Spray effect evaluation method and system based on sound detection, and electronic device

Abstract

This disclosure provides a method, system, and electronic device for evaluating spraying effectiveness based on sound detection. The method includes: acquiring the original spraying sound and the impact sound of droplets reaching the target crop; wherein the original spraying sound is the sound from the nozzle of the spraying equipment during spraying; filtering the impact sound and the original spraying sound respectively; analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effectiveness evaluation index based on the analysis results; and determining the spraying effectiveness evaluation result based on the spraying effectiveness evaluation index. This method achieves spraying effectiveness evaluation based on sound signals, is low-cost and easy to deploy, improves the real-time performance and accuracy of spraying effectiveness evaluation, thereby avoiding pesticide waste and reducing the risk of pesticide damage.

Description

Technical Field

[0001] This disclosure relates to the field of agricultural information technology, and in particular to a method, system and electronic device for evaluating spraying effect based on sound detection. Background Technology

[0002] In existing technologies, automatic spraying systems cannot detect in real time whether droplets are effectively deposited on crop leaves, and it is difficult to detect in real time the drift of pesticides caused by strong winds. They cannot provide intuitive and real-time feedback on the spraying effect, which can easily lead to pesticide waste and the risk of pesticide damage. Existing methods such as vision, water-sensitive paper, and LiDAR (Light Detection and Ranging, a technology that uses lasers for ranging and detection) are either too costly and computationally intensive, or can only be processed offline, making it difficult to meet the needs of real-time, continuous, and whole-field operation monitoring. Summary of the Invention

[0003] The technical problem to be solved by this disclosure is to overcome the shortcomings of the prior art in that it cannot detect the spraying effect of droplets in real time and accurately, and to provide a spraying effect evaluation method, system and electronic device based on sound detection.

[0004] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0005] Firstly, a method for evaluating spraying effectiveness based on sound detection is provided, the method comprising the following steps:

[0006] Acquire the original spraying sound and the impact sound of the droplets reaching the target crop; wherein, the original spraying sound is the sound at the nozzle when the spraying equipment is spraying;

[0007] The impact sound and the original spraying sound are filtered respectively;

[0008] The filtered impact sound and the filtered original spraying sound were analyzed, and the spraying effect evaluation index was calculated based on the analysis results.

[0009] The spraying effect evaluation result is determined based on the spraying effect evaluation indicators.

[0010] Optionally, the step of filtering the impact sound and the original spraying sound respectively includes:

[0011] Based on the type of the target crop and the preset parameters of the spraying equipment, obtain the corresponding target impact sound frequency band and the target original spraying sound frequency band;

[0012] The impact sound is filtered based on the target impact sound frequency band to obtain the actual impact sound frequency band; and the original spraying sound is filtered based on the target original spraying sound frequency band to obtain the actual original spraying sound frequency band.

[0013] Optionally, the impact sound includes a first impact sound and a second impact sound, wherein the first impact sound is the sound of the droplets reaching the top of the target crop, and the second impact sound is the sound of the droplets reaching the bottom of the target crop; the analysis result includes a volume value, and the spraying effect evaluation index includes the droplet drift index;

[0014] The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include:

[0015] The filtered first impact sound and the filtered second impact sound were analyzed separately to obtain the corresponding first volume value and second volume value.

[0016] The drift index is calculated based on the first volume value and the second volume value;

[0017] And / or,

[0018] The impact sound includes a third impact sound and a fourth impact sound. The third impact sound is the impact sound corresponding to the target crop located on the left side of the spraying equipment, and the fourth impact sound is the impact sound corresponding to the target crop located on the right side of the spraying equipment. The spraying effect evaluation index includes the uniformity index.

[0019] The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include:

[0020] The filtered third and fourth impact sounds were analyzed separately to obtain the corresponding third and fourth volume values.

[0021] The uniformity index is calculated based on the third volume value and the fourth volume value.

[0022] Optionally, the spraying effect evaluation index includes the sound signal energy ratio;

[0023] The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include:

[0024] The filtered original spraying sound was analyzed to obtain the corresponding fifth volume value;

[0025] The sound signal energy ratio is calculated based on the first volume value and the fifth volume value.

[0026] Optionally, the spraying effect evaluation method further includes:

[0027] The changing trend of the drift index is obtained based on the drift index within a preset time period;

[0028] In response to the change trend exceeding the change threshold, a strong wind alarm signal is issued.

[0029] Optionally, the steps of analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include:

[0030] Time-domain analysis of the filtered impact sound yields the actual impact frequency of the fog droplets impacting the target crop.

[0031] The deposition density index is calculated based on the actual impact frequency and the standard impact frequency; wherein the standard impact frequency is associated with the type of the target crop and the preset parameters of the spraying equipment.

[0032] Secondly, a spraying effect evaluation system based on sound detection is provided, characterized in that the spraying effect evaluation system includes:

[0033] The sound acquisition module is used to acquire the original spraying sound and the impact sound of the droplets reaching the target crop; wherein, the original spraying sound is the sound at the nozzle when the spraying equipment is spraying;

[0034] A filtering module is used to filter the impact sound and the original spraying sound respectively;

[0035] The analysis and calculation module is used to analyze the filtered impact sound and the filtered original spraying sound, and calculate the spraying effect evaluation index based on the analysis results.

