Leaf nitrogen determination using non-destructive techniques–A review

© 2017 Taylor & Francis Group, LLC. The optimisation of plant nitrogen-use-efficiency (NUE) has a direct impact on increasing crop production by optimising use of nitrogen fertiliser. Moreover, it protects environment from negative effects of nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture systems has been major focus of many researchers. Improvement of NUE can be achieved through several methods including more accurate measurement of foliar N contents of crops during different growth phases. There are two types of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and time-consuming, as they require tissue sampling and subsequent laboratory analysis. Thus, many farmers find destructive methods to be less attractive. Non-destructive methods are rapid and less expensive but are usually less accurate. Accordingly, improving the accuracy of non-destructive N estimations has become a common goal of many researchers, and various methods varying in complexity and optimality have been proposed for this purpose. This paper reviews various commonly used non-destructive methods for estimating foliar N status of plants

Leaf Nitrogen Determination using Handheld Meters

The maximisation of plant nitrogen-use-efficiency (NUE) has direct impact on increasing crop production, due to the increase of agronomic value of the fertiliser. Moreover, optimising the use of nitrogen will reduce the adverse effect on the environment that can be caused by nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture has been the focus of many researchers. Improvement of NUE can be achieved through measurement of the foliar N content of crops during growth. There are two classes of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and time-consuming as they require tissue sample collection and laboratory analysis. Non-destructive methods are rapid and less expensive, but usually less accurate. Improved accuracy is the goal of many researchers and various methods have been proposed that vary in complexity and optimality. This paper highlights non-destructive handheld methods to estimate foliar N status of plants

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Image Analysis using Color Co-occurrence Matrix Textural Features for Predicting Nitrogen Content in Spinach

This study aimed to determine the nitrogen content of spinach leaves by using computer imaging technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural features constructed from RGB and grey colors. From the 40 textural features, the best features-subset was selected by using features selection method. Features selection method can increase the accuracy of image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of ANN. The computer vision method using ANN model which has been developed can be used as non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate application of their nitrogen fertilization strategies using low cost computer imaging technology

Use of consumer-grade cameras to assess wheat N status and grain yield

Relationships between (a) fractional Intercepted PAR (fIPAR), and (b) aboveground biomass (Biomass) and (c) grain yield at harvest with the Normalized Difference Vegetation Index (NDVI) derived either from a spectroradiometer or a conventional camera at final grain filling (n = 12).Postprint (published version

Assessment of RGB vegetation indices to estimate chlorophyll content in sugar beet leaves in the final cultivation stage

Estimation of chlorophyll content with portable meters is an easy way to quantify crop nitrogen status in sugar beet leaves. In this work, an alternative for chlorophyll content estimation using RGB-only vegetation indices has been explored. In a first step, pictures of spring-sown ‘Fernanda KWS’ variety sugar beet leaves taken with a commercial camera were used to calculate 25 RGB indices reported in the literature and to obtain 9 new indices through principal component analysis (PCA) and stepwise linear regression (SLR) techniques. The performance of the 34 indices was examined in order to evaluate their ability to estimate chlorophyll content and chlorophyll degradation in the leaves under different natural light conditions along 4 days of the canopy senescence period. Two of the new proposed RGB indices were found to improve the already good performance of the indices reported in the literature, particularly for leaves featuring low chlorophyll contents. The 4 best indices were finally tested in field conditions, using unmanned aerial vehicle (UAV)-taken photographs of a sugar beet plot, finding a reasonably good agreement with chlorophyll-meter data for all indices, in particular for I2 and (R−B)/(R+G+B). Consequently, the suggested RGB indices may hold promise for inexpensive chlorophyll estimation in sugar beet leaves during the harvest time, although a direct relationship with nitrogen status still needs to be validated

Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers

Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers