Method, device and equipment for predicting occurrence degree of rice leaf roller larvae

Abstract

The present disclosure provides a kind of Cnaphalocrocis medinalis larva occurrence degree prediction method, device and equipment, the method comprises: identifying the current generation adult of Cnaphalocrocis medinalis in rice cluster migrates into peak day, and obtains the base number of peak day migration;Based on the pre-set migration adult female-male ratio factor, migration population egg-laying potential matrix and the base number of hundred clusters of insects, the basic population contribution value of next generation larva is calculated;Based on the basic population contribution value of next generation larva, the occurrence degree of Cnaphalocrocis medinalis larva is determined.The accuracy of Cnaphalocrocis medinalis larva occurrence degree prediction can be improved by the method of the present disclosure.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus and equipment for predicting the occurrence of rice leaf roller larvae. Background Technology

[0002] The rice leaf roller is a significant migratory pest of rice, belonging to the order Lepidoptera and family Crambidae, and is widely distributed in tropical and subtropical rice-growing regions of Asia. Its larvae roll up leaves and feed on the leaf tissue, leading to decreased photosynthesis in rice and, in severe cases, causing large-scale yield reductions. Therefore, accurately predicting the future extent of rice leaf roller infestations and issuing corresponding early warnings has become a crucial aspect of scientific pest control to effectively reduce the impact of rice leaf roller larvae on rice yield.

[0003] In traditional forecasting work, the total number of moths measured using the "field moth-driving method" is typically used to estimate the severity of the next generation of larvae. However, practical experience shows that this traditional method, which directly relies on the total number of moths measured using the field moth-driving method to estimate the severity of the next generation of larvae, results in a significant discrepancy between the estimated severity and the actual severity of the next generation of larvae in the field. Therefore, improving the accuracy of predicting the severity of rice leaf roller larvae has become a pressing technical challenge for those skilled in the art. Summary of the Invention

[0004] In view of this, this disclosure proposes a method, apparatus, equipment and storage medium for predicting the occurrence of rice leaf roller larvae, which can improve the accuracy of predicting the occurrence of rice leaf roller larvae.

[0005] According to a first aspect of this disclosure, a method for predicting the occurrence of rice leaf roller larvae is provided, comprising: Identify the peak day of the migration of contemporary adult rice leaf rollers into rice bushes, and obtain the base number of adults per 100 bushes on the peak day of the migration. Based on the pre-set female-to-male ratio factor of the migrating adults, the oviposition potential matrix of the migrating population, and the base number of adults per hundred clusters, the basic population contribution value of the next generation of larvae is calculated. The extent of rice leaf roller larvae occurrence was determined based on the baseline population contribution value of the next generation of larvae.

[0006] In one possible implementation, the formula for calculating the basic population contribution value of the next generation of larvae is as follows: In the formula, Contribution value to the basic population of the next generation of larvae The number of adult insects in the aforementioned 100 clusters is the baseline. The pre-set adult male-to-female ratio factor, The pre-defined matrix of the spawning potential of the migrating population.

[0007] In one possible implementation, the oviposition potential matrix of the migrating population is constructed based on a preset ratio of mated migrating individuals, the oviposition of adults that have mated before migration, the ratio of mated individuals after migration, and the oviposition of adults that have mated after migration.

[0008] In one possible implementation, determining the extent of rice leaf roller larvae occurrence based on the baseline population contribution value of the next generation of larvae includes: Data on key meteorological factors within a set time period after the peak migration day are obtained, and an environmental survival correction matrix is ​​calculated based on the data on each key meteorological factor. Data on key biological factors are obtained within a set time period after the peak migration day, and biological environmental load factors are calculated based on the data of each key biological factor. The population size of the next generation of larvae is obtained by correcting the basic population contribution value based on the environmental survival correction matrix and the biological environmental load factor. The extent of rice leaf roller larvae occurrence was determined based on the population size of the next generation of larvae.

[0009] In one possible implementation, the key meteorological factor data includes at least one of temperature data, relative humidity data, rainfall data, and lunar phase data.

[0010] In one possible implementation, the key biological factor data include at least one of the following: the developmental stage of rice, rice variety, rice planting density, and the number of natural enemies in the field.

[0011] In one possible implementation, after determining the extent of rice leaf roller larvae infestation, the method further includes issuing an alert based on the extent of infestation.

[0012] According to a second aspect of this disclosure, a device for predicting the occurrence of rice leaf roller larvae is provided, comprising: The first calculation module is used to identify the peak day of the migration of contemporary adult rice leaf rollers into rice bushes and to obtain the base number of adult insects per hundred bushes on the peak day of the migration. The second calculation module is used to calculate the basic population contribution value of the next generation of larvae based on the pre-set female-to-male ratio factor of the migrating adult, the oviposition potential matrix of the migrating population, and the base number of adults in the hundred clusters. The occurrence severity prediction module is used to determine the occurrence severity of rice leaf roller larvae based on the basic population contribution value of the next generation of larvae.

[0013] According to a third aspect of this disclosure, a device for predicting the occurrence of rice leaf roller larvae is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method described in the first aspect of this disclosure.

[0014] According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the method described in the first aspect of this disclosure.