[0036] The result determination module is used to determine the spraying effect evaluation result based on the spraying effect evaluation index.

[0037] Optionally, the filtering module specifically includes:

[0038] The acquisition unit is used to acquire the corresponding target impact sound frequency band and the target original spraying sound frequency band according to the type of the target crop and the preset parameters of the spraying equipment.

[0039] The filtering unit is used to filter the impact sound based on the target impact sound frequency band to obtain the actual impact sound frequency band; and to filter the original spraying sound based on the target original spraying sound frequency band to obtain the actual original spraying sound frequency band.

[0040] Optionally, the impact sound includes a first impact sound and a second impact sound, wherein the first impact sound is the sound of the droplets reaching the top of the target crop, and the second impact sound is the sound of the droplets reaching the bottom of the target crop; the analysis result includes a volume value, and the spraying effect evaluation index includes the droplet drift index;

[0041] The result determination module specifically includes:

[0042] The first analysis unit is used to analyze the filtered first impact sound and the filtered second impact sound respectively to obtain the corresponding first volume value and second volume value.

[0043] The first calculation unit is used to calculate the drift index based on the first volume value and the second volume value;

[0044] And / or,

[0045] The impact sound includes a third impact sound and a fourth impact sound. The third impact sound is the impact sound corresponding to the target crop located on the left side of the spraying equipment, and the fourth impact sound is the impact sound corresponding to the target crop located on the right side of the spraying equipment. The spraying effect evaluation index includes the uniformity index.

[0046] The result determination module specifically includes:

[0047] The second analysis unit is used to analyze the filtered third impact sound and the filtered fourth impact sound respectively, and obtain the corresponding third volume value and fourth volume value.

[0048] The second calculation unit is used to calculate the uniformity index based on the third volume value and the fourth volume value.

[0049] Optionally, the spraying effect evaluation index includes the sound signal energy ratio;

[0050] The result determination module specifically includes:

[0051] The third analysis unit is used to analyze the filtered original spraying sound and obtain the corresponding fifth volume value;

[0052] The third calculation unit is used to calculate the sound signal energy ratio based on the first volume value and the fifth volume value.

[0053] Optionally, the spraying effect evaluation system further includes:

[0054] The trend acquisition module is used to obtain the trend of the drift index based on the drift index within a preset time period;

[0055] An alarm module is used to issue a strong wind alarm signal in response to the change trend exceeding a change threshold.

[0056] Optionally, the result determination module specifically includes:

[0057] The fourth analysis unit is used to perform time-domain analysis on the filtered impact sound to obtain the actual impact frequency of the fog droplets hitting the target crop.

[0058] The fourth calculation unit calculates the deposition density index based on the actual impact frequency and the standard impact frequency; wherein the standard impact frequency is related to the type of the target crop and the preset parameters of the spraying equipment.

[0059] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the sound detection-based spraying effect evaluation method described in the first aspect.

[0060] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the sound detection-based spraying effect evaluation method described in the first aspect.

[0061] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the sound detection-based spraying effect evaluation method described in the first aspect. Based on common knowledge in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain various preferred embodiments of this disclosure.

[0062] The positive and progressive effects of this disclosure are as follows: To solve the problem of the inability to accurately detect the spraying effect of droplets in real time in existing technologies, this disclosure achieves the evaluation of spraying effect based on sound signals by acquiring and filtering the original spraying sound at the nozzle and the impact sound of droplets reaching the target crop during spraying. This method is low-cost and easy to deploy. The filtered impact sound and the filtered original spraying sound are analyzed, and spraying effect evaluation indicators are calculated based on the analysis results. The spraying effect evaluation result is determined based on the spraying effect evaluation indicators. This improves the real-time performance and accuracy of spraying effect evaluation, provides more intuitive feedback results, thereby avoiding pesticide waste and reducing the risk of pesticide damage. Attached Figure Description

[0063] Figure 1 A flowchart of a spraying effect evaluation method based on sound detection provided in Embodiment 1 of this disclosure;

[0064] Figure 2 A detailed flowchart of step S12 provided in Embodiment 1 of this disclosure;

[0065] Figure 3 A detailed flowchart of step S13 provided in Embodiment 1 of this disclosure;

[0066] Figure 4 A detailed flowchart of step S13 provided in Embodiment 1 of this disclosure;

[0067] Figure 5A detailed flowchart of step S13 provided in Embodiment 1 of this disclosure;

[0068] Figure 6 A partial flowchart of a spraying effect evaluation method based on sound detection provided in Embodiment 1 of this disclosure;

[0069] Figure 7 A detailed flowchart of step S13 provided in Embodiment 1 of this disclosure;

[0070] Figure 8 A detailed flowchart of a spraying effect evaluation method based on sound detection provided in Embodiment 1 of this disclosure;

[0071] Figure 9 This is a structural block diagram of an intelligent spraying operation closed-loop control system provided in Embodiment 1 of this disclosure;

[0072] Figure 10 This is a schematic diagram of a spraying effect evaluation system based on sound detection provided in Embodiment 2 of this disclosure;

[0073] Figure 11 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of this disclosure. Detailed Implementation

[0074] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0075] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0076] The spraying effect evaluation method based on sound detection in this disclosure can be applied to ground unmanned vehicle spraying, such as outdoor orchards, vineyards, and dwarf dense planting orchards where there are obvious gusts of wind; it can also be applied to forestry, landscaping and urban greening spraying, such as spraying disinfection or pest control for roadside trees, hedges, park landscapes, etc.; it can also be applied to precision agriculture research as an evaluation means to objectively record and audit the quality of operations.

