Aircraft Control System

Abstract

To make a plan for application of chemicals with a high degree of accuracy.SOLUTION: A flight body control system for controlling a flight body 1 for spraying an agent in a field, comprises a quality information acquiring unit 11 for acquiring quality information 18 relating to the measured quality from a quality measuring machine 15 for measuring the quality of harvested agricultural products in a predetermined area of the field by linking the quality information 18 to the area, and a plan calculation unit 12 for calculating a spray plan 27 for the agent and a flight plan 28 of the flight body 1 based on the quality information 18 and the area, and the flight body 1 applies the agent to the field based on the spray plan 27 and the flight plan 28.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a control system for an aircraft that sprays pesticides on agricultural crops. [Background technology]

[0002] In farm fields, various pesticides are sprayed to grow crops properly. As shown in Patent Document 1, pesticides are sometimes sprayed from the air using an aircraft such as a drone.

[0003] In Patent Document 1, the drone sprays pesticides based on a work plan set based on previously input pesticide identification information such as the amount of pesticide to be sprayed, the name of the pesticide, and the product number. The type of pesticide to be sprayed (name, product number) and the amount of pesticide to be sprayed can be uniform throughout the entire field, but can also be different for each specified section or ridge / row. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-40598 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the pesticide spraying plan, which includes the type of pesticide to be sprayed and the amount of pesticide to be sprayed, is determined based on the worker's experience, taking into consideration the type of pesticide and the amount of pesticide sprayed in the past, and, if possible, the yield and quality of the past crops. Therefore, it is difficult to determine the pesticide spraying plan with high accuracy.

[0006] In this case, it is difficult to take into account the cost of spraying pesticides and the flight costs of drones and other aerial vehicles, so even if the harvest volume or quality improves as a result of spraying pesticides, the increase in crop sales (profits) may be smaller than the increased costs of spraying pesticides and flying the aerial vehicle.

[0007] Therefore, there is a demand for more accurate drug spraying plans.

[0008] An object of the present invention is to develop a drug spraying plan with high accuracy. [Means for solving the problem]

[0009] In order to achieve the above-mentioned objective, an aircraft control system according to one embodiment of the present invention is an aircraft control system that controls an aircraft that sprays pesticides in a field, and is equipped with a quality information acquisition unit that acquires quality information regarding the measured quality of crops harvested in a specified area of ​​the field from a quality measuring device that measures the quality of the crops, linking the measured quality to the area, and a plan calculation unit that calculates a spraying plan for the pesticide and a flight plan for the aircraft based on the quality information and the area, and the aircraft sprays the pesticide in the field based on the spraying plan and flight plan.

[0010] With this configuration, it is possible to calculate a pesticide spray plan for each area by linking and considering quality information related to the quality of previously harvested agricultural crops and the area in which the crops were harvested, and further to calculate a flight plan for the aircraft. Therefore, it is possible to create a plan for spraying pesticides with high accuracy based on past pesticide spray results, and to spray pesticides in a way that improves the quality and yield of the harvested agricultural crops.

[0011] In addition, it is preferable that the plan calculation unit calculates the spraying plan and the flight plan by taking into account at least one of the price of the agricultural products for each quality, the spraying cost of the pesticide, and the flight cost of the aircraft.

[0012] It is expected that spraying pesticides on agricultural crops will improve the yield or quality of the crops, and as a result, increase profits. However, spraying pesticides using an aircraft requires costs for spraying the pesticides and flying the aircraft, which reduces the increase in profits.

[0013] Therefore, by taking into account the price of each quality of crop, the cost of spraying the pesticide, and the flight cost of the aircraft when calculating the spraying plan and flight plan, it is possible to calculate a pesticide spraying plan and an aircraft flight plan that are likely to result in increased profits, and to devise a more accurate plan for spraying pesticide that is likely to increase profits.

[0014] In addition, the agricultural product may be rice, the color sorter may sort the rice kernels into one of a number of pre-set product levels, and the quality may include the product level of the kernels and the proportion of the amount of the kernels sorted into each product level relative to the total amount.

