Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making

Li, Guan-Sin; Wu, Dong-Hong; Su, Yuan-Chih; Kuo, Bo‐Jein; Yang, Ming‐Der; Lai, Ming-Hsin; Lu, Hsiu-Ying; Yang, Chin-Ying

doi:10.3390/agronomy11081626

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…The NDVI and Normalized Difference Red Edge (NDRE) are valuable vegetation indices to assess the level of nutrients and health of rice plants and crops. Both NDVI and NDRE have shown a positive correlation with the rice plant's nitrogen level, respectively 0.83 and 0.82, based on Li et al (2021) study. Rehman et al (2019) also support the application of NDVI as the most popular and valuable tool to be applied in the assessment and prediction of grain rice yield.…”

Section: Remote Sensing and Vegetation Algorithms Indices Application...mentioning

confidence: 60%

Agronomic Assessment on Paddy Field Using Aerial Imagery and Vegetation Indices: A Review

Sarifudin,

Che' Ya,

Fazlil Ilahi

et al. 2023

Adv Agric Food Res J

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Rice is consumed by the majority of Southeast Asians, particularly in Malaysia itself. As years pass by, rice production has not to inclined to align with a number of populations in Malaysia that keeps growing, thus the self-sufficiency level (SSL) of 100% is not achieved until today. The intention of the government in expanding the granary areas for rice farming by concerning the SSL target to be achieved. Despite that, crop management specifically paddy farming is tedious and requires a lot of manpower. However, as the technology develops and gets sophisticated these days, a discipline that helps in crop management has been introduced over the years called remote sensing. Remote sensing is undoubtedly showed significant application in crop management. The vegetation indices (VIs) algorithm is a branch in remote sensing that enables researchers to evaluate vegetative covers on the ground from aerial view by using spectral measurements either qualitatively or quantitatively. The VIs obtains from the spectral response of the ground areas from the aerial view commonly composed of vegetation areas, soil brightness, various vegetation on the ground, canopy, environmental effects, shadow, and moisture level of soil and plants. Nonetheless, the favourable of remote sensing is the spatial-temporal which helps in determining changes of vegetation over a certain period. This also includes rice farming. Thus, this paper discusses and summarizes the VIs algorithm that was relevant in the application of rice planting. It proposes the popularity and relevance of the VIs algorithm in rice crop management.

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Section: Remote Sensing and Vegetation Algorithms Indices Application...mentioning

confidence: 60%

Agronomic Assessment on Paddy Field Using Aerial Imagery and Vegetation Indices: A Review

Sarifudin,

Che' Ya,

Fazlil Ilahi

et al. 2023

Adv Agric Food Res J

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“…In that study different irrigation methods and nitrogen fertilizer levels were evaluated. Results of that study indicated that plant nitrogen and chlorophyll content at the maximum tillering stage were signi cantly in uenced by the interaction between water and fertilizer (Li, G.-S et al, 2021). Furthermore, NDVI obtained from the multispectral images captured by the sensors, correlated well with plant nitrogen content and rice growth stages.…”

Section: Introductionmentioning

confidence: 91%

“…Furthermore, where the GreenSeeker was used the apparent fertilizer recovery was 70-75% compared with the 60% recovery with the recommended rate. In another study conducted in Taiwan Agricultural Research Institute in Taiwan, water management with eld sensors was carried out for water and fertilizer use e ciency (Li, G.-S et al, 2021). In that study different irrigation methods and nitrogen fertilizer levels were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Handheld NDVI Sensor-based Rice Productivity Assessment under Combinations of Fertilizer Soil Amendment and Irrigation Water Management in Lower Moshi Irrigation Scheme, Tanzania

Kimaro

Gebre

Hieronimo

et al. 2022

Preprint

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Handheld Optical Sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, Tanzania. The study was carried out in an experimental design consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rate of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (r) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertiliser combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy respectively (p < 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (p < 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly less than 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results from this study have demonstrated a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. The study conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management.

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“…In that study, different irrigation methods and N fertilizer levels were evaluated. Results of that study indicated that plant N and chlorophyll content at the maximum tillering stage were significantly influenced by the interaction between water and fertilizer (Li et al 2021). Furthermore, NDVI obtained from the multispectral images captured by the sensors, correlated well with plant nitrogen content and rice growth stages (Mckenzie et al 2000).…”

Section: Introductionmentioning

confidence: 98%

“…Furthermore, where the GreenSeeker was used the apparent fertilizer recovery was 70-75% compared with the 60% recovery with the recommended rate. In another study conducted in Taiwan, water management with field sensors was carried out for water and fertilizer use efficiency (Li et al 2021). In that study, different irrigation methods and N fertilizer levels were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania

et al. 2023

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Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (r) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (P < 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (P < 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly < 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results demonstrate a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. Thus, we conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management. This allows future multi-functional land management within the soil–water-food nexus.

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Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making

Cited by 8 publications

References 36 publications

Agronomic Assessment on Paddy Field Using Aerial Imagery and Vegetation Indices: A Review

Agronomic Assessment on Paddy Field Using Aerial Imagery and Vegetation Indices: A Review

Handheld NDVI Sensor-based Rice Productivity Assessment under Combinations of Fertilizer Soil Amendment and Irrigation Water Management in Lower Moshi Irrigation Scheme, Tanzania

Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania

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