 2 of 0.88 for the correlation between plant height and field-measured biomass, using a tractor based LiDAR system. Bendig et al.[10] used a structure from a motion (SfM) technique on UAV acquired imagery to derive plant height and found an R 2 of 0.81 between field-measured height and SfM derived height.In the last few years, LiDAR systems have been miniaturized, resulting in lower weights and reduced dimensions, and as a result, can be operated from UAVs. This development opens the way towards high throughput derived, more complex products like biomass and yield, thus, improving the speed and frequency at which these plant traits can be acquired in the field in an undisturbed way.LiDAR-based biomass estimates of agricultural crops can be derived in different ways. Based on the Lambert-Beer LAI model of [11], the authors of [2] developed a biomass prediction model called the 3-Dimensional Profile Index (3DPI). Where the LIDAR 3D point cloud is divided into layers...

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Section: Introductionmentioning

confidence: 99%

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

2019

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“…Otherwise, we will suffer from food insecurity problems which will be the biggest threat. Moreover, Qiu and coworkers [9] revealed that the progress of the agriculture production is not only significant for to producing food to feed the population, but it is also essential for the industrial sector. Similarly, the agriculture is the main source to produce the raw material for many industrial sectors.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Control Systems in Agriculture Using Intelligent Sensor Techniques: A Review of the Aeroponic System

et al. 2018

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In recent years, intelligent sensor techniques have achieved significant attention in agriculture. It is applied in agriculture to plan the several activities and missions properly by utilising limited resources with minor human interference. Currently, plant cultivation using new agriculture methods is very popular among the growers. However, the aeroponics is one of the methods of modern agriculture, which is commonly practiced around the world. In the system, plant cultivates under complete control conditions in the growth chamber by providing a small mist of the nutrient solution in replacement of the soil. The nutrient mist is ejected through atomization nozzles on a periodical basis. During the plant cultivation, several steps including temperature, humidity, light intensity, water nutrient solution level, pH and EC value, CO2concentration, atomization time, and atomization interval time require proper attention for flourishing plant growth. Therefore, the object of this review study was to provide significant knowledge about early fault detection and diagnosis in aeroponics using intelligent techniques (wireless sensors). So, the farmer could monitor several paraments without using laboratory instruments, and the farmer could control the entire system remotely. Moreover, the technique also provides a wide range of information which could be essential for plant researchers and provides a greater understanding of how the key parameters of aeroponics correlate with plant growth in the system. It offers full control of the system, not by constant manual attention from the operator but to a large extent by wireless sensors. Furthermore, the adoption of the intelligent techniques in the aeroponic system could reduce the concept of the usefulness of the system due to complicated manually monitoring and controlling process.

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“…Images are captured to generate a DSM with photogrammetric techniques based on a structure from motion (SfM) algorithm. The plant height can be calculated by subtracting a digital terrain model (DTM), which represents the field with no plants, from the DSM, which includes the plants [59,62,63]. However, the estimated plant height can show a low correlation with the actual plant height due to the spatial resolution of RGB sensors and the spiky form of the target plants [17,64].…”

Section: Rgb Digital Camerasmentioning

confidence: 99%

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Jang

Kim

et al. 2020

Remote Sensing

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Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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Sensors for measuring plant phenotyping: A review

Cited by 60 publications

References 150 publications

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Monitoring and Control Systems in Agriculture Using Intelligent Sensor Techniques: A Review of the Aeroponic System

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

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