[0015] With this configuration, it is possible to take into account the proportion of each product level present, allowing for more accurate planning of spraying of pesticides.

[0016] The quality measuring machine may also be a color sorter.

[0017] With this configuration, the quality of agricultural products can be easily measured.

[0018] It is also preferable that the spraying plan includes at least one of the type of pesticide to be sprayed, the amount of the pesticide to be sprayed, and the number of times the pesticide is sprayed, and that the flight plan includes a flight route.

[0019] With this configuration, it is possible to create accurate dispersion plans and flight plans that fully incorporate all necessary information.

[0020] Furthermore, the area may be each section into which the field is divided, or each ridge or row of the field, and the quality information may be information on the quality measured for each section, each ridge, or each row.

[0021] With this configuration, spraying plans and flight plans can be formulated for each finer area, enabling spraying plans and flight plans to be formulated with greater accuracy.

[0022] The device may further include a memory unit, which stores a spraying management table in which the spraying plan applied to the harvested crop and the quality information of the harvested crop are linked to the area each time the crop is harvested, and the plan calculation unit may calculate the flight plan by referring to the spraying management table and referring to the progress of the quality information of the crop harvested in the past and the spraying plan for each area.

[0023] This configuration makes it possible to refer to the relationship between the pesticide spray status recorded in the spray management table (the spray status based on the pesticide spray plan) and the quality information of the crops harvested as a result of the pesticide spraying. As a result, by referring to the progress of the pesticide spray status and the quality information, it is possible to create more accurate spray plans and flight plans that reflect the actual pesticide spray results. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a diagram illustrating a configuration for spraying pesticides using a drone. [Diagram 2] FIG. 2 is a diagram illustrating functional blocks of the drone control system. [Diagram 3] FIG. 13 is a diagram illustrating an example of a spraying management table. [Figure 4] FIG. 1 is a diagram illustrating an example of an operation flow of the drone control system. [Diagram 5] FIG. 1 is a diagram illustrating an example of a plot of a farm field. [Figure 6] FIG. 1 is a diagram illustrating a configuration for measuring rice quality using a color sorter. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] [Spraying of drugs] When growing crops in field F, various pesticides are sprayed on the crops in field F in order to increase the crop yield and improve the quality of the harvested crops.

[0026] There are various types of pesticides, and fertilizers are used to increase harvest yields, while pesticides are sprayed according to the disease or pest to suppress disease and pest damage in agricultural crops and improve the yield of harvested products (crops) of appropriate quality. In addition, pesticides must be sprayed in the appropriate amount, at the appropriate number of times, and at the appropriate time.

[0027] Also, as shown in FIG. 1, the pesticide may be sprayed from the air onto crops 5, such as rice, in a field F, using a drone 1 (corresponding to an "aircraft"). The drone 1 is equipped with a tank 2 that stores the pesticide, and a spraying nozzle 3 that sprays the stored pesticide. The drone 1 sprays a predetermined amount of pesticide as it flies in a scanning manner above the field F. Also, if it becomes necessary to replenish the pesticide during spraying, the drone 1 lands at a predetermined parking area 7 set up near the field F, and is replenished with the pesticide from a supply tank 8.

[0028] The chemicals are sprayed based on a spraying plan 27 (see FIG. 2) in order to increase the harvest amount or improve the yield of crops of appropriate quality. The spraying plan 27 includes at least one of an appropriate type of chemical, an appropriate spraying amount, an appropriate number of sprays, an appropriate spraying time, and the like, and is determined based on the quality of crops harvested in the past. In addition, the spraying plan 27 is determined for each field F or for each predetermined area within the field F.

[0029] [Drone control system] Next, a drone control system that determines a drug spraying plan 27 using a drone 1 will be described with reference to Figs. 1 to 3.

[0030] The quality of the harvested products (agricultural products) is measured by a quality measuring device such as a color sorter 15. The color sorter 15 (quality measuring device) measures the quality of each harvested product harvested in the field F where the harvested products were harvested or in a specified area of ​​the field F. Furthermore, the harvested products whose quality is measured are provided with location information 17 that identifies the location of the field F or the area. The color sorter 15 (quality measuring device) then links the location information 17 to output quality information 18, which is the result of the quality measurement.

