Nitrogen is the most significant nutrient element that affects crop growth, yield, and quality. The total nitrogen content in the crop is approximately 0.3% to 5.0% of dry weight. Nitrogen is involved in the composition of chlorophyll, which is not only the major component of protein, but also an important constituent of many enzymes in nucleic acids and plants. In addition, some vitamins, certain alkaloids, and some plant hormones such as auxins and cytokinins in plants contain nitrogen. In the production, when nitrogen deficiency occurs, the growth of shoots and roots of crops are significantly inhibited. The formation and development of reproductive organs is also limited. Plants mature in advance, seeds and fruits are small and unfilled, and the yield and quality of crops are significantly affected. On the contrary, increasing nitrogen fertilizer can increase crop yields and improve the quality of crop products, so the amount of nitrogen fertilizer input increases year by year. With the large increase in nitrogen fertilizer application, the efficiency of nitrogen fertilizer use is gradually reduced. On average, the utilization rate of several nitrogen fertilizers for wheat crops ranges from 27% to 34%, which is far lower than that of corn, cotton, and rice. Due to the unscientific application of nitrogen fertilizers, nitrogen is often lost through leaching, denitrification, denitrification, and nitrogen volatilization. The loss rate of wheat crops ranges from 14% to 55%, and the loss rate of autumn crops ranges from 18% to 53%. %. Most of the lost nitrogen enters groundwater and surface water, resulting in the continuous increase of nitrate nitrogen in groundwater and surface water, which leads to eutrophication of the water, resulting in a series of environmental problems such as the destruction of water resources and aquatic resources, and lower water use value. The cost of water treatment will even threaten human health. Therefore, understanding and real-time monitoring of crop nitrogen status and accordingly determining scientific fertility management measures are important for improving nitrogen use efficiency, rational use of resources, increasing crop yields, improving quality, and protecting the environment.
The research on the diagnosis of crop nitrogen nutrition began in the 19th century. By the 20th century, the diagnosis and chemical diagnosis of the disease had laid a certain foundation in theory and method. In the following 50 years, great progress has been made in the scope of application, object, content, and technology. The monitoring and diagnosis of crop nitrogen nutrition and growth dynamics is the core content of crop cultivation regulation and production management. It is the main basis for agricultural technical guidance departments and producers to make management decisions, and it also provides necessary basic information for the modernization of precision agriculture. Therefore, monitoring and diagnosis of crop growth based on nitrogen nutrition status has always been one of the core research contents in agronomy.
1 Traditional methods for the diagnosis of crop ammonia nutrition
1.1 Appearance Diagnosis of Crop Infertility
(1) Diagnosis of symptoms According to a specific symptom exhibited by the crop, it may be determined that it may lack an element. Symptom diagnosis has been widely used in the diagnosis of many nutrient elements. In the absence of nitrogen, the growth of shoots and roots of the crops was significantly inhibited, and the leaves were small and upright, with small angles to the stems, pale greenish when light, and pale yellow when severe. Plant stems are thin and long, with few tillers or branches, and the formation and development of reproductive organs are also limited. Flowers and fruits are sparse, and plants mature in advance. Small and unfilled seeds and fruits significantly affect crop yields and quality. Symptoms of excessive nitrogen are leggy, long internodes, multiple tillers, green leaves, and late-maturing lust. This method of nitrogen diagnostics is usually only effective if the plant is only deficient in one element. It is also caused by the lack of two or more nutrients or non-nutrient elements (pests, diseases, phytotoxicity, physiological diseases). The symptoms are easily confused and cause misdiagnosis. Furthermore, when a plant shows some symptoms, it indicates that the ammonia deficiency is already quite serious, and it is too late to take remedial measures. Therefore, there are obvious limitations in the practical application of symptom diagnosis, especially the accuracy and timeliness need to be improved.
