Multifunctional intelligent agricultural robot

Abstract

The invention relates to the technical field of agriculture, in particular to a multifunctional intelligent agricultural robot which comprises a data acquisition module, a data storage module, a data processing module and an operation execution module. The data acquisition module is used for acquiring crop growth data, crop environment data and crop pest data; the crop pest data comprises a pest image, a pest smell, a pest secretion image and a pest audio; the data processing module is used for judging the insect pest type according to the insect pest image, the insect pest smell and the insect pest audio, and judging the insect pest amount according to the insect pest smell concentration, the insect pest secretion amount and the noise volume of the insect pest audio; the data processing module is further used for comparing the insect pest smell concentration change value, the insect pest secretion amount change value and the insect pest audio noise volume change value in different time periods to judge the insect pest number change condition. According to the invention, the identification range and identification strength of pests can be effectively improved, and the protection effect on crops is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agriculture, and particularly to a multifunctional intelligent agricultural robot. Background Art

[0002] Modern agricultural projects have begun to apply robotics, computer technology, and wireless communication technology to facility agriculture, which can effectively improve the yield and quality of crops, and at the same time reduce the operating costs. It can be seen that researching agricultural robots is of great significance.

[0003] In modern agriculture, pests cause great harm to crops. Therefore, in the existing multifunctional intelligent agricultural robot technology, the agricultural robot can not only perform multiple functions such as crop irrigation, fruit picking, weeding and impurity removal for farmers, but also conduct pest supervision; when the existing agricultural robot conducts pest supervision, it uses image acquisition to identify the position of pests, and then performs pest control on the specified position; however, due to the dense and overlapping crop leaves, the existing agricultural robot in the prior art cannot identify the pests hidden behind the leaves, resulting in incomplete pest control and poor pest prevention effect.

[0004] Different pests have different odors, sounds, and secretions. For example, bed bugs secrete specific pheromones, carbon dioxide, methane and other chemical substances. By identifying the odor of pests, the presence and type of pests can be judged.

[0005] In summary, how to solve the problem that the existing agricultural robot in the prior art cannot identify the pests hidden behind the leaves, resulting in incomplete pest control and poor pest prevention effect has become a difficult problem that needs to be solved urgently in the current field. Therefore, it is necessary to propose a multifunctional intelligent agricultural robot that can identify pests more comprehensively. Summary of the Invention

[0006] To solve the above problems, the present invention provides a multifunctional intelligent agricultural robot. Through the design of the data processing module, while detecting the growth situation of crops, it can also analyze the pest situation of crops based on various information of pests, so as to better analyze the pest situation of crops, and then achieve more comprehensive pest prevention and control of crops.

[0007] To achieve the above object, the technical solution of the present invention is as follows: A multifunctional intelligent agricultural robot includes a data acquisition module, a data storage module, a data processing module, and an operation execution module.

[0008] A data acquisition module, which is used to collect crop growth data, crop environment data and crop pest data; and transmit the collected crop growth data, crop environment data and crop pest data to the data storage module; the crop pest data includes pest images, pest odors, pest secretion images and pest audio.

[0009] A data storage module, which is used to receive and store the crop growth data, crop environment data and crop pest data collected by the data acquisition module, and provide the data processing module with the ability to query the crop growth data, crop environment data and crop pest data.

[0010] A data processing module, which is used to integrate and transmit the crop growth data, crop environment data and crop pest data, compare the crop growth data, crop environment data and crop pest data in different time periods, judge the growth situation of the crops according to the comparison results of the crop growth data, crop environment data and crop pest data, and issue an execution instruction to the operation execution module according to the growth situation.

[0011] The data processing module is also used to judge the pest species according to the pest images, pest odors and pest audio, and judge the pest quantity according to the pest odor concentration, pest secretion amount and noise volume of the pest audio.

[0012] An operation execution module, which is used to receive the execution instruction from the data processing module, move according to the travel information included in the execution instruction, and perform crop irrigation, pesticide spraying, weeding and crop harvesting according to the crop processing information.

[0013] Furthermore, the data processing module compares the most recent crop environment data and crop growth data with the crop environment data and crop growth data 2 - 3 hours ago every 2 - 3 hours, and issues an execution instruction for regulating the crop environment to the operation execution module according to the changes in air temperature, air humidity and soil pH, combined with the growth stage, plant height and crop yield of the crops.