[0077] Furthermore, the sound signals in the spraying effect evaluation method of this disclosure can be fused with multi-source information such as images (e.g., visible light, multispectral, and thermal infrared), wind speed and direction, GPS (Global Positioning System) or RTK (Real-Time Kinematic) positioning to construct a more comprehensive and accurate spraying quality evaluation model. For example, by using image recognition of crop canopy structure and pest distribution, combined with the deposition effect of acoustic feedback, true "precision target application" can be achieved.

[0078] In large-scale planting scenarios, using the spraying effect evaluation method in the publicly disclosed embodiments, multiple spraying devices can form a collaborative operation network, with each device uploading spraying effect data to the cloud in real time. Through big data analysis, a spatial distribution map of farmland spraying effects can be constructed, identifying "problem areas" for subsequent respraying; at the same time, a database of optimal spraying parameters for different crops, different growth stages, and different weather conditions can be accumulated, providing decision support for subsequent operations.

[0079] Currently, pesticide spraying quality assessment mainly relies on offline methods such as water-sensitive paper and dyeing. By promoting and applying the spraying effect assessment method in the disclosed embodiments, a spraying effect assessment standard based on acoustic characteristics can be gradually established, upgrading pesticide spraying quality detection from "post-operation offline assessment" to "real-time online assessment during operation," thus promoting the intelligent upgrading of plant protection operations.

[0080] Example 1

[0081] Figure 1 This embodiment provides a flowchart of a spraying effect evaluation method based on sound detection, which includes the following steps:

[0082] S11. Acquire the original spraying sound and the impact sound of the droplets reaching the target crop; wherein, the original spraying sound is the sound at the nozzle when the spraying equipment is spraying.

[0083] In this embodiment, multiple acoustic sensors can be deployed at various locations on the spraying platform to acquire the original spraying sound and the impact sound of droplets reaching the target crop. This enables the evaluation of spraying effectiveness based on sound signals, resulting in low evaluation costs and easy deployment. In specific implementations, the acoustic sensors can be miniature microphone arrays. These arrays can be positioned at the spray boom end or the arm end of the spraying equipment to acquire the original spraying sound; they can also be positioned above the target crop canopy to collect the impact sound of droplets reaching the target crop as the primary signal. Additionally, they can be positioned below the target crop canopy to collect the impact sound of droplets reaching the target crop as an environmental noise reference and drift detection reference. Through array beamforming and adaptive noise suppression, the miniature microphone array can still extract effective impact sound signals under strong winds and mechanical noise, improving environmental robustness. Furthermore, compared to expensive high-speed photography systems and high-end detection equipment such as hyperspectral cameras, the cost of miniature microphone arrays is extremely low, demonstrating good economic viability and promising prospects for widespread application. Therefore, this embodiment adopts a non-contact acoustic detection scheme. The microphone is placed on the spraying equipment or near the target crop canopy. It does not need to contact the crop leaves, does not affect the normal operation of the spraying operation, and avoids the pollution and wear problems that may be caused by contact sensors.

[0084] S12. Filter the impact sound and the original spraying sound respectively.

[0085] In this embodiment, the impact sound and the original spraying sound are filtered respectively to obtain the sound signal of the corresponding frequency band, thereby achieving noise suppression and realizing effective identification of the sound signal during the spraying period.

[0086] S13. Analyze the filtered impact sound and the filtered original spraying sound, and calculate the spraying effect evaluation index based on the analysis results.

[0087] In this embodiment, short-time Fourier transform can be used to analyze the filtered impact sound and the filtered original spray sound, extracting the corresponding acoustic features as the analysis results. These acoustic features can include at least one of the following: frequency band energy distribution features, impact event detection features, power spectral density features, and spectral centroid and bandwidth features. Specifically, the characteristic frequency bands corresponding to the filtered impact sound and the filtered original spray sound are divided into several sub-frequency bands, such as a low-frequency band of 300~2kHz, a mid-frequency band of 2~6kHz, and a high-frequency band of 6~15kHz. The frequency band energy distribution features are used to characterize the average energy proportion of each sub-frequency band in the characteristic frequency band; the impact energy of large-diameter droplets is concentrated in the lower frequency band, while the impact energy of small-diameter droplets is more widely distributed. In a specific implementation, the occurrence frequency and amplitude distribution of droplet impact events can also be detected through time-domain envelope analysis to obtain impact event detection features, which reflect the density of droplets impacting the leaf surface per unit time. In specific implementations, the Welch algorithm can also be used to calculate the estimated power spectral density of the acoustic signal and extract the energy values ​​at characteristic frequency points, thereby obtaining the power spectral density characteristics. Additionally, the spectral centroid and bandwidth characteristics are used to reflect the overall frequency distribution characteristics of the characteristic frequency band.