(2) Growth diagnosis The Chinese people have long summarized many methods for diagnosing their growth and development based on crop morphological characteristics in long-term production practices. Narrow growth is the speed of crop growth, such as how soon and after the occurrence of tillering, the speed of the leaves, the length of the leaves and the size of the leaf area, the strength and the number of hair roots. After the 1960s, this method of diagnosis has received extensive attention in China and has been developed and enriched on the basis of summarizing the experiences of high-income farmers. The main indicators are the dynamics of tillers, growth and leaf area index. For example, Henan farmers concluded that the leaf appearance of wheat at the seedling stage is as follows: thin and thin seedlings are like horse ears, strong seedlings like licking ears, and Wang Miao like pig ears. Recently, Yang Bangjie has expanded the connotation of growth, including the traditional look at seedlings to diagnose all the indicators, and defined growth as the status and trend of crop growth. Crop growth can be described by individual and population characteristics. Individual characteristics of cereal crops can be described by characteristics of stem, leaf, root and ear, such as plant height, number of tillers, number, shape, color of leaves, and development of roots. Wait. Population characteristics can be described in terms of population density, leaf area index, layout, and dynamics. Only a reasonable group of individuals with robust development can grow well. According to the length of the plant growth and the length of the internodes between specific leaf positions, it is possible to diagnose the abundance of nitrogen in different growth stages. For example, when the rice is deficient in nitrogen, the angle between the leaf and the main stem is less than 450, the leaves are self-supporting, the plants are short, and the wheat is short. In the absence of nitrogen, the tillers occur slowly and are absent, with less secondary roots. This method can, to a certain extent, determine the nitrogen status of plants, but due to the frequent renewal of varieties, its appearance will change in appearance and appearance, and thus it will be limited in production and application.
(3) Leaf color diagnosis Chinese farmers have the traditional experience of looking at the topdressing of crops with leaf color. From the "Shen's Agricultural Book" about 300 years ago, the diagnosis of rice leaf color was applied to the present and the leaf color was used to diagnose nitrogen nutrition. The method has gradually matured. In the 1950s, the National Labor Model Chen Yongkang summed up the “three black and three yellow†changes in the leaf color of rice population to control the growth and development of late rice, and achieved the experience of high yield and stable yield. He proposed that “Fertilizer Yellow Peeling Plants and Thin Fields See Yellow Plantsâ€. The principle of rice topdressing is generally not applied to fields. People have done a lot of research and summary on the method of seeing seedling fertilization. However, this method lacks the objective criteria of quantitative leaf color depth and it is difficult to popularize and apply. Some researchers have used colorimetric cardiogram colorimetric determination of rice leaf color grade, made a series of studies on the physiological basis of yellow-black changes in single-season late rice, and put forward the corresponding indicators. In the 1970s and 1980s, Japanese agronomists and Chinese scholars successively developed leaf-colored tickets and leaf-colored cards, and established leaf-color level evaluation criteria. However, the measurement method still uses visual inspection, and the evaluation of subjective consciousness of leaf-color level is influenced by people’s subjective consciousness. Larger. The leaf color is the external manifestation of nitrogen nutrient in the plant. The leaf color grade determined by the leaf color card can be roughly used as an indicator of the level of nitrogen nutrition. They determine the standard leaf level range according to different plant types. When the field leaf color level exceeds the standard leaf color level, indicating excess nitrogen, measures should be taken to control; when the leaf color level is less than the standard leaf color level, it indicates that nitrogen is malnutrition. , should apply the right amount of nitrogen fertilizer. Leaf color diagnosis is a simple and easy method for nitrogen nutrient diagnosis. If the standard leaf color grade is determined properly, the diagnosis will achieve good results. Overall, the leaf color card method is simple, convenient, and semi-quantitative in nutrient diagnosis, but it cannot be distinguished whether the chlorosis of the crop is caused by nitrogen deficiency or other factors. The method is also affected by factors such as variety, vegetation density, and changes in soil nitrogen status and chlorophyll content. In addition, there are differences in the visual perception of color between different individuals, which restricts the application and accuracy of leaf color card method in the diagnosis of nitrogen nutrition in rice.