[0014] Furthermore, the data processing module collects crop pest data once every 4 - 6 hours, compares the most recent crop pest data with the crop pest data collected 4 - 6 hours ago, judges the change in pest quantity according to the change value of pest odor concentration, the change value of pest secretion amount and the change value of noise volume of the pest audio, and at the same time issues an execution instruction for pest control to the operation execution module based on this.

[0015] Furthermore, the data acquisition module is also used to monitor the movement trajectories of animals in the planting area to determine whether the animals enter the crop planting area; when the data acquisition module detects that an animal enters the crop planting area, it records the images in this area, moves to this area, and determines whether the crops are damaged. If the crops are damaged, the data acquisition module issues an alarm.

[0016] Furthermore, the data processing module is constructed based on a deep learning model, and the deep learning model is obtained by training based on a number of pest images, pest audio, pest secretion images, and pest odor information.

[0017] The technical principle of the above solution is as follows:

[0018] The data acquisition module collects crop growth data, crop environment data, and crop pest data, and transmits the collected crop growth data, crop environment data, and crop pest data to the data storage module for storage. The operator views the crop growth data, crop environment data, and crop pest data stored in the data storage module through the data processing module, and then judges the growth status and pest situation of the crops.

[0019] When analyzing the pest situation, the data processing module will judge the pest type based on pest images, pest odors, and pest audio, and judge the pest quantity based on the pest odor concentration, pest secretion volume, and noise volume of the pest audio; the data processing module is also used to compare the change values of pest odor concentration, pest secretion volume, and pest audio noise volume in different time periods to judge the change situation of the pest quantity, and then issue corresponding execution instructions to the operation execution module. The operation execution module will control the intelligent agricultural robot to carry out targeted pest control and extermination according to the execution instructions issued by the data processing module.

[0020] The above solution has the following beneficial effects:

[0021] 1. In the present invention, the data acquisition module can comprehensively collect the growth data and environment data of crops, including the crop growth stage, growth height, crop yield, air temperature, air humidity, and soil acidity and alkalinity, which is convenient for the operator to master the growth situation of the crops and adjust the growth environment of the crops; in addition, the data acquisition module also collects pest images, pest odors, pest secretion images, and pest audio, etc., providing a rich information basis for the operator to judge the pest type, pest quantity, and pest quantity change, enabling the operator to better carry out pest control and extermination work in agricultural management, and thus better protecting the crops from the influence of pests and improving economic benefits.

[0022] 2. Compared with the existing pest control technologies, in addition to providing images of pests, the present invention also collects the odors, secretions, and audio of pests, thereby more comprehensively identifying the types, quantities, and changes of pests, enabling the pest prevention and control work to be more comprehensive and efficient, improving the scope and intensity of pest identification, and thus achieving better pest prevention and control effects.

[0023] 3. Through the design of the data processing module, the operator can remotely understand and control the situation of the crops and their growth environment, regulate the growth environment of the crops, thereby improving the growth state of the crops. Through the intelligent design, the labor cost is greatly reduced, the operation efficiency and quality are improved, and thus the economic benefits are increased.

[0024] Further, the operation execution module includes a mobile vehicle, on the top of which a robotic arm is fixedly connected, and at the top of the robotic arm, a robotic claw and a high-definition camera are fixedly connected.

[0025] Beneficial effects: The mobile vehicle enables the whole device to move freely in the crop environment; the robotic arm cooperates with the robotic claw to clean the weeds and sundries in the field and pick the mature crops or fruits; the high-definition camera provides a basis for image acquisition by the data acquisition module.

[0026] Further, an irrigation tank for irrigation is also fixedly connected to the top of the robotic arm.

[0027] Beneficial effects: The irrigation tank is used for watering, fertilizing, and spraying pesticides on the crops.

[0028] Further, a plurality of lighting lamps are fixedly connected to the side wall of the mobile vehicle; when the data processing module detects a situation of human damage, the data processing module will issue an execution command to the operation execution module, and the operation execution module will control the lighting lamps to flash.