[0088] Furthermore, the spraying effect evaluation index is calculated based on the analysis results. In one specific implementation, the spraying effect evaluation index can be calculated by constructing a quantitative evaluation model for the spraying effect, thus realizing the evaluation of the spraying effect based on the sound signal. The spraying effect evaluation index may include at least one of the following: deposition density index, uniformity index, drift index, and sound signal energy ratio.

[0089] S14. Determine the spraying effect evaluation result based on the spraying effect evaluation indicators.

[0090] In this embodiment, by acquiring and filtering the original spraying sound from the nozzle and the impact sound of droplets reaching the target crop during spraying, the spraying effect can be evaluated based on sound signals. This method is low-cost and easy to deploy. The filtered impact sound and the filtered original spraying sound are analyzed, and spraying effect evaluation indicators are calculated based on the analysis results. The spraying effect evaluation result is determined based on these indicators. This improves the real-time performance and accuracy of spraying effect evaluation, providing more intuitive feedback results, thereby avoiding pesticide waste and reducing the risk of pesticide damage. Furthermore, monitoring based on acoustic signals is unaffected by light intensity, diurnal variations, and weather conditions, enabling continuous monitoring around the clock, overcoming the limitations imposed by light conditions on image detection and spectral detection methods in existing technologies.

[0091] In one alternative implementation, such as Figure 2 As shown, step S12 specifically includes:

[0092] S121. Obtain the corresponding target impact sound frequency band and the target original spraying sound frequency band according to the type of the target crop and the preset parameters of the spraying equipment.

[0093] Due to the varying leaf hardness of different crops, droplet size of different nozzles, and spraying pressure, the frequency band of impact sound characteristics can differ by 2 to 3 kHz. Therefore, a fixed 300 to 15 kHz frequency band cannot be adapted to different scenarios, and a fixed frequency band is prone to introducing irrelevant noise or losing effective signals.

[0094] In this embodiment, by acquiring the target impact sound frequency band and the target original spraying sound frequency band that are compatible with the type of target crop and the preset parameters of the spraying equipment, the problem of fixed frequency bands easily introducing irrelevant noise or losing effective signals is solved. In a specific embodiment, a scene-to-feature frequency band mapping library can be pre-established to store the target impact sound frequency band and the target original spraying sound frequency band corresponding to different types of target crops and different preset parameters. The type of target crop can include wheat, corn, and fruit trees. The preset parameters of the spraying equipment can include the nozzle model and spraying pressure. For example, through multiple experiments, it can be found that the impact sound of wheat leaves is concentrated in the range of 400~8kHz, and the impact sound of apple leaves is concentrated in the range of 300~10kHz.

[0095] In practical applications, the system automatically loads the corresponding target impact sound frequency band and the target original spraying sound frequency band based on the preset parameters of the spraying equipment and the type of the target crop, avoiding the introduction of irrelevant noise and the loss of effective signals. Furthermore, the cutoff frequency can be dynamically adjusted in real time based on the impact event density during spraying operations, such as setting the cutoff frequency to ±500Hz.

[0096] Furthermore, in other implementations, frequency band validity verification can also be performed. If the number of valid impact events within the current target impact sound frequency band obtained in subsequent analysis is lower than a threshold, the system automatically switches to an adjacent frequency band of the current target impact sound frequency band for re-detection to avoid the current target impact sound frequency band being invalid.

[0097] S122. Filter the impact sound based on the target impact sound frequency band to obtain the actual impact sound frequency band; and filter the original spraying sound based on the target original spraying sound frequency band to obtain the actual original spraying sound frequency band.

[0098] In this embodiment, the impact sound is filtered based on the target impact sound frequency band to obtain the actual impact sound frequency band. This achieves dynamic adaptive bandpass filtering, preserving the relevant frequency band characteristics of droplet impact on the leaf surface, solving the problem of poor adaptability of fixed frequency bands, and significantly improving the sound signal extraction accuracy under different target crops and different preset parameter conditions. Furthermore, filtering the original spraying sound based on the target original spraying sound frequency band to obtain the actual original spraying sound frequency band allows for accurate acquisition of the corresponding target original spraying sound frequency band according to the type of target crop and the preset parameters of the spraying equipment. This achieves accurate extraction of the original spraying sound, improves extraction accuracy, and facilitates more accurate calculation of spraying effect evaluation indicators.

[0099] In other alternative implementations, interference such as wind noise and mechanical vibration noise can be eliminated by spectral subtraction, and endpoint detection can be performed to identify acoustic signal segments within the effective spraying period.

[0100] In one optional implementation, the impact sound includes a first impact sound and a second impact sound, wherein the first impact sound is the sound of the droplets reaching the top of the target crop, and the second impact sound is the sound of the droplets reaching the bottom of the target crop; the analysis results include a volume value (V), and the spraying effect evaluation index includes the droplet drift index (DI).

[0101] In this embodiment, the volume value is used to represent the sound intensity and can be a dimensionless digital sampled value. When using a miniature microphone array for sound acquisition, the volume value can be the digital volume value directly output by the microphone, with a corresponding volume range of 0~4095, where a larger number indicates a louder sound. The droplet drift index is used to represent how much of the liquid has drifted away and can be a dimensionless index with a corresponding value range of 0~1, where a smaller drift index indicates a better spraying effect.