1.2 Chemical diagnosis of crops lacking fertilizer
(1) Diagnosis of total nitrogen in plants The earliest and most complete diagnosis of plant nitrogen in crop chemical diagnostic analysis work. The critical concentration of nitrogen in different growth stages and organs of most crops has been basically clear. Plant total nitrogen content can well reflect the status of crop nitrogen nutrition, and has a good correlation with crop yield, and the total nitrogen content is relatively stable, which is a good diagnostic indicator. The traditional method of total nitrogen nutrition diagnosis is mainly based on the laboratory chemical analysis of plant tissues. The main laboratory chemical analysis method is Duma's method. The main instrument of this method is an automatic nitrogen determination instrument. Duma's method is to fully burn the sample, and all forms of plant nitrogen are converted to nitrogen (N2). The total nitrogen of the sample is calculated by calculating the volume of nitrogen. The main drawback of this method is that the instrument is too expensive to be popularized. Another commonly used method is the Kjeldahl method, where concentrated sulfuric acid and a mixture of accelerators or oxidants are used to digest the plant sample and the organic nitrogen is converted to ammonium nitrogen and determined by distillation titration. Both the Dumman and Kjeldahl methods are laboratory chemical analysis methods that generally require the destruction of vegetation samples. It takes a great deal of time, manpower, and material resources to collect a large number of samples, dry them, weigh them, and polish them until they are tested using potentially harmful drugs. Due to the length of time spent, the timeliness of the results is not strong, and the laboratory chemical analysis requires experienced professional analysts and a large number of analytical reagents and equipment, so it is difficult to achieve rapid and scalable application in production.
(2) Nitrate is rapidly diagnosed as nitrate nitrogen is present in plants as a non-metabolized substance in a semi-reservoir state. When there is a slight nitrogen deficiency in the crop, the demand for nitrate nitrogen pools rapidly increases. At this time, the total nitrogen pool is increased. No significant changes have yet been made and the nitrate pools have changed significantly. If the nitrogen supply exceeds the crop demand, nitrate nitrogen also has a larger increase than total nitrogen. The relative change of nitrate nitrogen content in plant tissues is much larger than that of total nitrogen. It can sensitively reflect the crop's nitrogen requirement. Therefore, nitrate nitrogen can be used instead of total ammonia as a nitrogen nutrient diagnostic indicator to estimate the nitrogen status of plants. And top dressing recommendations. Many studies have shown that nitrate nitrogen is more reliable in predicting nitrogen deficiency in wheat, but the critical value varies greatly from site to site, affected by plant genotype, soil, etc., and changes rapidly with time, so there are certain limitations in practical applications. Sex. At present, the use of nitrate nitrogen (nitrate) as a diagnostic tool for nitrogen nutrient abundance is more common in dryland crops and vegetables. It is generally believed that wheat is a suitable diagnostic site for the stem base, which serves as a transport and storage organization. Most of the remaining nitrate nitrogen in the plant accumulates here, and its target changes are relatively small. Maize generally uses the veins of new mature leaves as a diagnostic site. Lu Shihua et al. confirmed through field experiments that the diphenylamine NO3-N method at the stem base of wheat at the jointing stage can quickly and accurately diagnose the status of nitrogen nutrition. It is feasible to promote the application of nitrogen in production. Cao Hongsheng et al. studied the rapid NND diagnosis of wheat by using the content of nitrate nitrogen in wheat leaf sheath fluid to guide the jointing fertilizer. Zhang Xuejun et al. also used the jointing period as the nitrogen nutrient diagnosis period, measured the nitrate content in the stem base by a reflectometer, and simultaneously diagnosed the plant nitrate and the soil surface Nmin (test of soil profile inorganic nitrogen) to establish a recommended fertilization model. The above two methods for measuring nitrate nitrogen have the advantages of rapidness, accuracy, and convenience. They are suitable for diagnosis of nitrogen under field conditions. However, the diphenylamine method is suitable for plants with low ammonia levels. The emission instrument method has a high cost and its application is subject to certain limit.
(3) Nitrate reductase As nitrate reductase (NRA) represents the level of nitrogen assimilation, and NO3-represents only nitrogen accumulation in the body, this NRA is better than NO3-concentration as a nutrient diagnostic indicator. Research by Hong Jianming et al. found that within a certain range of fertilization levels, NRA levels in the main functional leaves of wheat increased with increasing NO3- concentration in the soil. NRA is a sensitive adaptation enzyme, which can well reflect the nitrogen in the soil. Condition. Ma Fengming et al found that beet was used as a raw material. During the early growth stage, NRA increased with the increase of nitrogen application rate and accelerated NO3-reduction. However, the sugar metabolism was the main factor in the later period, and the correlation between NO3- and NRA was not significant. The response to nitrogen was selected. The critical period is used as a suitable period for diagnosing the abundance of nitrogen, and then the theoretical NRA values ​​for each period are given. However, because NRA is sensitive to changes in internal and external conditions, it must be performed under strict control conditions during specific operations. Note the effects of NO3-, concentration, light, persistent high temperature, and water stress on NRA levels.