[0029] Beneficial effects: The lighting lamps can provide a good light environment when the high-definition camera performs image acquisition; at the same time, when the operator or the data processing module detects other abnormal situations such as human damage, the warning can be given by flashing the lighting lamps.

[0030] Further, an air path system for purging the plants is also provided at the top of the robotic arm.

[0031] Beneficial effects: When the high-definition camera performs image acquisition, if the data acquisition module identifies a situation where the leaves overlap and block each other, the air path system can blow the leaves away, enabling the high-definition camera to take more comprehensive pictures, thereby identifying the pests blocked by the leaves. At the same time, when spraying pesticides, the air path system can also blow the leaves away, enabling the pesticides to be sprayed to the positions where the pests are located, and thus achieving better pest prevention and control effects.

[0032] Further, a temperature and humidity sensor, an odor sensor, and an audio sensor are fixedly connected to the mobile vehicle.

[0033] Beneficial effects: The temperature and humidity sensor is used to collect the air temperature and humidity in the crop environment; the odor sensor is used to identify the gas components and their concentrations in the crop environment, and the audio sensor is used to collect the sounds in the crop environment.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. Description of the Drawings

[0035] Figure 1 It is a structural diagram of a multifunctional intelligent agricultural robot in an embodiment of the present invention.

[0036] Figure 2 It is an axonometric view of the intelligent agricultural robot in an embodiment of the present invention.

[0037] Reference numerals in the accompanying drawings of the specification include: 1, mobile vehicle; 2, robotic arm; 3, irrigation tank; 4, robotic claw; 5, high-definition camera; 6, lighting lamp. Detailed Embodiments

[0038] The following is a further detailed description through specific embodiments:

[0039] Embodiment 1:

[0040] As shown in the attached Figure 1 figures: A multifunctional intelligent agricultural robot includes a data acquisition module for collecting various types of crop data, a data storage module for storing various types of crop data, a data processing module for remotely regulating the crop environment, and an operation execution module for performing various operations. The data acquisition module, the data storage module, the data processing module, and the operation execution module are interconnected by signals.

[0041] The specific functions of each module are as follows:

[0042] The data acquisition module is used to collect crop growth data, crop environment data, and crop pest data; and transmit the collected crop growth data, crop environment data, and crop pest data to the data storage module; wherein, the crop growth data includes the crop growth stage, crop height, and crop yield; the crop environment data includes air temperature, air humidity, and soil acidity; the crop pest data includes pest images, pest odors, pest secretion images, and pest audio.

[0043] In this embodiment, the data acquisition module mainly uses image acquisition equipment, combines image recognition technology and image processing technology to identify and process pest images.

[0044] In this embodiment, the data acquisition module mainly uses gas monitoring equipment to collect and analyze the gases secreted by pests. Gas monitoring equipment is arranged in areas where pests may exist to ensure that the equipment can fully come into contact with the gases released by pests. Gas samples are collected at set time intervals or continuously to ensure capturing real-time information on pest activities.

[0045] In this embodiment, the data acquisition module mainly uses a sound recording device to collect the audio of pests, and then processes the audio of pests based on audio processing technology. The sound recording device is used to collect audio data of pest activities continuously or at regular intervals. During the collection process, key information such as the collection time and location of the audio data should be noted. The collected audio data is denoised to reduce the impact of environmental noise on the analysis results. Then, audio analysis technology is used to extract characteristic parameters of pest audio, such as frequency, amplitude, and waveform, etc. At the same time, pre-emphasis processing is performed on the audio signal to highlight the high-frequency components in the audio signal and improve the analysis accuracy.

[0046] This embodiment takes the crops in a certain greenhouse as an example. For example, the data acquisition module collects images of tomatoes in the greenhouse and identifies that the height of the tomatoes in the greenhouse is 2m, the fruits are fully mature, and the plants have reached the mature stage. At the same time, when the data acquisition module collects images of pests, no pests or abnormal secretions are identified in the images. However, when detecting the odor of pests, a specific pheromone and a small amount of methane are collected. At this time, the data acquisition module will transmit the captured tomato images and the components and concentrations of the collected odors to the data storage module for storage.

[0047] The data storage module is used to receive and store the crop growth data, crop environment data, and crop pest data collected by the data acquisition module, and provide them for the data processing module to query the crop growth data, crop environment data, and crop pest data. In this embodiment, the data acquisition module mainly uses a data storage device to store various types of data. In this embodiment, the data storage device is selected as a memory.