[0015] This disclosure provides a method, apparatus, device, and storage medium for predicting the occurrence of rice leaf folder larvae. The method includes: identifying the peak day of adult migration of rice leaf folder into rice plants and obtaining the base number of adults per 100 plants on the peak day; calculating the basic population contribution value of the next generation of larvae based on a pre-set female-to-male ratio factor of the migrating adults, an oviposition potential matrix of the migrating population, and the base number of adults per 100 plants; and determining the occurrence degree of rice leaf folder larvae based on the basic population contribution value of the next generation of larvae. In this disclosure, by introducing the female-to-male ratio factor of the adults, the total number of migrating moths can be accurately converted into the number of female moths capable of reproduction. At the same time, by introducing the oviposition potential matrix of the migrating population, the number of migrating female moths can be further accurately converted into the basic population contribution value of the next generation of larvae, thereby achieving a precise conversion from "effective number of female moths" to "expected number of larvae". Therefore, this system solves the prediction bias problem caused by ignoring the differences in sex structure and egg production in traditional methods, and significantly improves the accuracy and scientific validity of early inference of the occurrence of rice leaf roller larvae.

[0016] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0017] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0018] Figure 1 A flowchart illustrating a method for predicting the occurrence of rice leaf roller larvae according to an embodiment of the present disclosure is shown. Figure 2 A schematic block diagram of a rice leaf roller larvae occurrence prediction device according to an embodiment of the present disclosure is shown. Figure 3 A schematic block diagram of a device for predicting the occurrence of rice leaf roller larvae according to an embodiment of the present disclosure is shown. Detailed Implementation

[0019] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0020] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0021] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0022] <Method Implementation> Figure 1 A flowchart illustrating a method for predicting the occurrence of rice leaf roller larvae according to an embodiment of this disclosure is shown. Figure 1 As shown, the method includes steps S1100-S1300.

[0023] S1100 identifies the peak day of adult migration of rice leaf rollers in rice bushes and obtains the baseline number of adults per 100 bushes on the peak day of migration.

[0024] In one possible implementation, identifying the peak day of adult rice leaf roller migration into rice paddies can include the following steps: Installing JDCB-type intelligent insect monitoring lamps within the rice leaf roller monitoring area, automatically uploading the daily trapping count of adult rice leaf rollers to the system executing this method at a specified time (e.g., 0:00). The system statistically analyzes the daily trapping count. When the system analyzes that the daily trapping count has increased for a consecutive set number of days (e.g., 3 consecutive days) and the peak daily trapping count is ≥ a preset number (e.g., 5), it prompts for field moth removal verification. After receiving the prompt, monitoring personnel select multiple (e.g., 5) sampling points within the rice leaf roller monitoring area, survey a preset area (e.g., 4㎡) at each sampling point, and use a 2m bamboo pole to move the upper part of the rice paddies against the wind along the field ridge, counting the number of moths that fly up, and calculating the moth count per 100 paddies (moths / 100 paddies). These moths are identified as rice leaf rollers. After obtaining the number of moths per 100 clusters at each sampling point, the average number of moths per 100 clusters is calculated. It is then determined whether this average is greater than a preset outbreak level. If the average is greater than the preset outbreak level, the date on which the peak daily trapping count is greater than or equal to a preset number of moths is designated as the peak day for the current generation of adults to migrate in. Simultaneously, the calculated average number of moths per 100 clusters is input into the system as the base number A of adults per 100 clusters on the peak day for the current generation of adults to migrate in. In this way, the system can obtain the base number A of adults per 100 clusters on the peak day for the current generation of adults to migrate in.

[0025] S1200 calculates the basic population contribution value of the next generation of larvae based on pre-set factors such as the female-to-male ratio of migrating adults, the oviposition potential matrix of the migrating population, and the base number of adults per 100 plant clusters. The basic population contribution value of the next generation of larvae refers to the theoretical initial total number of larvae (unit: larvae / 100 plant clusters) predicted within a specific monitoring area, based on the base number of adults per 100 plant clusters monitored in real time in the field, combined with the biological reproductive characteristics of the rice leaf roller. This indicator, as a core input, solves the problem of quantitative conversion from "adult migration volume" to "next generation larval size".

[0026] In one possible implementation, the following formula is used to calculate the basic population contribution value of the next generation of larvae based on a pre-set female-to-male ratio factor of the migrating adults, the oviposition potential matrix of the migrating population, and the base number of adults per hundred clusters: In the formula, Contribution value to the basic population of the next generation of larvae This represents the base number of adult insects per 100 clumps monitored in real time in the field. For the pre-set female-to-male ratio factor of the migrating adult insects, A pre-defined matrix of the spawning potential of the migrating population.

[0027] In one possible implementation, the sex ratio factor for migrating adults is set based on the ratio of female to male moths observed in field sampling of the migrating generation of rice leaf folder. Specifically, field sampling observations revealed that the proportion of female moths in the migrating generation of rice leaf folder was significantly higher than that of male moths, with a female-to-male ratio approaching 1.5:1. Therefore, the sex ratio factor for migrating adults can be set accordingly. The value was set to 1.5. This was achieved by introducing a female-to-male ratio factor for the migrating adults. The total number of moths migrating in (i.e., the base number of adults per 100 clusters, A) can be precisely converted into the population of female moths capable of reproduction, thereby increasing the basic population contribution value of the next generation of larvae. The accuracy of the calculation.