(4) Amino nitrogen diagnosis studies have shown that in a certain range of nitrogen application, the nitrogen application rate is significantly related to the total nitrogen content of functional leaves of cotton buds and the nitrate nitrogen content of the petiole base, when the leaf's total nitrogen and petioles When the nitrate nitrogen content in the base was high, the amino nitrogen was also high, and vice versa, and the amino nitrogen was significantly positively correlated with the total nitrogen in the leaves. According to this, amino nitrogen can be used as a diagnostic indicator of nitrogen nutrition in cotton and preliminary diagnostic values ​​are given. Yuan Ling and others believe that the peak period of free amino acid content in functional leaves of rice occurs in the tillering stage, decreases to 1/2 in the jointing stage, and decreases to 1/3 in the heading stage. The rules are similar to the changes of ammonium nitrogen in the soil and also to the plant nitrogen content. The changes are similar, so it is recommended to use free amino nitrogen (FA-N) as a nitrogen nutrient index. Zhang Weijian et al. found that the free amino nitrogen of the inverted 3-leaf sheath of rice increased with the increase of nitrogen application amount, and the differences among the organs were the most obvious in the inverted 3-leaf sheath. The change of the content of FA-N in the inverted 3-leaf sheath could rapidly diagnose nitrogen nutrition. , to guide the quantitative fertilizer in the middle and late period of rice.
2 Modern Techniques for Diagnosis of Crop Nitrogen Nutrition
2.1 Chlorophyll Analysis Technology
When the crop is deficient in nitrogen, it will generally show some obvious symptoms of nutrient deficiency, such as the leaf color resulting from the decrease of leaf chlorophyll content becomes lighter, and the excessive nitrogen, the darker color of the leaf color. The leaf chlorophyll content of the plant is closely related to the leaf nitrogen content, and the leaf nitrogen content and the chlorophyll change trend are similar, so the nitrogen nutrition status of the crop can be understood through the change of leaf color. The study found that there is a quantitative relationship between the light reflectance characteristics of crop leaves and the depth of leaf color. The reflectance of plants in the visible light range is mainly affected by chlorophyll, and the reflectance near 550 nm and 675 nm is sensitive to the chlorophyll content, but the reflectivity in a single wave band is easy. Due to the influence of biomass, background, etc., the ratio of the two bands can improve the accuracy of chlorophyll spectrum diagnosis. According to this principle, Japan's MINOLTA Corporation introduced SPAD-501 and SPAD-502 chlorophyll meters in the 1980s to conduct field crop nitrogen diagnosis and guide fertilization, and achieved good results in the diagnosis of certain crops. At present, in the application research of chlorophyll meter, the measurement site used is generally the same, that is, the first leaf newly taken as the measurement site in the early stage of crop growth, and the functional leaf as the measurement site in the middle and later growth phase. Chlorophyll meter differs in the measured values ​​between individuals, and the measured values ​​in different parts of the same leaf are also not the same. It is generally considered that the SPAD value at a distance of 55% from the base of the leaf is large and the deviation is small, which is a suitable test site. point. Therefore, the chlorophyll meter can characterize the nitrogen nutrition status of crops to a certain extent, but its practical application is often affected by the crop species, growth period, and growth environment, and it is necessary to accurately estimate the level of nitrogen nutrition. It is also necessary to establish a calibration curve or improve the calculation method. Wang Shaohua et al. used the principle of relative leaf color difference (RSPAD) based on the principle that the top 4 leaves respond to nitrogen sensitivity and top 3 leaves insensitivity, and initially established the RSPAD diagnostic model for the nitrogen content of the leaves (plants). The type of rice subspecies may not be affected by the specific varieties and fertility processes, and has a good universality, but it needs to be verified by more ecological points, more varieties, and larger areas. The three-leaf SPAD value of top three leaves based on wheat in Changxian et al. varies greatly among different nitrogen treatments, and is used as an indicator leaf for nitrogen nutrient diagnosis. Based on the theory of crop yield and nutrient concentration, the jointing and booting stages were specifically proposed. Appropriate and critical SPAD value, this method is simple, time-sensitive, still need more years and more varieties of tests to further improve the card, in order to establish a universal model for the guidance of production. In addition, the correlation between chlorophyll meter value and nitrogen nutrition can be adjusted through laboratory analysis using some calibration parameters (such as leaf dry weight, leaf area, etc.), but this will weaken the chlorophyll meter quickly, easily and without damage. The characteristics of vegetation growth and other characteristics have lost its original meaning, and all the models obtained by correlation analysis or regression analysis are subject to species, growth period and environmental conditions. Therefore, this method is not a fast and reliable non-destructive method.