[0031] The quality included in the quality information 18 is any quality, such as the yield (weight), moisture content, size, whether defective or non-defective, of the harvested product, depending on the quality measuring machine. A quality measuring machine such as the color sorter 15 judges whether the harvested product is good or defective, and separates the harvested products into good and defective products and discharges them. Defects include, for example, damage by pests such as stink bugs, and in the case of rice, discolored rice and white rice. For example, the color sorter 15 can identify rice damaged by stink bugs, discolored rice, white rice, etc. as defective.

[0032] Furthermore, quality measuring machines such as color sorter 15 record the amount of defects or the ratio of defects to the total amount of the harvested product whose quality was measured as quality information 18, linking it to location information 17 for each type of defect. For example, quality measuring machines such as color sorter 15 record the amount or ratio of rice damaged by stink bugs, the amount or ratio of discolored rice, and the amount or ratio of white rice, linking them to location information 17.

[0033] The drone control system illustrated in Figure 2 includes a control unit 10 having a processor such as a CPU, and the control unit 10 includes a quality information acquisition unit 11, a plan calculation unit 12, and a memory unit 13.

[0034] The quality information acquiring unit 11 acquires quality information 18 measured by a quality measuring device such as a color sorter 15 and location information 17. The quality information acquiring unit 11 stores the quality information 18 and the location information 17 in the storage unit 13 in association with each other.

[0035] The plan calculation unit 12 calculates a spraying plan 27 and a flight plan 28 based on the spraying management table 21, taking into account at least one of the current purchase price 23, spraying cost 24, and flight cost 25.

[0036] Specifically, when the yield (weight) of the harvested crop recorded in the spraying management table 21 is less than a predetermined yield, the plan calculation unit 12 selects fertilizer as the chemical, and calculates a spraying plan 27 and a flight plan 28 in accordance with the degree of yield deficiency, linking them to the location information 17, while taking into account at least one of the current purchase price 23, spraying cost 24, and flight cost 25. In addition, the plan calculation unit 12 compares the moisture content and size recorded in the spraying management table 21 with predetermined values, and calculates a spraying plan 27 and a flight plan 28 in accordance with the comparison result, linking them to the location information 17, while taking into account at least one of the current purchase price 23, spraying cost 24, and flight cost 25. In addition, when a defect such as stink bug damage is recorded in the spraying management table 21, the plan calculation unit 12 calculates a spraying plan 27 and a flight plan 28 in accordance with the content and extent of the defect, linking them to the location information 17, while taking into account at least one of the current purchase price 23, spraying cost 24, and flight cost 25.

[0037] Then, the plan calculation unit 12 estimates the sales revenue for the harvest when the pesticide is sprayed and when it is not sprayed from prices such as the purchase price 23, compares these with the total spraying cost 24 and flight cost 25, searches for the spraying plan 27 that will maximize profits, and calculates a flight plan 28.

[0038] In addition, the plan calculation unit 12 may calculate the dispersion plan 27 and the flight plan 28 using machine learning technology based on artificial intelligence.

[0039] The spraying plan 27 includes at least any of the type of pesticide to be sprayed, the amount of pesticide to be sprayed, the number of applications, the time of application, etc. for each field F or area. The flight plan 28 includes at least a flight path, and the flight path may include, in addition to the flight route when spraying pesticide, movement of the field F or area, movement for replenishing pesticide, etc.

[0040] The purchase price 23 is not limited to the purchase price of the agricultural products, and may be various prices such as the market price. The spraying cost 24 may include the cost of purchasing each pesticide to be sprayed, as well as labor costs for purchasing and spraying the pesticide. The flight cost 25 may include the cost of purchasing the drone 1, the fuel cost for the drone 1, labor costs, and other costs incurred when flying the drone 1.

[0041] The plan calculation unit 12 stores the calculated spraying plan 27 and flight plan 28 in the memory unit 13. The drone 1 sprays the pesticide on the crops in the field F in accordance with the spraying plan 27 and the flight plan 28. In addition, the supply tank 8 is prepared with the amount of pesticide required for spraying based on the spraying plan 27.