[0028] In one possible implementation, the oviposition potential matrix of the migrating population is constructed based on preset parameters: the proportion of mated migrating individuals, the oviposition rate of adults that mated before migration, the proportion of mated individuals after migration, and the oviposition rate of adults that mated after migration. The formula for constructing the oviposition potential matrix of the migrating population is shown below: = In the formula, For a pre-defined matrix of the spawning potential of the migrating population, The percentage of those who have already mated and moved in. This refers to the number of eggs laid by adults that mated before migration. This represents the mating rate after relocation. This represents the number of eggs laid by adult insects after mating upon arrival.

[0029] It should be noted that, according to field sampling observations, the percentage of adult rice leaf rollers that had already mated before migration was extremely low, only 5%–20% in most years, with an average of 10%. Therefore, the percentage of mated females migrating into the area can be considered a reliable indicator. The percentage is set at 5% to 20%, with the preferred percentage being the proportion of those that have already mated and migrated. The average value is set at 10%. Female moths that have mated before migration are mature and typically lay 80-120 eggs per moth, averaging 100. Therefore, the egg-laying capacity of adults that mated before migration can be used as a reference. The optimal number of eggs laid is 80-120, with the most preferred being the number of eggs laid by adults mated before migration. The average number of grains was set at 100. Since the proportion of adult rice leaf rollers mating after migrating to the area is extremely high, the proportion of mating after migration can be used as a criterion. The percentage of matings after relocation will be set at 80%–95%, with priority given to those that relocate there. The average value was set at 87.5%. Female moths that mate and lay eggs after migrating typically lay 40-70 eggs, with an average of 50. Therefore, the egg-laying rate of adults that mate after migrating can be used as a reference. The optimal number of eggs is set at 40-70, with the most preferred being the number of eggs laid by the adults after mating upon migration. The average number of eggs was set at 50. This was achieved by introducing the aforementioned matrix of the spawning potential of the migrating population. By weighted calculations based on the mating physiological states of adults before and after migration, the accuracy of oviposition prediction for the migrating generation can be improved, thereby increasing the basic population contribution value of the next generation of larvae. The accuracy of the calculation.

[0030] In this embodiment, the basic population contribution value of the next generation of larvae The calculation formula is as follows: In assessing the baseline population contribution of the next generation of larvae At that time, by introducing the female-to-male ratio factor of the migrating adults. and the spawning potential matrix of the migrating population The number of adult insects per 100 clumps obtained from field sampling observations The correction can avoid the estimation error caused by traditional forecasting methods that rely solely on the total number of moths observed in field sampling to estimate the contribution value of the next generation of larvae's basic population, thereby improving the accuracy of the calculation of the contribution value of the next generation of larvae's basic population and thus improving the accuracy of the prediction of the occurrence of rice leaf roller larvae.

[0031] S1300, based on the baseline population contribution value of the next generation of larvae, determines the occurrence degree of rice leaf roller larvae.

[0032] In one possible implementation, determining the extent of rice leaf roller larvae occurrence based on the baseline population contribution value of the next generation of larvae may include the following steps: First, acquire data on key meteorological factors within a set time period after the peak migration day, and calculate the environmental survival correction matrix based on the data on key meteorological factors.

[0033] In one possible implementation, the set time period can be determined based on the average duration of the rice leaf roller's migration from adult to egg hatching. Preferably, the set time period can be 15 days after the peak migration day, which covers the pre-oviposition and egg stages, ensuring the capture of key meteorological factors affecting the hatching rate.

[0034] Once the peak date of adult insect migration is determined, the China Meteorological Administration's CMA-GEPS global ensemble forecast system or ECMWF numerical weather prediction product can be immediately accessed to obtain key meteorological factor data for a specified time period following the predicted peak migration date. If the forecast lead time is less than 15 days, missing dates are filled with historical climate averages for the same period (1980–2022). The key meteorological factor data includes at least one of the following: temperature data, relative humidity data, precipitation data, and lunar phase data. The temperature data is the daily average temperature collected during the specified time period; the relative humidity data is the daily average relative humidity collected during the specified time period; the precipitation data can be the daily average cumulative precipitation collected during the specified time period; and the lunar phase data is the corresponding lunar phase during the specified time period.

[0035] After obtaining the key meteorological factor data for a set time period following the peak migration day, the environmental survival correction matrix can be calculated based on this data. This environmental survival correction matrix... This refers to a set of algorithms that collect data on key meteorological factors within a set time period after the peak migration date, and then apply nonlinear weighted corrections to the survival rate, hatching rate, and developmental progress of the next generation of larvae in the basic population. This environmental survival correction matrix... The aim is to reduce the "theoretical egg production" to the "actual number of surviving larvae".