[0102] like Figure 3 As shown, step S13 specifically includes:

[0103] S131. Analyze the filtered first impact sound and the filtered second impact sound respectively to obtain the corresponding first volume value and second volume value.

[0104] S132. Calculate the drift index based on the first volume value and the second volume value.

[0105] In this embodiment, the first impact sound is acquired by an acoustic sensor positioned above the target crop canopy, and the second impact sound is acquired by an acoustic sensor positioned below the target crop canopy. After filtering and time-frequency analysis, corresponding first and second volume values ​​are obtained. The drift index can be calculated based on the ratio of the second volume value to the first volume value. The calculation of the drift index allows wind-induced drift to be identified at an early stage, reducing non-target deposition and environmental pollution, which is beneficial to environmental safety and protection.

[0106] In a specific example, V2=819, V1=4095, DI=V2 / V1≈0.2. Here, V1 represents the first volume value (dimensionless); V2 represents the second volume value (dimensionless); and DI represents the drift index (dimensionless). The drift index DI effectively reflects the spraying effect; a small drift index indicates that the droplets are effectively sprayed onto the target crop.

[0107] In one optional embodiment, the impact sound includes a third impact sound and a fourth impact sound, wherein the third impact sound is the impact sound corresponding to the target crop located on the left side of the spraying device, and the fourth impact sound is the impact sound corresponding to the target crop located on the right side of the spraying device; the spraying effect evaluation index includes the Spray Uniformity Index (SUI).

[0108] In this embodiment, the uniformity index is used to indicate whether the spraying is uniform, and the corresponding value ranges from 0 to 1. When there is a strong wind, the uniformity index will change accordingly.

[0109] like Figure 4 As shown, step S13 specifically includes:

[0110] S133. Analyze the filtered third impact sound and the filtered fourth impact sound respectively to obtain the corresponding third volume value and fourth volume value.

[0111] S134. Calculate the uniformity index based on the third volume value and the fourth volume value.

[0112] In one specific implementation, the formula for calculating the uniformity index SUI can be: ;in, This indicates the third volume value. This indicates the fourth volume value. This represents the difference between the third and fourth volume values. This represents the average of the third and fourth volume values. A lower uniformity index indicates more even spraying.

[0113] In one optional implementation, the spraying effect evaluation index includes the energy ratio of the acoustic signal (ER).

[0114] like Figure 5 As shown, step S13 specifically includes:

[0115] S135. Analyze the filtered original spraying sound to obtain the corresponding fifth volume value;

[0116] S136. The sound signal energy ratio is calculated based on the first volume value and the fifth volume value.

[0117] In this embodiment, the sound signal energy ratio can be the ratio of the first volume value to the fifth volume value, and the corresponding calculation formula is ER=V1 / V5, where V1 represents the first volume value and V5 represents the fifth volume value. The larger the ER is, the better the spraying effect.

[0118] In one alternative implementation, such as Figure 6 As shown, the spraying effect evaluation method further includes:

[0119] S15. Obtain the changing trend of the drift index based on the drift index within a preset time period.

[0120] In this embodiment, the trend of the drift index can be used to indicate the change in wind strength in the environment; the greater the trend, the stronger the wind.

[0121] S16. In response to the change trend being greater than the change threshold, a strong wind alarm signal is issued.

[0122] In this embodiment, based on the real-time monitored trend of the drift index (DI), when the DI exceeds a threshold, it is determined to be a "strong wind impact" state, and a strong wind alarm signal is sent to the operating terminal as a warning. Since environmental disturbances such as strong winds significantly alter droplet transport and deposition distribution, calculating the drift index trend can effectively identify wind-induced drift and ultimately reflect it in the spraying effect evaluation results. In other embodiments, the spraying equipment can be automatically adjusted based on the strong wind alarm signal, such as reducing the spraying height, adjusting the nozzle direction, and increasing the droplet size. In other embodiments, the drift index (DI) trend can also be represented by color intensity in interactive interfaces such as work reports, providing accurate feedback on the spraying status.

[0123] In one alternative implementation, such as Figure 7 As shown, step S13 specifically includes:

[0124] S137. Perform time-domain analysis on the filtered impact sound to obtain the actual impact frequency of the fog droplets impacting the target crop.

[0125] In this embodiment, the actual impact frequency (N, Number) is used to represent the number of times the fog droplets hit the target crop leaf surface within 1 second, and the unit is times / second.

[0126] S138. Calculate the deposition density index based on the actual impact frequency and the standard impact frequency; wherein the standard impact frequency is associated with the type of the target crop and the preset parameters of the spraying equipment.

[0127] In this embodiment, the Droplet Deposition Index (DDI) is used to indicate the density of spraying. The DDI ranges from 0 to 1; a higher value indicates denser spraying and a better spraying effect. The formula for calculating the Droplet Deposition Index is as follows: ;in, This indicates the actual impact frequency, expressed in times per second. This indicates the standard impact frequency, measured in impacts per second. For example, when... ,but .

[0128] In one specific implementation, the spraying effect evaluation indicators include drift index, deposition density index, uniformity index, and acoustic signal energy ratio. The normal range, slightly abnormal range, severely abnormal range, and meaning of abnormality for each spraying effect evaluation indicator are shown in Table 1.