[0042] As shown in Figure 3, the spraying management table 21 records the quality, including past harvest yields for each field F or area, and the spraying status of the pesticide (corresponding to the spraying plan 27 described below) each time a harvest is carried out, and stores the changes in quality information 18 and the spraying status.

[0043] 3 records the area of ​​field F, the harvest volume (tan) from two years ago, the previous target harvest volume (tan) from one year ago, the previous harvest volume (tan), the difference between the previous harvest volume (tan) and the previous year and the difference between the target, the stink bug level which is the level of damage caused by stink bugs as an example of previous quality information 18, the current (this year) target harvest volume (tan), the current harvest volume (tan), the difference between the current harvest volume (tan) and the previous year and the difference between the target, the current stink bug level, the stink bug control status which is the spraying status of the pesticide sprayed on the currently harvested crops, etc. Here, the yield is the harvest volume per tan (10a). In the example of Figure 3, the level of damage caused by stink bugs is recorded as quality information 18, but at least one of information related to pest damage such as stink bug damage, harvest yield (weight), moisture content, size, discolored rice, white rice sap, etc. may also be recorded.

[0044] Control unit 10 updates spraying management table 21 stored in memory unit 13 based on quality information 18 and position information 17 acquired by quality information acquisition unit 11, and spraying plan 27 when the pesticide was sprayed the previous time. As a result, quality information 18 of the crops to which the pesticide was sprayed based on the previous spraying plan 27 is added / updated for each position information 17 (field F or area) in spraying management table 21. Note that spraying management table 21 may be configured so that information is added each time the pesticide is sprayed, harvested, or quality is measured, or may be configured so that the oldest information is deleted when new information is added.

[0045] Spraying pesticides on agricultural products is expected to improve yields and the quality of agricultural products by preventing diseases and suppressing pest damage. On the other hand, spraying pesticides with drone 1 requires the spraying cost of 24 for the pesticide and the flight cost of drone 1 of 25.

[0046] If the increase in profits due to improved crop yields and improved product quality is sufficiently greater than the combined costs of spraying the pesticide (24) and flying the drone (25), then it is worth spraying the pesticide. However, if the increase in profits is less than the combined costs of spraying (24) and flying (25), then there is less need to spray the pesticide.

[0047] According to the drone control system of the present invention, by calculating the spraying plan 27 and flight plan 28 based on the quality information 18 for each field F or each area, it is possible to create accurate and appropriate pesticide spraying plans 27 and flight plans 28.

[0048] Furthermore, by using the spraying management table 21 to calculate the spraying plan 27 and flight plan 28 taking into account past performance, a more accurate and appropriate spraying plan 27 for the pesticide can be created.

[0049] Moreover, by calculating spraying plan 27 and flight plan 28 taking into consideration spraying cost 24 and flight cost 25, it is possible to accurately create spraying plan 27 and flight plan 28 for pesticides that will increase profits beyond the costs required for spraying pesticides. Note that if the costs required for spraying pesticides exceed the increase in profits, spraying plan 27 and flight plan 28 may be plans that do not involve spraying pesticides.

[0050] [Update of spray management sheet] As described above, in the drone control system, the updating of the spraying management table 21 based on the spraying plan 27 and the calculation of the spraying plan 27 based on the spraying management table 21 are repeated. Below, the processing flow of the drone control system taking into consideration the updating of the spraying management table 21 will be explained using Fig. 4 while also referring to Figs. 1 and 2.

[0051] First, the plan calculation unit 12 of the control unit 10 calculates the spray plan 27 and the flight plan 28 based on the spray management table 21 stored in the memory unit 13, taking into account the spray cost 24 and the flight cost 25 (step #1 in FIG. 4). Then, the plan calculation unit 12 stores the spray plan 27 and the flight plan 28 in the memory unit 13.

[0052] Next, drone 1 is configured based on spraying plan 27 and flight plan 28 (step #2 in Figure 4) and sprays the pesticide on the crops in field F (step #3 in Figure 4).

[0053] When the crops in field F have grown sufficiently, the crops are harvested (step #4 in Figure 4).