[0036] In an embodiment where key meteorological factor data includes temperature data, relative humidity data, rainfall data, and lunar phase data, the calculation of the environmental survival correction matrix based on each key meteorological factor data will include the following steps: First, determine the temperature correction factor based on the temperature data. Specifically, as a poikilothermic organism, the rice leaf folder's egg hatching rate follows a typical normal distribution according to a temperature-based developmental simulation model. This normal distribution indicates that the optimal temperature range for rice leaf folder egg hatching is 26 ≤ temperature ≤ 28℃, with the highest hatching rate reaching 90%–98%, averaging 95%. Therefore, when the obtained temperature data falls within this optimal temperature range, a temperature correction factor can be applied. The average hatching rate was set at 0.95 for the optimal temperature zone. Temperatures ≥35℃ represent an extreme high-temperature zone for rice leaf roller egg formation. Under these conditions, egg masses lose water and become inactive, causing the hatching rate to plummet to below 10%, averaging 5%. Therefore, when the acquired temperature data falls within this extreme high-temperature zone, a temperature correction factor can be applied. The average hatching rate in the extreme high-temperature zone was set to 0.05. Research shows that temperatures ≤22℃ constitute the low-temperature zone for rice leaf folder hatching. At this low temperature, the hatching rate decreases and the development period is prolonged. Therefore, when the acquired temperature data falls within this low-temperature zone, a temperature correction factor can be applied. Set to 0.6. When the temperature is between 28°C and 35°C, the hatching rate of the rice leaf roller gradually decreases but remains at a moderate level (approximately 70%–80%), averaging 75%. Therefore, the temperature correction factor can be adjusted. Set to 0.75. When the temperature is between 22 and 26°C, the average hatching rate of the rice leaf roller is approximately 80%, therefore the temperature correction factor can be set to 0.75. Set it to 0.8.

[0037] Secondly, based on relative humidity data and the "Analysis of Meteorological Conditions for the Occurrence of Rice Leaf Roller", a humidity correction factor was determined. Specifically, the rice leaf roller prefers moist conditions; a high humidity environment (RH ≥ 80%) is conducive to adult oviposition and newly hatched larvae rolling leaves for concealment. Therefore, when the obtained relative humidity data is RH ≥ 80%, it is conducive to population outbreak, hence the humidity correction factor is applied. Set to 1.1. When the acquired relative humidity data (RH≤60%), the egg masses of the rice leaf roller easily dry out, and newly hatched larvae are prone to death due to dehydration before the leaves roll. Therefore, the humidity correction factor is set to 1.1. The humidity correction factor was set to 0.6. When the relative humidity (RH) data obtained was greater than 60% and less than 80%, the development of rice leaf roller eggs and the survival of newly hatched larvae were at normal levels, with neither a significant high humidity promoting effect nor a significant drought stress effect. Set to 1.0.

[0038] Next, based on the rainfall data, the rainfall erosion coefficient is determined. Specifically, rainfall has a physical scouring effect on egg masses and young larvae attached to the surface of rice leaves. However, in the absence of rain or with light rain (<10 mm), there is no physical scouring effect; therefore, the rainfall scouring coefficient is [not specified]. Take 1.0; during moderate rain (10–25 mm), a slight scouring effect exists, therefore the rainfall scouring coefficient is... The coefficient is taken as 0.9; during heavy rain (25–50 mm), the scouring effect is significant, therefore the rainfall scouring coefficient is... Take 0.8; during heavy rain or above (>50mm), the scouring effect is strong, therefore the rainfall scouring coefficient is... Take 0.7. After determining the average daily cumulative rainfall for each day within the set time period, the rainfall erosion coefficient can be determined based on the maximum average daily cumulative rainfall. Alternatively, it can count all rainfall dates and determine the rainfall erosion coefficient for each rainfall date based on the average daily cumulative rainfall. Then, the rainfall erosion coefficient corresponding to each date was calculated. Perform cumulative multiplication, and use the result as the final rainfall erosion coefficient. .

[0039] Finally, based on the temperature correction factor Humidity correction factor Rainfall erosion coefficient Calculate the environmental survival correction matrix Among them, the environmental survival correction matrix The calculation formula is as follows: In this embodiment, the environment survival correction matrix The impact of dynamic meteorological factors such as temperature, relative humidity, and rainfall on the survival rate, hatching rate, and developmental progress of the next generation of larvae was considered. Therefore, an environmental survival correction matrix was introduced. Basic population contribution value to the next generation of larvae Revisions could further improve the accuracy of population size predictions for the next generation of larvae.

[0040] Second, data on key biological factors were acquired for a set time period following the peak migration date, and a biological environmental load factor was calculated based on this data. The key biological factor data included at least one of the following: the rice's developmental stage, rice variety, rice planting density, and the number of natural enemies in the field. This biological environmental load factor... This refers to a comprehensive parameter that adjusts the predicted population size for spatial carrying capacity and biological resistance based on rice physiological development stages, varietal resistance characteristics, planting density, and the pest control capabilities of natural enemies in the field. This factor reflects the ecological coupling relationship among the host, pests, and natural enemies.