[0129] Table 1

[0130] DI (Drift Index) 0≤DI<0.2 0.2≤DI<0.4 DI≥0.4 Medicine liquid drifts away and is wasted DDI (Depositional Density Index) 0.7 ≤ DDI ≤ 1.0 0.5≤DDI<0.7 DDI<0.5 Spraying too diluted will result in poor control. SUI (Uniformity Index) 0.7≤SUI≤1.0 0.5≤SUI<0.7 SUI<0.5 Uneven spraying, with localized missed or oversprayed areas. ER (Energy Ratio of Acoustic Signal) 0.2≤ER≤0.6 0.1≤ER<0.2 IS<0.1 Low droplet transmission efficiency

[0131] In a specific implementation, the drift index, deposition density index, uniformity index, and acoustic signal energy ratio are combined and judged based on actual values ​​to generate corresponding alarm triggering rules. Through difference analysis of multiple indicators, the degree of drift can be quantitatively assessed. An automatic alarm is triggered when strong winds cause a decline in spraying quality, allowing operators to adjust operating parameters or suspend operations in a timely manner, avoiding large-scale ineffective spraying and pesticide waste. Therefore, compared with existing technologies that rely solely on wind speed sensors for qualitative judgment, the sound detection-based spraying effect evaluation method in this embodiment is more accurate and practically significant. Specifically, different colors of signal lights can reflect different spraying effects.

[0132] 1. A green light indicates normal spraying status, and the following conditions must be met simultaneously:

[0133] DI < 0.2;

[0134] DDI≥0.7;

[0135] SUI ≥ 0.7;

[0136] ER≥0.2.

[0137] If all of the above conditions are met, it indicates that the spraying effect is good and no adjustment is needed.

[0138] II. A yellow light indicates a mild abnormality. A yellow light warning will be issued if at least one of the following conditions is met:

[0139] 1. Mild drift: 0.2 ≤ DI < 0.4 and DDI ≥ 0.6;

[0140] 2. Slight underspray: 0.5 ≤ DDI < 0.7 and DI < 0.3;

[0141] 3. Slight non-uniformity: 0.5≤SUI<0.7 and DDI≥0.6;

[0142] 4. Mild transmission loss: 0.1≤ER<0.2 and DDI≥0.

[0143] III. A red light indicates a serious abnormality requiring automatic intervention. A red light alarm will be triggered if at least one of the following conditions is met.

[0144] 1. Severe drift warning (highest priority):

[0145] DI ≥ 0.4 or (DI ≥ 0.3 and DDI < 0.6);

[0146] Automatic intervention: Immediately reduce the spraying height to the minimum safe value and increase the droplet size. If the DI is still ≥0.4, suspend the operation.

[0147] 2. Severe underspray warning:

[0148] DDI < 0.5 and DI < 0.3;

[0149] Automatic intervention: Increase nozzle flow rate by 20% and reduce spraying equipment speed by 20%.

[0150] 3. Severe unevenness alarm:

[0151] SUI < 0.5 and DDI ≥ 0.5;

[0152] Automatic intervention: Check if the corresponding nozzle is blocked and send a nozzle abnormality alert to the operator.

[0153] 4. Nozzle clogging alarm:

[0154] ER < 0.1 and DDI < 0.3 and DI < 0.2;

[0155] Automatic intervention: Immediately stop the operation of the corresponding nozzle and send a blockage alarm to the operator.

[0156] In this embodiment, the evaluation results of different spraying effects were determined based on the collected sound signals, and the spraying effect was monitored in real time.

[0157] In one specific implementation method Figure 8 This is a flowchart of a specific method for evaluating spraying effectiveness based on sound detection.

[0158] Step 1: Multi-channel acoustic signal acquisition. Once the spraying process begins, multi-channel acoustic signal acquisition is performed, which can be implemented at the data acquisition layer. The microphone array includes: a microphone at the spray boom or arm end, a near-end microphone above the crop canopy, and a far-end reference microphone above the crop canopy. The microphone at the spray boom or arm end is used to acquire the raw spraying sound, the near-end microphone above the crop canopy is used to acquire the first impact sound, and the far-end reference microphone above the crop canopy is used to acquire the second impact sound for environmental noise reference and drift detection.

[0159] Step 2: Preprocessing of acoustic signals. The original acoustic signals collected are processed in the signal processing layer in the following steps to obtain the purified effective acoustic signals: (1) Bandpass filtering, the main function of which is to retain the characteristic frequency band of the droplets hitting the target crop leaf surface in the first impact sound; (2) Environmental background noise suppression, using adaptive filtering or spectral subtraction to eliminate interference such as wind noise and mechanical vibration noise; (3) Endpoint detection, to identify the acoustic signal segments within the effective spraying period.

[0160] Step 3: Time-Frequency Feature Extraction. The following acoustic features are extracted in the feature extraction layer: frequency band energy distribution features, impact event detection features, power spectral density features, and spectral centroid and bandwidth features. Specifically, it also includes an anomaly handling branch: if excessive noise is detected, the acquisition of the sound signal is paused; if signal loss is detected, a corresponding signal prompt is issued to alert the operator to check the spraying equipment; if an anomaly is detected in the time-frequency feature extraction, recalibration is performed.