2.2 Chlorophyll Fluorescence Analysis Technology
Chlorophyll fluorescence technology has a unique role in determining the light energy absorption, transmission, dissipation, and distribution during the photosynthesis process of the leaf. Compared with the “apparent†gas exchange index, the chlorophyll fluorescence parameter is more With the characteristic of reflecting "innerness", the chlorophyll fluorescence dynamics technology is called the fast and non-invasive probe for determining the photosynthetic function of leaves. In recent years, a series of studies on chlorophyll fluorescence kinetics technology have further demonstrated that it is feasible to use the in vivo chlorophyll fluorescence as a natural probe to study and detect the photosynthetic physiological status of plants and the influence of various external factors on it, making it a variety of plants Resistance physiology, high yield theory, crop breeding and cultivation, plant ecology, and even plant remote sensing telemetry and other botanical branches and agronomy have been applied to varying degrees. With the continuous deepening of research, more and more work has shown that the chlorophyll fluorescence signal emitted from plants contains abundant biological information and can easily change with external environmental conditions. It can be used as a rapid, sensitive and non-invasive research and The ideal method for detecting photosynthesis of plants with multiple stress factors. Due to its advantages of fast, sensitive and non-destructive measurement, it is superior and more accurate than other current detection methods.
Studies have shown that photochemical efficiencies Fv/Fm and φ~PSII of wheat seedlings exposed to nitrogen stress are significantly lower than normal ammonia supply. The photosynthetic efficiency of chlorophyll fluorescence parameters and the leaf nitrogen content of Shatian pomelo were significantly positively correlated. Fan Yanping et al. studied the photosynthetic characteristics of R. splendens by hydroponic method and found that the leaf chlorophyll fluorescence induction kinetic parameters can be used as a suitable environment for nitrogen nutrition of plants. Degree of sensitivity indicator. Guo Yanping measured the maximum fluorescence Fm, PSII photochemical efficiency (Fv/Fm) and apparent electron transport rate (ETR) of the leaves of Satsuma mandarin leaves significantly lower than that of the phosphorus-supplying plants. Li Shaochang et al. showed that low phosphorus treatment increased the degree of PSH closure in corn leaves, decreased light energy conversion and electron transfer efficiency, and increased excess excitation energy. Analysis shows that the relationship between leaf phosphorus content and Fv/Fm is highly consistent with the exponential function relationship. The specific performance is that when the content of phosphorus in leaves is lower than 0.1mg/gFW, Fv/Fm will decrease rapidly with the decrease of phosphorus content, and when the content of phosphorus in leaves is higher than 0.1mg/gFW, Fv/Fm will gradually stabilize and become too high. Phosphorus content in the leaves does not increase the photochemical efficiency of the leaves, so the Fv/Fm enthalpy value is 0.1 mg/g FW. Ma Jifeng and other studies showed that the leaf nitrogen content of wheat leaf and the fluorescence parameters Fv/Fm and Fv/F0 are extremely significant positive correlation, and the head of the two leaves the best correlation. For wheat varieties with low, medium, and high egg content, a uniform power function regression equation can be used to describe the variation of nitrogen content with Fv/F0 for the top two leaves. These preliminary findings indicate that chlorophyll fluorescence technology can be used for plant nutrient diagnosis, but work in this area is still reported less frequently. In addition, for the determination of chlorophyll fluorescence, there is currently a lack of a simple, fast, inexpensive new method.