[0041] In embodiments where key biological factor data include: the developmental stage of rice, rice variety, rice planting density, and the number of natural enemies in the field, the calculation of the biological environmental load factor based on each key biological factor data may include the following steps: First, based on the developmental stage of rice, determine the synergistic factors for rice growth. Specifically, the severity of rice leaf roller infestation is closely related to the rice growth stage. Rice leaf roller larvae prefer to feed on tender, green leaves with high nitrogen content. During the tillering to booting stage of rice, the plants are young and have high nitrogen content, which is most conducive to larval development. Therefore, if the rice is in the tillering to booting stage, the infestation will be more severe. The setting is 1.2. After the heading stage, rice leaves gradually age and become fibrous, reducing essential nutrients for larvae such as soluble sugars and proteins. Leaf hardening increases the difficulty of leaf curling, leading to a significant decrease in larval survival rates. Therefore, if the rice is in a developmental stage after the heading stage, then... The value is set to 0.6. For other growth stages, such as the seedling stage and the grain-filling to maturity stage, the seedling stage has small plants, few leaves, and insufficient space for larvae to roll leaves. After the grain-filling stage, leaf aging, increased fibrosis, and decreased nutrition are all unfavorable for the development of rice leaf roller larvae. Therefore, the value is set to 0.6. Set to 0.7.

[0042] Secondly, based on the rice variety and planting density, the factors of variety resistance and plant density are determined. Specifically, the leaf width, bristle density, and hardness of different rice varieties directly affect the oviposition selection of female moths and the survival of larvae. For susceptible varieties, the core impact of planting density lies in its regulation of field canopy closure and microenvironment humidity. Planting density can generally be divided into three ranges: low density (e.g., ≤25 holes / m²), medium density (25–35 holes / m²), and high density (≥35 holes / m²). High-density planting, due to its high field canopy closure and high microenvironment humidity, is conducive to pest outbreaks. When planted at a medium density, the field canopy closure is moderate, which has a certain promoting effect on the population, but it is not as significant as that of high density. Take 1.0; when planted at low density, the field has good ventilation and light penetration, and the microenvironment humidity is close to the atmospheric humidity, so there is no obvious promoting effect. Take 0.9. Insect-resistant varieties reduce the success rate of larval leaf rolling through physical barriers and biochemical resistance; their core mechanism of action is independent of planting density. Therefore, regardless of planting density, insect-resistant varieties... All values ​​are uniformly set to 0.8, without needing to differentiate by density. This is because the inhibition of larval survival by insect-resistant varieties is mainly achieved through the variety's own characteristics. The effects of its "physical barrier" and "biochemical resistance" remain relatively stable when planting density changes. Environmental stress factors such as low-temperature synergy are already considered in other coefficients of the model (such as the temperature correction coefficient). Humidity correction factor Rainfall erosion coefficient In the treatment of insect-resistant varieties, the correction factor should be independent of density.

[0043] Next, based on the number of natural enemies in the field, the pest control factors of natural enemies were determined. Specifically, parasitic natural enemies (such as Trichogramma wasps) and predatory natural enemies (such as spiders and black-shouldered green mirid bugs) in the field have a significant natural control effect on rice leaf rollers. If green pest control is implemented in the monitoring area for a long time, the use of chemical pesticides is low, and the base number of natural enemies is high, then a reduction correction coefficient is introduced. The classification can be set based on the actual number of natural enemies in the field: when there are ≥50 natural enemies per 100 bushes. Use 0.7-0.8 (significant pest control); for 20-50 heads. Use 0.85–0.95; for <20 heads Take 1.0. If actual measurement is not possible, it can also be inferred based on prevention and control management – ​​green prevention and control zone (low pesticide use). Take 0.85, for routine prevention and control areas (2-3 applications per year). Take 0.95 for areas with excessive chemical control (≥4 times / season and frequent use of highly toxic pesticides). Take 1.0.

[0044] Finally, based on the synergistic factors of rice growth period Variety resistance and plant density factors and natural enemy pest control factors Calculate the biological environmental load factor Among them, the biological environmental load factor The calculation formula is as follows: Third, the contribution value of the basic population is corrected based on the environmental survival correction matrix and the biological environmental load factor to obtain the population size of the next generation of larvae. The formula for calculating the population size of the next generation of larvae is shown below: In the formula, Contribution value to the basic population of the next generation of larvae For environmental survival correction matrix, This refers to the biological environmental load factor.

[0045] Fourth, the severity of rice leaf roller infestation is determined based on the population size of the next generation of larvae. Specifically, after calculating the population size of the next generation of larvae, the percentage of the area affected by this larvae relative to the transplanted rice area is obtained. Then, by combining the population size of the next generation of larvae with this percentage, the severity of rice leaf roller infestation is determined.

[0046] In one possible implementation, when determining the severity of rice leaf roller larvae infestation by comprehensively considering the population size and proportion of the next generation of larvae, a pre-defined grading index table for rice leaf roller infestation severity is used. Specifically, the population size and proportion of the next generation of larvae are determined by consulting the grading index table. This comprehensive approach, which prioritizes larval quantity over area, more accurately reflects the actual damage caused by rice leaf roller larvae, avoiding overestimation due to localized high density but small overall area, or underestimation due to large-area low density, thus improving the scientific rigor and practicality of early warning systems.

[0047] In a specific embodiment, the pre-set grading index table of the occurrence degree of rice leaf folder in the "Technical Specification for Monitoring Rice Leaf Scroll in Guizhou Province" (DB52 / T 395—2005) is shown in Table 1.