[0161] Step 4: Multi-channel signal difference analysis. The acoustic signal energy ratio is calculated based on the acoustic signals collected by the microphone at the nozzle tip and the microphone near the crop canopy to analyze the energy attenuation and drift loss of droplets during their transmission from the nozzle to the leaf surface. The acoustic signal energy ratio is also calculated based on the acoustic signals from the microphone near the crop canopy and the reference microphone at the far end of the crop canopy to quantitatively assess the degree of droplet drift.

[0162] Step 5: Construct a quantitative evaluation model for spraying effectiveness. Based on the acoustic characteristics obtained in Step 3, output the following quantitative indicators, which may include the droplet deposition density index, the uniformity index of spray coverage, and the drift index. Steps 4 and 5 can be performed at the analysis and evaluation layer.

[0163] Step Six: Strong Wind Disturbance Identification and Alarm. The drift index is monitored in real-time at the decision-making and execution level. When the drift index exceeds a preset threshold, it is determined to be a "strong wind impact" state, an alarm is sent to the operation terminal, and the spraying system can be automatically linked to adjust parameters, such as reducing spray height, adjusting nozzle direction, and increasing droplet size. Alternatively, the spraying effect can be represented by color intensity in interactive interfaces such as work reports, providing realistic feedback on the spraying status, and finally outputting a work report.

[0164] In one specific implementation method Figure 9 This is a block diagram of a closed-loop control system for intelligent spraying operations, based on a specific sound detection-based spraying effect evaluation method. Primarily used in agricultural plant protection, such as drone and agricultural machinery pesticide spraying, it achieves end-to-end optimization from data acquisition to intelligent control. Its core objectives are to reduce pesticide drift and improve spray uniformity and operational efficiency.

[0165] 1. Main data flow (thick solid line): realizes the closed loop from signal to decision.

[0166] Data acquisition: Three types of microphone arrays respectively acquire raw acoustic signals from the near end, far end and nozzle end of the crop canopy. After synchronous and high-precision acquisition through a multi-channel acquisition card, the data is transmitted to the edge computing unit for preprocessing, such as filtering and noise reduction.

[0167] Feature extraction: The preprocessed acoustic signal is first processed by the time-frequency feature extraction module to calculate acoustic features such as power spectrum and spectral centroid, and then the multi-channel signal difference analysis module compares the energy and correlation of different channels to extract the difference features.

[0168] Effect evaluation: The differential characteristics are input into the spraying effect evaluation model to calculate three core indicators: DDI, SUI, and DI. At the same time, the DI index is simultaneously input into the wind disturbance identification module to determine the wind damage level and drift risk.

[0169] Automatic alarm: If the DI exceeds the threshold, the automatic linkage control system is triggered to send control commands to the spraying operation platform, such as adjusting the height of the spray boom and the direction of the nozzle, to achieve real-time correction; if the risk is serious, an alarm signal is sent to the operation terminal at the same time, triggering audible and visual prompts and thermal map warnings.

[0170] Data archiving and reporting: All data (signals, characteristics, evaluation results) during the operation are stored in a historical database and automatically generated into PDF reports through the operation report generation system, providing optimization suggestions. Through historical data archiving and report analysis, operation parameters can be optimized in a targeted manner, reducing pesticide waste, lowering the risk of pesticide residues, and improving the scientific nature of plant protection operations.

[0171] 2. Auxiliary flow / control flow (dashed thin line): Enables human-machine collaboration and platform interaction.

[0172] The human-interactive terminal allows users to manually adjust parameters, confirm alarms, and directly intervene in the automatic control process; from data acquisition and evaluation to automatic adjustment and alarms, it realizes the automation and intelligence of spraying operations, reducing human intervention.

[0173] The navigation and speed information of unmanned vehicles and agricultural machinery platforms, as well as external environmental data (wind speed, terrain), will be fed back to the evaluation model and control module to improve the accuracy of decision-making.

[0174] Historical data can be used to optimize models and update expert rule bases, forming a closed loop of continuous iteration.

[0175] Example 2

[0176] Corresponding to the aforementioned embodiment 1 of the spraying effect evaluation method based on sound detection, this disclosure also provides an embodiment of a spraying effect evaluation system based on sound detection.

[0177] Figure 10 This is a schematic diagram of a spraying effect evaluation system 20 based on sound detection provided in this embodiment. The spraying effect evaluation system 20 includes:

[0178] The sound acquisition module 201 is used to acquire the original spraying sound and the impact sound of the droplets reaching the target crop; wherein, the original spraying sound is the sound at the nozzle when the spraying equipment is spraying;

[0179] Filtering module 202 is used to filter the impact sound and the original spraying sound respectively;

[0180] The analysis and calculation module 203 is used to analyze the filtered impact sound and the filtered original spraying sound, and calculate the spraying effect evaluation index based on the analysis results.

[0181] The result determination module 204 is used to determine the spraying effect evaluation result based on the spraying effect evaluation index.