[0054] When the crops are harvested, a color sorter 15 (quality measuring device) measures the quality of the harvested product (crops) for each field F or each area. Quality information 18 relating to the quality is linked to position information 17 of the field F or area (step #5 in FIG. 4).

[0055] The quality information acquisition unit 11 acquires quality information 18 linked to location information 17 from the color sorter 15 (quality measurement device) and stores it in the memory unit 13. Then, the control unit 10 updates the spraying management table 21 based on the quality information 18 linked to the location information 17 and the spraying plan 27 stored in the memory unit 13 (step #6 in FIG. 4).

[0056] [Another embodiment] (1) In the above embodiment, the area of ​​the field F may be each of the sections f into which the field F is divided, as shown in Fig. 5. In this case, quality information 18 is acquired for each section f, and the quality information 18 is linked to location information 17 that identifies the section f, and a spraying plan 27 and a flight plan 28 are calculated for each section f.

[0057] Furthermore, multiple fields F may be managed as large-scale fields, the large-scale fields may be divided into areas consisting of one or more fields F, quality information 18 may be obtained for each area, the quality information 18 may be linked to location information 17 that identifies the area, and a spraying plan 27 and a flight plan 28 may be calculated for each area.

[0058] Furthermore, one area may be one row in which seedlings are planted, and one area may be one ridge in which vegetables, etc. are grown. In this case, quality information 18 is acquired for each row or ridge, and the quality information 18 is linked to position information 17 that identifies the row or ridge, and the spraying plan 27 and flight plan 28 are calculated for each row or ridge.

[0059] Furthermore, the area is not limited to a configuration defined by the sections f, rows, and ridges of the field F, but may be the range in which harvested products (crops) are harvested at one time.

[0060] For example, as shown in FIG. 6, rice plants planted in rows are harvested by a harvester C in groups of several rows. At this time, the harvester C threshes the harvested stalks while traveling back and forth in a field F, and stores the resulting grains in a grain tank. When the grain tank is filled with grains, the harvester C discharges the grains into a grain transport vehicle T. When the grain transport vehicle T is filled with collected grains or has collected a predetermined amount of grains, it transports the grains to a dryer 29. The dryer 29 dries the transported grains. A color sorter 15 (quality measuring machine) measures the quality of the dried grains in the form of brown rice or polished rice.

[0061] At this time, the harvester C, grain transport vehicle T, dryer 29, and color sorter 15 (quality measuring machine) manage position information 17, which is information about the positions in the field F where the grains (harvested products) are harvested. Then, the color sorter 15 (quality measuring machine) outputs quality information 18 related to the measured quality in association with the position information 17.

[0062] In such a situation, the area can be the range where the harvest product (crop) is harvested between the time the harvester C discharges grains and the time the next time it discharges them, the range where the harvest product (crop) relating to the grains transported by the grain transport vehicle T is harvested in one go, the range where the harvest product (crop) relating to the grains dried by the dryer 29 in one go, and the range where the harvest product (crop) relating to the grains whose quality is measured by the color sorter 15 (quality measuring machine) in one go.

[0063] As described above, by setting areas according to the work status, linking the area to location information 17 to obtain quality information 18, and calculating a spray plan 27 and a flight plan 28 for each area, it is possible to accurately create an appropriate drug spray plan 27 and flight plan 28 according to the work status.

[0064] (2) In each of the above embodiments, a quality measuring machine such as a color sorter 15 may not record the amount or percentage of defects, but may manually inspect harvested products (crops) that have been rejected as defective, and determine the amount or percentage of defects for each type of defect as quality information 18, and manually record this in the spraying management table 21 in association with location information 17.

[0065] (3) In each of the above embodiments, the color sorter 15 (quality measuring machine) may sort (category) the quality of the harvested product into one of a plurality of preset product levels. For example, if the agricultural product is rice, the product level may be categorized as finished product, middle rice, broken rice, etc. In this case, the quality may be the yield of each product level for each field F or area. The yield may be the ratio of the amount of harvested product categorized into each product level to the total amount (total amount) of the harvested product sorted for each field F or area.