[0048] Table 1 Grading Indicators for Rice Leaf Roller Occurrence Severity In one possible implementation, after determining the severity of rice leaf roller larvae infestation, the system also includes issuing alerts based on the severity level. Specifically, when the predicted severity of the next generation of larvae infestation reaches level 3 (moderate infestation) or higher, the system automatically triggers an alert and issues blue, yellow, orange, or red warnings according to the level. The alert content includes the predicted severity level, the number of larvae per 100 plants, the optimal control period (e.g., 7-10 days after hatching), and recommended pesticides, which are pushed to plant protection personnel and farmers via SMS, mobile apps, etc.

[0049] To clearly illustrate the predictive accuracy of this method, two specific examples are provided below to demonstrate its predictive performance. Comparison of real-time observed rice leaf roller occurrence severity with predicted levels in 2023 (1) Initial background and data collection ) calculation Monitoring data: From the end of May to the beginning of June 2023, the Longbao Dam in Anshun City entered the peak period of adult moth migration.

[0050] Field survey (A): Through field measurements of moths, the average number of moths per 100 clumps was A = 32.4 moths / 100 clumps.

[0051] Basic parameter settings: Female-to-male ratio (S): 1.5; Percentage of animals that had mated before migration ( : 10% (average), number of eggs laid ( ): 100 grains; mating rate after migration ( ): 90%, compared with egg production ( ): 50 pieces.

[0052] Grain / Hundred Clumps (2) Environmental survival correction matrix ( Application of ) temperature( In one possible implementation, the temperature correction factor can be determined based on the daily average temperature within each sub-time period divided within a set time period. For example, the average temperature from the end of May to the beginning of June is shown in Table 2. The average daily temperature for the six days from May 20th to May 25th was 22-24℃. Therefore, a temperature correction factor is applied to these days. The value is set to 0.8; the average daily temperature for the five days from May 26th to May 30th was 25-26℃, therefore, the temperature correction factor for these days is set to 0.8. The value is set to 0.8; the average daily temperature for the four days from May 31st to June 3rd was 26-28℃, therefore, the temperature correction factor for these days is set to 0.8. Set to 0.95, using the number of days in each sub-time period as the temperature correction factor for these sub-time periods. The weighted average can then be used as the final temperature correction factor for the period from the end of May to June. The final temperature correction factor = (6×0.8+5×0.8+4×0.95) / 15=0.84.

[0053] In another possible implementation, the final temperature correction factor can be determined based on the average daily temperature over a given time period. For example, by calculating the average daily temperature over the 15 days from the end of May to the beginning of June, and based on this average daily temperature, the final temperature correction factor is set according to the rules for determining the temperature correction factor mentioned above. .

[0054] Table 2 Environmental Survival Factor Monitoring Table humidity( Anshun Dam area is currently experiencing the start of the rainy season, with relative humidity consistently above 85%. Humidity correction factor... Take 1.1.

[0055] Rainfall ( As shown in the table above, although there was rainfall from the end of May to the beginning of June, it was mostly light rain. However, a strong scour event with rainfall exceeding 50mm occurred on June 2nd. Therefore, the scour coefficient of this rainfall can be... Set it to 0.7.

[0056] Based on the determined temperature correction factor The humidity correction factor is 0.84. The erosion coefficient is 1.1, which is the rainfall scour coefficient. In the embodiment where 0.7 is used, The calculation formula is as follows: (3) Biological environmental load factor ( Application of ) Rice growth period synergistic factors ( At this time, the rice at Longbao Dam is in the peak tillering stage, and the plants are tender and green, which is very conducive to leaf curling. The correction coefficient is taken as 1.2.

[0057] Variety resistance and plant density factor ( The area is mostly populated with high-quality rice susceptible to insects and planted at medium density. A value of 1.0 is used because this area is a routine pest control zone with a moderate frequency of pesticide use. Take 0.95.

[0058] (4) Comprehensive prediction results ( ) Head / Hundred Clumps (5) Comparison and verification with actual situation According to the Guizhou Provincial Local Standard (Table 1), the model predicts a population of 788 insects per 100 rice plants. Meanwhile, the proportion of the area affected by this insect population to the transplanted rice area, as determined by a field survey, is approximately 85%, which falls under Level 5 (major outbreak).

[0059] Actual situation verification: From the end of June to the beginning of July 2023, the Longbao Dam in Anshun City did indeed experience the most severe rice leaf roller infestation in nearly ten years, with the number of larvae per 100 plants in some fields even exceeding 1,048. An emergency prevention and control warning was issued for the whole city.

[0060] Analysis of the formula's rationality: The formula's prediction of 788 insects far exceeds the "major outbreak" threshold of 300, accurately capturing the population explosion caused by the "coupled temperature and humidity" and "overlapping tillering stages" that year. It should be noted that the predicted 788 insects is based on the average value of a systematic survey of the entire monitoring area (multiple representative fields) at Longbao Dam, while "local fields exceeding 1048 insects" refers to individual hotspot fields with the most concentrated insect population within the area. Because adult rice leaf rollers migrate and distribute unevenly in the fields (preferring to lay eggs in light green, densely vegetated areas), there is significant spatial heterogeneity in larval occurrence; the highest measured insect population in a single field is often 1.3 to 1.5 times the regional average. Therefore, the ratio of 1048 to 788 insects (1.33 times) fully conforms to this natural distribution pattern. Based on the actual results, both 788 and 1048 belong to level 5 major outbreaks, effectively serving as an early warning system.