2.3 Image and Machine Vision Technology
Machine vision uses an image sensor instead of the human eye to acquire an image of an object, converts the image into a digital image, and uses a computer to simulate human judgment criteria to understand and recognize the image so as to analyze the image and make conclusions. purpose. Crops show different stem and leaf colors and morphologies under different nutrient conditions, which are important information to characterize crop growth. Image processing techniques are used to diagnose crop nutrition status mainly through field acquisition of digital images, and use of digital image processing and models. Techniques such as identification, scene analysis, and image comprehension analyze and process images, obtain external information of crops from images, and serve as image feature vectors for computer processing, classification, identification, and decision diagnosis to reflect crop growth and Nitrogen status. In crop nutrition diagnosis, the image recording is formed by sensing the light reflection and light absorption properties of the crop canopy and the leaves of the crop by an electron scavenger CCD in an image sensor, and then the spectral differences under different nutritional conditions are obtained through image processing and analysis techniques. To achieve its entire diagnostic process. In recent years, the visible light color analysis technology has become a new research hotspot and is applied in the application of Shiyue mouth. If Btackmer and others analyzed the relative brightness of the canopy on color photos to predict corn yield, there was a very significant correlation between the red, green and blue light and corn yield. Dymond and Trotter used CCD digital cameras to obtain color images of forests and pastures through aerial photography, effectively evaluating the dual-wavelength reflection characteristics of plant canopy in forests and pastures. Adamsen et al. used a digital camera to obtain the canopy image of winter wheat. It is believed that there is a significant correlation between the ratio of green (G) to red (R) G/R and canopy greenness. Lukina et al. used a digital camera to estimate winter wheat canopy biomass. Scharf et al. applied normal visible light images to study the optimum nitrogen application rate of maize. Jia and Cheng applied a digital camera to establish the relationship between the green depth of the canopy at the jointing stage and the booting stage and the total nitrogen of the plant above the shoot. These researches lay the foundation for the application of digital camera-based color image processing technology and machine vision color recognition technology in crop growth monitoring. This shows that machine vision has been able to better simulate the visual characteristics of the human eye. It has far exceeded the capabilities of the human eye in distinguishing and sensing colors, and it is combined with a computer image processing system based on color characteristics. The obvious advantage in the diagnosis of crop nitrogen is bound to have broad application prospects in the detection and diagnosis of crop nitrogen.
3 Spectral remote sensing analysis technology
The spectral characteristics of crops are due to the physiological characteristics of crops that determine their absorption, transmission and reflection of light, and the physiological characteristics of crops reflect its growth situation, so it can be monitored according to the spectral differences under different cultivation conditions. The growth of the crop. The traditional remote sensing spectrum has few bands and low resolution. With the development of remote sensing technology, especially the emergence and rise of hyperspectral remote sensing technology, the spectral bands can be subdivided in a specific spectral region to diagnose the nitrogen nutrition of crops. Infused new vitality. High-resolution ground-based spectrometers may be used for simple, rapid and non-destructive estimation of plant canopy biochemical composition. Its good prospects are attracting more and more attention of agronomists, and have monitored plant nutrition in large areas. The status of research has made significant progress. Using hyperspectral remote sensing technology, we can quickly and accurately obtain information on crop growth status and environmental stress, and adjust the input amount of input materials accordingly to reduce waste, increase production, protect agricultural resources and environmental quality. It is the future An important means for the sustainable development of agriculture and agriculture. In order to explore the possibility of remote sensing diagnosis of nitrogen in plants, since the 1970s scientists have conducted a lot of basic research to find the sensitive bands of nitrogen and its reflectance performance under different nitrogen levels. The study found that many plants in the absence of nitrogen, whether in the visible light band reflectance of the leaf or plant canopy level have increased, the most sensitive to the nitrogen content of the band in the 530-560mm area by spectrometry and its variable operations such as The ratio of infrared to infrared (NIR/Red) can distinguish different levels of nitrogen nutrition. After clarifying the nitrogen-sensitivity bands of plants, many scholars have used various statistical methods to seek the relationship between nitrogen content and spectral reflectance or its quantification, and established a model to estimate the nitrogen content of crops. Shibayma et al.'s study on rice found that the linear combination of leaf nitrogen content per unit area and R620 and R760 and the linear combination of R620 