The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

Mwinuka, Paul Reuben; Mbilinyi, Boniface; Mbungu, Winfred; Mourice, Sixbert K.; Mahoo, H. F.; Schmitter, Petra

doi:10.1016/j.agwat.2020.106584

Cited by 44 publications

(23 citation statements)

References 47 publications

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“…Osco et al used NDVI, NDRE, the green normalized difference vegetation (GNDVI), and the soil-adjusted vegetation index (SAVI) to predict maize leaf nitrogen concentration (LNC) [ 29 ]. For water status prediction, SAVI [ 25 ], the normalized green-red difference index (NGRDI) [ 26 ], NDVI [ 30 ], NDRE [ 31 ] and so on, were reported in different studies for different crops. One of the main reasons why different spectral features are used in different crops is that the physiological characteristics and canopy distribution characteristics of different crops are different.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

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Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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“…For example, it was found that the deviation of sensor-measured ET c from true ET c ranged from À14% to À31% in silt loam soil (Sharma et al, 2021). Consequently, researchers have moved towards finding new hot technologies for precision agriculture using plant-based methods because symptoms of canopy stress can be identified much earlier than symptoms in soil (Mwinuka et al, 2021).…”

Section: Rs-based Irrigation Managementmentioning

confidence: 99%

A review on smart irrigation management strategies and their effect on water savings and crop yield

Touil

Richa

Fizir

et al. 2022

Irrigation and Drainage

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Appropriate irrigation water management has attracted a considerable degree of attention because it is crucial to guarantee water and food security worldwide. A smart irrigation system is applied for the rational use of water in agricultural fields and for maximizing crop yield. Thus, the high demand for water resources is decreased, and the adverse environmental impacts of irrigation are reduced. Different irrigation approaches were developed to reduce overirrigation by assessing the soil moisture content or crop water stress index. This paper aims to review conventional and smart irrigation technologies and discuss their effect on water savings, yield, and crop quality. The analysis of prominent studies highlighted that the water use efficiency based on soil moisture sensors depends on the volumetric moisture content threshold value set by farmers. Soil moisture sensor controllers, evapotranspiration controllers, and rain sensors have been shown to save 20%-92%, 20%-71%, and 7%-50% of water, respectively, while maintaining crop growth and quality. The optical sensor-based system showed a better ability to assess crop and soil variability in the field than the on-site measurements. The conventional controlled deficit irrigation technique was found to decrease the crop yield to less than 25% with water savings of up to 13%.

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“…Remote sensing is an effective approach to assess phonological changes in large-scale of rice fields. Multispectral cameras are useful for monitoring and evaluating crop conditions with accuracy and precision, including near-infrared and thermal infrared (TIR) cameras [1,2,3,4]. The leaves' canopy structure with chlorophyll and water content are mainly accessible in the solar reflective domain, i.e., from the visible to the short-wave infrared [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…One of the popular methods for plant monitoring is the plant canopy-based method with destructive or non-destructive measurements [1,3,9]. Remote sensing is the popular non-destructive canopy measurement to detect canopy temperature and vegetation indices (VIs) [3,4,10], which the signs of canopy stress detected earlier than ground-based signs [1]. The canopy temperature can detect crop water stress indicator (CWSI) by using the different temperatures between canopy and air temperature [11,12].…”

Section: Introductionmentioning

confidence: 99%

Monitoring responses of NDVI and canopy temperature in a rice field to soil water and meteorological conditions

Irsyad

Oue

Mon

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Aerial photography of the vegetation canopy is an effective approach for spatial monitoring and evaluating physiological plant conditions precisely and accurately. This study aimed to obtain the relationship between the response of NDVI and canopy temperature in the rice field to soil water (before and after the irrigation process) and meteorological variables. This study was conducted in Matsuyama, Japan, with a rice cultivar (Oryza sativa L. cv. Koshihikari) and focused on the heading and flowering stage (July 31 - August 10, 2020). Aerial photographs were successfully detected the spatial variation of NDVI and canopy temperature across the rice fields. Taken NDVI by the UAV camera was successfully correlated with the SPAD value at the same point (R2= 0.96), and thermal imagery by UAV also successfully correlated with the hand-held temperature measurement at the same point (R2=0.84). NDVI values in the wet soil condition of the rice field were higher than those in the dry condition. Moreover, the canopy temperature in the dry area was higher than in the wet area by an average difference of 0.63 °C. The yield with high soil water content was significantly higher (P=0.03) than those in the lower condition by 0.28 kg/m2.

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The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

Cited by 44 publications

References 47 publications

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

A review on smart irrigation management strategies and their effect on water savings and crop yield

Monitoring responses of NDVI and canopy temperature in a rice field to soil water and meteorological conditions

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