[0061] Example 2: Comparison of real-time observed rice leaf roller occurrence in 2024 with predicted levels. (1) Initial background and data collection ) calculation Monitoring data: From the end of May to the beginning of June 2024, the Longbao Dam in Anshun City entered the peak period of adult moth migration.

[0062] Field survey (A): Through field measurements of moth driving, the average number of moths per 100 clumps was A = 6.1 moths / 100 clumps.

[0063] Basic parameter settings: Female-to-male ratio (S): 1.5; Percentage of animals that had mated before migration ( : 10% (average), number of eggs laid ( ): 100 grains; mating rate after migration ( ): 90%, compared with egg production ( ): 50 pieces.

[0064] Grain / Hundred Clumps (2) Environmental survival correction matrix ( Application of ) temperature( In late May 2024, temperatures in Guizhou Province were lower than normal and fluctuated considerably, with average temperatures between 22 and 24°C. The hatching rate coefficient... =0.8.

[0065] Table 3 Environmental Survival Factor Monitoring Table humidity( Anshun Dam area is currently experiencing the start of the rainy season, with relative humidity consistently above 85%. (Correction factor...) Take 1.1.

[0066] Rainfall ( The forecast indicates that from late May to early June, there will be moderate to heavy rain. Between May 20th and June 3rd, there are predicted to be three events of moderate to heavy rainfall, including two moderate rain events and one heavy rain event. The erosion coefficients for the two moderate rain events are 0.9, and the erosion coefficient for the heavy rain event is 0.7. Multiplying these by 0.9 × 0.9 × 0.7 = 0.57, the final erosion coefficient is... Set it to 0.57.

[0067] (3) Biological environmental load factor ( Application of ) Rice growth period synergistic factors ( At this time, the rice at Longbao Dam is in the peak tillering stage, and the plants are tender and green, which is very conducive to leaf curling. The correction coefficient is taken as 1.2.

[0068] Variety resistance and plant density factor ( The area is mostly populated with high-quality rice susceptible to insects and planted at medium density. A value of 1.0 is used because this area is a routine pest control zone with a moderate frequency of pesticide use. Take 0.95.

[0069] (4) Comprehensive prediction results ( ) Head / Hundred Clumps (5) Comparison and verification with actual situation According to the Guizhou Provincial Local Standard (Table 1), the model predicts a value of 113 insects per 100 rice plants. Meanwhile, the area affected by this insect population accounts for approximately 35% of the transplanted rice area, which falls under level 3 (moderate).

[0070] Real-world verification: Through field data from May 21 to June 5, 2024, at Longbao Dam in Anshun City, Guizhou Province, the model successfully identified the inhibitory effect of unfavorable reproductive factors on the population and predicted that the next generation of larvae would be 113 per 100 clumps (moderate occurrence), which is highly consistent with the field measurement of 110-185 larvae. This effectively corrected the false alarms that might have been caused by the high initial population, and proved the accuracy and practical value of the multi-factor coupling model in early warning under complex habitats.

[0071] Formula rationality analysis: This composite formula overcomes the one-sidedness of traditional forecasting that relies solely on adult insect numbers by establishing a three-dimensional coupling logic of "basic population - environmental stress - habitat load". It scientifically introduces a piecewise temperature development function, a high humidity gain factor, and a rainfall physical erosion coefficient, enabling it to accurately identify the inhibitory effect of low temperatures and rainy weather on pest hatching and survival in Anshun in 2024. This corrects the seemingly high moth population base to a moderate occurrence level that reflects reality, significantly improving the accuracy of nonlinear fitting in early warning and its guiding value for disaster prevention and pesticide reduction.

[0072] 1.5 Severity of Occurrence The system automatically determines the extent of the next generation of larvae based on the "Technical Specification for Monitoring Rice Leaf Roller in Guizhou Province" (DB52 / T 395—2005).

[0073] This disclosure provides a method for predicting the occurrence of rice leaf folder larvae, including: identifying the peak day of adult migration of rice leaf folder into rice plants and obtaining the base number of adults per 100 plants on the peak day; calculating the basic population contribution value of the next generation of larvae based on a pre-set female-to-male ratio factor of the migrating adults, an oviposition potential matrix of the migrating population, and the base number of adults per 100 plants; and determining the occurrence of rice leaf folder larvae based on the basic population contribution value of the next generation of larvae. In this disclosure, by introducing the female-to-male ratio factor of the adults, the total number of migrating moths can be accurately converted into the number of female moths capable of reproduction. Simultaneously, by introducing the oviposition potential matrix of the migrating population, the number of migrating female moths can be further accurately converted into the basic population contribution value of the next generation of larvae, thereby achieving a precise conversion from "effective female moth population" to "expected larval population". Therefore, this system solves the prediction bias problem caused by ignoring the differences in sex structure and egg production in traditional methods, and significantly improves the accuracy and scientific validity of early inference of the occurrence of rice leaf roller larvae.