[0048] Taking the above tomatoes as an example, the data storage module will store the captured tomato images and the components and concentrations of the collected odors transmitted by the data acquisition module, for subsequent operators to observe and analyze the data through the data processing module.

[0049] The data processing module is used to integrate and transmit crop growth data, crop environment data, and crop pest data in real time, compare the crop growth data, crop environment data, and crop pest data at different time periods, judge the growth condition of the crops according to the comparison results of the crop growth data, crop environment data, and crop pest data, and issue an execution instruction to the operation execution module according to the growth condition. The data processing module in this embodiment is mainly constructed based on artificial intelligence technologies such as machine learning technology and deep learning technology. In this embodiment, the data processing module is mainly constructed based on deep learning technology, uses deep learning technology to construct a crop growth model, and trains the crop growth model with a large amount of crop growth data, crop environment data, and crop pest data, so that the data processing module can automatically analyze the growth trend of the crops, environmental changes, and the occurrence of pest infestations according to the crop growth data, crop environment data, and crop pest data.

[0050] The data processing module compares the most recent crop environment data and crop growth data with the crop environment data and crop growth data three hours ago every three hours, and issues an execution instruction for regulating the crop environment to the operation execution module according to the changes in air temperature, air humidity, and soil acidity and alkalinity, combined with the growth stage of the crops, plant height, and crop yield.

[0051] For example, the data processing module compares and finds that the tomato plants stood upright and the leaves were stretched three hours ago, but the leaves were bent and drooped three hours later, and the indoor temperature increased by 5°C compared with three hours ago. At this time, the data processing module issues an execution instruction "reduce the indoor temperature by 5°C" to the operation execution module.

[0052] The data processing module is constructed based on a deep learning model, and the deep learning model is obtained by training based on a number of pest images, pest audio, pest secretion images, and pest odor information. The data processing module collects crop pest data once every six hours, compares the most recent crop pest data with the crop pest data collected six hours ago, judges the change in the number of pests according to the change value of the pest odor concentration, the change value of the pest secretion amount, and the change value of the noise volume of the pest audio, and at the same time issues an execution instruction for pest control to the operation execution module based on this.

[0053] Taking the above tomato as an example, for instance, 6 hours ago the tomato plant had intact leaves. After the operator viewed and compared the images of the tomato in the recent 6 hours through the data processing module, it was found that the tomato leaves were gradually incomplete, and the incomplete parts were all crescent-shaped. During this process, the deep learning model would analyze the type of pest based on the images of the leaves and the components in the pest odor collected by the data collection module. Since the pest odor contained specific pheromones of bed bugs and a small amount of methane, the deep learning model determined that the pest was a bed bug. At the same time, based on the low content of bed bug pheromones and methane, the deep learning model judged that the number of bed bugs was less than 10. At this time, the operator could issue corresponding execution instructions to the operation execution module through the data processing module to spray pesticides at the specified location.

[0054] The operation execution module includes an intelligent agricultural robot. The operation execution module is used to receive the execution instructions from the data processing module and control the intelligent agricultural robot to move, irrigate crops, spray pesticides, remove weeds and pests, and pick crops according to the execution instructions.

[0055] Taking the above tomato as an example, when the operation execution module receives the execution instruction to spray pesticides at the specified location, the operation execution module will control the intelligent agricultural robot to spray pesticides on the plant to kill pests.

[0056] The data processing module is also used to compare the change values of pest odor concentration, pest secretion volume, and the noise volume change value of pest audio at different time periods to judge the change situation of pest quantity.

[0057] Taking the above tomato as an example, for instance, on the day after the tomato plant was sprayed with pesticides by the intelligent agricultural robot, the data collection module collected images and pest odors of the tomato plant again. No pests or abnormal secretions were found in the collected images, no new gnawing marks appeared on the tomato leaves, and no abnormal pheromones, methane and other gases were identified in the air. The deep learning module analyzed again based on the above information, judged that the pest problem had been solved, and sent a prompt to the operator. The operator could view the images collected on-site and the analysis results of the deep learning module through the data processing module.