and R880 all have a good regression relationship. The predicted value and the measured value are linearly related and are not affected by The type of influence. Thomas et al. used the two bands of 550 nm and 670 nm to quantitatively estimate the nitrogen content of sweet pepper, with a precision of 90%. Fernandez et al. found that the linear combination of red (660nm) and green (545nm) bands can predict the nitrogen content of wheat and is not affected by nitrogen fertilizer treatment. Johnson used a stepwise multivariate regression method to find that the correlation between the first derivative of leaf reflectance and total leaf nitrogen at the 2160 nm band was best across the entire visible to infrared range. Spectral analysis was used to estimate the nitrogen content of fresh leaves. Accuracy is greater than 85%. Tarpley et al. analyzed the relationship between cotton leaf nitrogen concentration and 190 spectral ratio indexes, and clustered the analysis based on the accuracy and accuracy of the predictions, indicating that the ratio of red edge to short wave near infrared band was used to predict nitrogen content higher. Accuracy and accuracy. Pu Ruiliang He Gongpeng used multivariate statistics and spectral derivative techniques to evaluate small airborne imaging spectrometer (cASI) data for estimating the potential and efficiency of canopy biochemical concentrations (total chlorophyll, total nitrogen, and total phosphorus). Niu et al. believe that the linear regression equation of the first derivative of reflectance at 2120 nm and 1120 nm can predict the nitrogen content of fresh leaves, and the correlation between the measured value and the predicted value is more than 80%. Stone et al. used canopy reflectance spectroscopy to guide variable-rate fertilization in wheat and significantly improved total nitrogen use efficiency. Because the spectral reflectance characteristics of plant canopy are influenced by factors such as plant leaf water content, canopy geometry, soil coverage, atmospheric absorption of spectrum, etc., and the influence factors vary under different temporal and spatial conditions, the conditions established under specific conditions Plant nitrogen spectral diagnostic models are difficult to use in space-time conditions other than modeling, which makes the use of remote sensing for the reliability and popularity of crop nitrogen diagnosis is limited.
4 Conclusion
It can be seen from the above that, when assessing the status of crop nitrogen nutrition, the traditional testing methods take destructive sampling, and it takes a lot of manpower and material resources in sampling, measurement, data analysis, etc., and the timeliness is poor, which is not conducive to popularization and application. Therefore, in this context, non-destructive testing technology has received extensive attention in the diagnosis of crop nitrogen nutrition and recommended fertilization, and it is considered to be a highly promising crop nutrient monitoring and diagnostic technology, which is characterized by not destroying the plant tissue structure. Based on this, we try to use various means to monitor the growth and nutritional status of crops. This method can quickly and accurately monitor the nitrogen status of crops in the field and provide timely information needed for topdressing. The traditional NDT diagnostic non-destructive testing methods mainly include fertilizer window method and leaf color card method, but these methods are qualitative or semi-quantitative methods. In recent years, as the level of science and technology in related fields continues to increase, the non-destructive testing technology for nitrogen nutrient diagnosis is progressing from qualitative or semi-quantitative to precise quantitative direction, from manual testing to intelligent testing, and from individual operating units to grouped surfaces. Source detection. For example, through SPAD, machine vision and spectral analysis techniques, the nitrogen status of plants can be well detected non-destructively, so as to guide the rational application of nitrogen and nitrogen regulation and avoid blind fertilization, so as to achieve the goal of increasing nitrogen use efficiency. Because of the high accuracy and ease of operation of surface spectral monitoring models, many researchers have developed a variety of crop nitrogen monitoring instruments. For example, the Greenseeker spectrometer is the most advanced one developed internationally by Oklahoma State University in the mid- to late 1990s. The ground active remote sensing hyperspectral instrument realizes stable measurement under almost all light conditions from daytime to nighttime. It is a good field-scale recommended fertilization tool. It can analyze crop growth by observing NDVI data and perform real-time nitrogen. Diagnose and provide the best fertilization plan. At present, based on high-altitude remote sensing monitoring models, the prediction accuracy is low. Therefore, it is necessary to combine ground-based spectral monitoring models with relatively high accuracy with spatial remote sensing information to establish crop growth and nitrogen nutrition remote-sensing monitoring and forecasting models, and to realize geographic information systems at the same time. (GIS) and Global Positioning System (GPS) are organically combined for large-scale crop growth monitoring and management regulation. Therefore, the organic fusion of ground-based remote sensing information and spatial multi-source remote sensing data will help establish a multi-information-based crop nitrogen nutrition monitoring platform to guide high-efficiency crop nitrogen management and precise fertilization.