[0182] In this embodiment, by acquiring and filtering the original spraying sound from the nozzle and the impact sound of droplets reaching the target crop during spraying, the spraying effect can be evaluated based on sound signals. This method is low-cost and easy to deploy. The filtered impact sound and the filtered original spraying sound are analyzed, and spraying effect evaluation indicators are calculated based on the analysis results. The spraying effect evaluation result is determined based on these indicators. This improves the real-time performance and accuracy of spraying effect evaluation, providing more intuitive feedback results, thereby avoiding pesticide waste and reducing the risk of pesticide damage. Furthermore, monitoring based on acoustic signals is unaffected by light intensity, diurnal variations, and weather conditions, enabling continuous monitoring around the clock, overcoming the limitations imposed by light conditions on image detection and spectral detection methods in existing technologies.

[0183] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0184] Example 3

[0185] Figure 11 This is a schematic diagram of the structure of an electronic device shown in this embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the spraying effect evaluation method based on sound detection as described in Embodiment 1 above. Figure 11 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0186] like Figure 11 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0187] Bus 33 includes a data bus, an address bus, and a control bus.

[0188] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0189] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) program module 324, such program module 324 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0190] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the sound detection-based spraying effect evaluation method provided in Embodiment 1 above.

[0191] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 11 As shown, network adapter 36 communicates with other modules of electronic device 30 via bus 33. It should be understood that, although... Figure 11 Not shown, other hardware and / or software modules may be used in conjunction with electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0192] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0193] Example 4

[0194] This disclosure also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the sound detection-based spraying effect evaluation method provided in Embodiment 1 above.

[0195] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0196] Example 5

[0197] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the sound detection-based spraying effect evaluation method described in Embodiment 1 above.

[0198] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0199] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A method for evaluating spraying effect based on sound detection, characterized in that, The spraying effect evaluation method includes the following steps: Acquire the original spraying sound and the impact sound of the droplets reaching the target crop; wherein, the original spraying sound is the sound at the nozzle when the spraying equipment is spraying; The impact sound and the original spraying sound are filtered respectively; The filtered impact sound and the filtered original spraying sound were analyzed, and the spraying effect evaluation index was calculated based on the analysis results. The spraying effect evaluation result is determined based on the spraying effect evaluation indicators.

2. The spraying effect evaluation method as described in claim 1, characterized in that, The steps of filtering the impact sound and the original spraying sound respectively include: Based on the type of the target crop and the preset parameters of the spraying equipment, obtain the corresponding target impact sound frequency band and the target original spraying sound frequency band; The impact sound is filtered based on the target impact sound frequency band to obtain the actual impact sound frequency band; and the original spraying sound is filtered based on the target original spraying sound frequency band to obtain the actual original spraying sound frequency band.

3. The spraying effect evaluation method as described in claim 1, characterized in that, The impact sound includes a first impact sound and a second impact sound. The first impact sound is the sound of the droplets reaching the top of the target crop, and the second impact sound is the sound of the droplets reaching the bottom of the target crop. The analysis results include the volume value, and the spraying effect evaluation index includes the droplet drift index. The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include: The filtered first impact sound and the filtered second impact sound were analyzed separately to obtain the corresponding first volume value and second volume value. The drift index is calculated based on the first volume value and the second volume value; And / or, The impact sound includes a third impact sound and a fourth impact sound. The third impact sound is the impact sound corresponding to the target crop located on the left side of the spraying equipment, and the fourth impact sound is the impact sound corresponding to the target crop located on the right side of the spraying equipment. The spraying effect evaluation index includes the uniformity index. The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include: The filtered third and fourth impact sounds were analyzed separately to obtain the corresponding third and fourth volume values. The uniformity index is calculated based on the third volume value and the fourth volume value.

4. The spraying effect evaluation method as described in claim 3, characterized in that, The spraying effect evaluation index includes the sound signal energy ratio; The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include: The filtered original spraying sound was analyzed to obtain the corresponding fifth volume value; The sound signal energy ratio is calculated based on the first volume value and the fifth volume value.

5. The spraying effect evaluation method as described in claim 3, characterized in that, The spraying effect evaluation method also includes: The changing trend of the drift index is obtained based on the drift index within a preset time period; In response to the change trend exceeding the change threshold, a strong wind alarm signal is issued.

6. The spraying effect evaluation method according to any one of claims 1-5, characterized in that, The steps for analyzing the filtered impact sound and the filtered original spraying sound, and calculating the spraying effect evaluation index based on the analysis results, specifically include: Time-domain analysis of the filtered impact sound yields the actual impact frequency of the fog droplets impacting the target crop. The deposition density index is calculated based on the actual impact frequency and the standard impact frequency; wherein the standard impact frequency is associated with the type of the target crop and the preset parameters of the spraying equipment.

7. A spraying effect evaluation system based on sound detection, characterized in that, The spraying effect evaluation system includes: The sound acquisition module is used to acquire the original spraying sound and the impact sound of the droplets reaching the target crop; wherein, the original spraying sound is the sound at the nozzle when the spraying equipment is spraying; A filtering module is used to filter the impact sound and the original spraying sound respectively; The analysis and calculation module is used to analyze the filtered impact sound and the filtered original spraying sound, and calculate the spraying effect evaluation index based on the analysis results. The result determination module is used to determine the spraying effect evaluation result based on the spraying effect evaluation index.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the spraying effect evaluation method based on sound detection as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the spraying effect evaluation method based on sound detection as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the spraying effect evaluation method based on sound detection as described in any one of claims 1-6.

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