[0058] The data acquisition module is also used to monitor the movement trajectories of animals in the planting area and determine whether the animals enter the crop planting area. When the data acquisition module detects that an animal enters the crop planting area, it records the images in this area, moves to this area, and determines whether the crops are damaged. If the crops are damaged, the data acquisition module issues an alarm. In this embodiment, the data acquisition module uses various image acquisition devices to monitor the movement trajectories of animals in the planting area and the crop images and videos, such as high-definition cameras, infrared cameras, and thermal imaging cameras. In this embodiment, a high-definition camera is mainly selected. The data acquisition module then uses a storage device to store the acquired images and videos, and then uses an alarm device to issue an alarm for abnormal situations. In this embodiment, an alarm is mainly selected for alarming.

[0059] Through the data acquisition module, the present invention can comprehensively collect the growth data and environmental data of crops, including the crop growth stage, growth height, crop yield, air temperature, air humidity, and soil acidity and alkalinity, enabling the operator to remotely grasp the growth situation of crops and adjust the crop growth environment. In addition, the data acquisition module will also collect pest images, pest odors, pest secretion images, and pest audio, etc., and combine with a deep learning model to provide a rich information basis for the operator to judge the pest type, pest quantity, and the change of pest quantity, enabling the operator to better carry out pest prevention and control work in agricultural management, and thus better protect the crops from the influence of pests and improve economic benefits.

[0060] Compared with the existing pest control technologies, in addition to providing the collection of pest images, the present invention also collects the odors, secretions, and audio of pests, and thus more comprehensively identifies the pest type, pest quantity, and pest change situation, enabling the pest prevention and control work to be more comprehensive and efficient, improving the identification range and intensity of pests, and thus achieving a better pest prevention and control effect.

[0061] Embodiment 2:

[0062] As shown in the Figure 2 attachment: Different from the above embodiment, the intelligent agricultural robot includes a mobile vehicle 1. A robotic arm 2 is fixedly connected to the top of the mobile vehicle 1 by bolts. A robotic claw 4 and a high-definition camera 5 are fixedly connected to the top end of the robotic arm 2 by bolts. An irrigation tank 3 for irrigation is also welded to the top end of the robotic arm 2. A water pump is fixedly communicated with the front end of the irrigation tank 3. The operation execution module is used to control the opening and closing of the water pump.

[0063] The specific implementation process is as follows: The mobile vehicle 1 enables the entire device to move freely in the crop environment, thereby comprehensively regulating the plants in the crop environment; the robotic arm 2 cooperates with the robotic claw 4 to clean weeds and sundries in the field and pick ripe crops or fruits; the high-definition camera 5 provides a basis for the data acquisition module to collect images. The irrigation tank 3 is used to water, fertilize, and spray pesticides on the crops.

[0064] Embodiment 3:

[0065] As shown in the Figure 1 and Figure 2 figure: Different from the above embodiment, several lighting lamps 6 are fixedly connected to the side wall of the mobile vehicle 1 by bolts; when the data processing module detects a situation of human damage, the data processing module will issue an execution command to the operation execution module, and the operation execution module will control the lighting lamps 6 to flash.

[0066] The specific implementation process is as follows: The lighting lamps 6 can provide a good light environment when the high-definition camera 5 collects images; at the same time, when the operator provides data and the data processing module detects other abnormal situations such as human damage, the lighting lamps 6 can be flashed for warning.

[0067] Embodiment 4:

[0068] As shown in the Figure 2 figure: Different from the above embodiment, an air path system for blowing the plants is also provided at the top of the robotic arm 2. In this embodiment, the air path system selects a hair dryer in the prior art.

[0069] The specific implementation process is as follows: When the high-definition camera 5 collects images, if the data acquisition module identifies a situation where there are overlapping leaves and mutual occlusion, the data acquisition module will control the hair dryer to blow the leaves away, so that the high-definition camera 5 can take more comprehensive pictures, and then identify the occluded pests. At the same time, when spraying pesticides, the data acquisition module can also control the hair dryer to blow the leaves away, so that the pesticides can be sprayed to the position where the pests are located, thereby achieving a better effect of preventing and controlling pests.

[0070] Embodiment 5:

[0071] As shown in the Figure 2 figure: Different from the above embodiment, a temperature and humidity sensor, an odor sensor, and an audio sensor are fixedly connected to the mobile vehicle 1 by screws.