[0074] <Device Embodiment> Figure 2 A schematic block diagram of a device for predicting the occurrence of rice leaf roller larvae according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device 100 includes: The first calculation module 110 is used to identify the peak day of the migration of contemporary adult rice leaf rollers into rice bushes and to obtain the base number of adult insects per hundred bushes on the peak day of the migration. The second calculation module 120 is used to calculate the basic population contribution value of the next generation of larvae based on the pre-set female-to-male ratio factor of the migrating adult, the oviposition potential matrix of the migrating population, and the base number of adults per hundred clusters. The occurrence degree prediction module 130 is used to determine the occurrence degree of rice leaf roller larvae based on the basic population contribution value of the next generation of larvae.

[0075] <Equipment Example> Figure 3 A schematic block diagram of a device for predicting the occurrence of rice leaf roller larvae according to an embodiment of the present disclosure is shown. Figure 3 As shown, the rice leaf folder larvae occurrence prediction device 200 includes a processor 210 and a memory 220 for storing executable instructions of the processor 210. The processor 210 is configured to implement any of the aforementioned rice leaf folder larvae occurrence prediction methods when executing the executable instructions.

[0076] It should be noted here that the number of processors 210 can be one or more. Furthermore, the rice leaf roller larvae occurrence severity prediction device 200 of this embodiment may also include an input device 230 and an output device 240. The processors 210, memory 220, input device 230, and output device 240 can be connected via a bus or other means, which are not specifically limited here.

[0077] The memory 220, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and various modules, such as the program or module corresponding to the rice leaf folder larvae occurrence prediction method of this disclosure embodiment. The processor 210 executes various functional applications and data processing of the rice leaf folder larvae occurrence prediction device 200 by running the software program or module stored in the memory 220.

[0078] Input device 230 can be used to receive input digital numbers or signals. These signals may include key signals related to user settings and function control of the device / terminal / server. Output device 240 may include a display device such as a screen.

[0079] <Storage Medium Examples> According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored, which, when executed by processor 210, implement any of the preceding methods for predicting the occurrence of rice leaf roller larvae.

[0080] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting the occurrence of Cnaphalocrocis medinalis Guenee larvae, characterized by, include: Identify the peak day of the migration of contemporary adult rice leaf rollers into rice bushes, and obtain the base number of adults per 100 bushes on the peak day of the migration. Based on the pre-set female-to-male ratio factor of the migrating adults, the oviposition potential matrix of the migrating population, and the base number of adults per hundred clusters, the basic population contribution value of the next generation of larvae is calculated. The extent of rice leaf roller larvae occurrence was determined based on the baseline population contribution value of the next generation of larvae.

2. The method of claim 1, wherein, The formula for calculating the basic population contribution value of the next generation of larvae is as follows: In the formula, Contribution value to the basic population of the next generation of larvae This refers to the base number of adult insects in the aforementioned hundred clusters. The pre-set adult male-to-female ratio factor, The pre-defined matrix of the spawning potential of the migrating population.

3. The method according to claim 2, characterized in that, The oviposition potential matrix of the migrating population is constructed based on the preset percentage of mated migrating individuals, the oviposition of adults that have mated before migration, the percentage of mated individuals after migration, and the oviposition of adults that have mated after migration.

4. The method according to claim 1, characterized in that, When determining the extent of rice leaf roller larvae occurrence based on the baseline population contribution value of the next generation of larvae, the following is included: Data on key meteorological factors within a set time period after the peak migration day are obtained, and an environmental survival correction matrix is ​​calculated based on the data on each key meteorological factor. Data on key biological factors are obtained within a set time period after the peak migration day, and biological environmental load factors are calculated based on the data of each key biological factor. The population size of the next generation of larvae is obtained by correcting the basic population contribution value based on the environmental survival correction matrix and the biological environmental load factor. The extent of rice leaf roller larvae occurrence was determined based on the population size of the next generation of larvae.

5. The method according to claim 4, characterized in that, The key meteorological factor data mentioned above include at least one of the following: temperature data, relative humidity data, rainfall data, and lunar phase data.

6. The method according to claim 4, characterized in that, The key biological factor data mentioned include at least one of the following: the developmental stage of rice, rice variety, rice planting density, and the number of natural enemies in the field.

7. The method according to claim 1, characterized in that, After determining the extent of rice leaf roller larvae infestation, the method also includes issuing an alert based on the extent of infestation.

8. A device for predicting the occurrence of rice leaf roller larvae, characterized in that, include: The first calculation module is used to identify the peak day of the migration of contemporary adult rice leaf rollers into rice bushes and to obtain the base number of adult insects per hundred bushes on the peak day of the migration. The second calculation module is used to calculate the basic population contribution value of the next generation of larvae based on the pre-set female-to-male ratio factor of the migrating adult, the oviposition potential matrix of the migrating population, and the base number of adults in the hundred clusters. The occurrence severity prediction module is used to determine the occurrence severity of rice leaf roller larvae based on the basic population contribution value of the next generation of larvae.

9. A device for predicting the occurrence of rice leaf roller larvae, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 7 when executing the executable instructions.

10. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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