[0072] The specific implementation process is as follows: The temperature and humidity sensor is used to collect the air temperature and humidity in the crop environment; the odor sensor is used to identify the gas components and their concentrations in the crop environment, and the audio sensor is used to collect the sounds in the crop environment.

[0073] Obviously, the above embodiments are merely examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or alterations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or alterations derived therefrom still fall within the protection scope of the present invention.

Claims

1. A multifunctional intelligent agricultural robot, characterized in that: It includes a data acquisition module, a data storage module, a data processing module and an operation execution module; The data collection module is used to collect crop growth data, crop environment data and crop pest data; and transmit the collected crop growth data, crop environment data and crop pest data to the data storage module; the crop pest data includes pest images, pest odors, pest secretion images and pest audio; The data storage module is used to receive and store the crop growth data, crop environment data and crop pest data collected by the data collection module, and to provide the data processing module with the opportunity to query the crop growth data, crop environment data and crop pest data; The data processing module is used to integrate and transmit crop growth data, crop environment data and crop pest data in real time, and compare the crop growth data, crop environment data and crop pest data of different time periods, judge the growth status of crops according to the comparison results of the crop growth data, crop environment data and crop pest data, and issue execution instructions to the operation execution module according to the growth status; The data processing module is also used to determine the type of pests based on the pest image, pest smell and pest audio, and determine the amount of pests based on the concentration of pest smell, the amount of pest secretions and the noise volume of pest audio; The operation execution module is used to receive the execution instruction of the data processing module, move according to the travel information contained in the execution instruction, and perform crop irrigation, pesticide spraying, weeding and crop picking according to the crop processing information.

2. The multifunctional intelligent agricultural robot according to claim 1, characterized in that: The data processing module compares the most recent crop environment data and crop growth data with the crop environment data and crop growth data 2-3 hours ago every 2-3 hours, and issues execution instructions for regulating the crop environment to the operation execution module based on changes in air temperature, air humidity and soil pH, combined with the crop growth stage, plant height and crop yield.

3. The multifunctional intelligent agricultural robot according to claim 2, characterized in that: The data processing module will collect crop pest data every 4-6 hours, and compare the most recent crop pest data with the crop pest data collected 4-6 hours ago. It will judge the change in the number of pests based on the change in the concentration of pest odor, the amount of pest secretions and the noise volume of pest audio, and use this as a basis to issue execution instructions on pest prevention and control to the operation execution module.

4. The multifunctional intelligent agricultural robot according to claim 3, characterized in that: The data acquisition module is also used to monitor the movement trajectory of animals in the planting area and determine whether the animals have entered the crop planting area. When the data acquisition module detects that an animal has entered the crop planting area, it records the image in the area and moves to the area to determine whether the crops are damaged. If the crops are damaged, the data acquisition module will sound an alarm.

5. The multifunctional intelligent agricultural robot according to claim 4, characterized in that: The data processing module is built based on a deep learning model, which is trained based on a number of pest images, pest audio, pest secretion images and pest odor information.

6. The multifunctional intelligent agricultural robot according to claim 5, characterized in that: The operation execution module comprises a mobile vehicle (1), the top of the mobile vehicle (1) is fixedly connected to a mechanical arm (2), and the top of the mechanical arm (2) is fixedly connected to a mechanical claw (4) and a high-definition camera (5).

7. The multifunctional intelligent agricultural robot according to claim 6, characterized in that: The top of the mechanical arm (2) is also fixedly connected with an irrigation box (3) for irrigation.

8. The multifunctional intelligent agricultural robot according to claim 7, characterized in that: A plurality of lighting lamps (6) are fixedly connected to the side wall of the mobile vehicle (1); when the data processing module finds human damage, the data processing module will send an execution command to the operation execution module, and the operation execution module will control the lighting lamps (6) to flash.

9. The multifunctional intelligent agricultural robot according to claim 8, characterized in that: The top of the mechanical arm (2) is also provided with an air path system for blowing the plants.

10. The multifunctional intelligent agricultural robot according to claim 9, characterized in that: The mobile vehicle (1) is fixedly connected with a temperature and humidity sensor, an odor sensor and an audio sensor.

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