[0066] The yield may also be the ratio of the amount of harvested products classified as good to the total amount (total amount) of harvested products selected for each field F or region. In this case, the quality information 18 in the spraying management table 21 may be this yield (the ratio of good products to the total amount).

[0067] (4) In each of the above embodiments, the plan calculation unit 12 may calculate the pesticide spraying plan 27 and the flight plan 28 for the drone 1 based on the quality information 18 and location information 17 of the agricultural crop to which the pesticide was last sprayed, without using the spraying management table 21.

[0068] This makes it possible to easily prepare a drug spraying plan 27 and a flight plan 28.

[0069] (5) In each of the above embodiments, the plan calculation unit 12 may calculate the pesticide spraying plan 27 and the drone 1 flight plan 28 without taking into account the spraying cost 24 and the flight cost 25.

[0070] This makes it possible to easily prepare a drug spraying plan 27 and a flight plan 28.

[0071] (6) In each of the above embodiments, the control unit 10 is not limited to being composed of the above-mentioned functional blocks, and may be composed of any functional blocks. For example, each functional block of the control unit 10 may be further subdivided, or conversely, some or all of the functional blocks may be combined. In addition, the functions of the control unit 10 are not limited to the above-mentioned functional blocks, and may be realized by a method executed by any functional block. In addition, some or all of the functions of the control unit 10 may be composed of software. A program related to the software is stored in any storage device such as the storage unit 13, and is executed by a processor such as a CPU included in the control unit 10 or a processor provided separately.

[0072] (7) In each of the above embodiments, the quality measuring machine is not limited to the color sorter 15 and may be any measuring machine, for example, a yield measuring machine mounted on the harvester C or a measuring machine that measures moisture, etc.

[0073] (8) In each of the above embodiments, spraying of pesticides is performed by any flying object, not limited to drone 1, such as a remote-controlled airplane, and the control system of the flying object may calculate spraying plan 27 and flight plan 28 for spraying pesticides by any flying object. [Industrial Applicability]

[0074] The present invention can be applied to spraying pesticides using drones not only on rice, but also on vegetables such as carrots and various other agricultural crops. [Explanation of symbols]

[0075] 1. Drone (aircraft) 5. Crops 11 Quality information acquisition department 12 Planning Calculation Department 15 Color sorting machine (quality measuring machine) 17 Location information 18 Quality information 21 Spraying management table 23 Purchase price (price) 24 Spraying Costs 25 Flight Cost 27 Spray Plan 28 Flight Planning F Field f Section

Claims

1. An aircraft control system for controlling an aircraft that sprays a pesticide in a field, a quality information acquisition unit that acquires, from a quality measuring device that measures the quality of agricultural products harvested in a predetermined area of ​​the farm field, quality information relating to the measured quality and the area in association with the measured quality information; A plan calculation unit that calculates a spray plan for the drug and a flight plan for the aircraft based on the quality information and the area; Equipped with The aircraft sprays the agent on the field based on the spraying plan and the flight plan; Further comprising a storage unit, The memory unit stores a spraying management table in which the spraying plan for the harvested crop and the quality information of the harvested crop are linked to the area each time the crop is harvested, The plan calculation unit is an aircraft control system that calculates the flight plan by referring to the spraying management table and the transition between the quality information of the crops harvested in the past and the spraying plan for each area.

2. The aircraft control system of claim 1, wherein the plan calculation unit calculates the spraying plan and the flight plan by taking into account at least one of the price of each quality of the agricultural produce, the spraying cost of the pesticide, and the flight cost of the aircraft.

3. The crop is rice, The quality measuring machine classifies the rice kernels into one of a plurality of pre-set product grades; 3. The aircraft control system of claim 1, wherein the quality includes classification of the kernels into the product levels and a ratio of the amount of the kernels classified into each product level to the total amount of the kernels.

4. The aircraft control system according to claim 1 , wherein the quality measuring device is a color sorter.

5. The spraying plan includes at least one of the type of the drug to be sprayed, the amount of the drug to be sprayed, and the number of times the drug is sprayed, The air vehicle control system of claim 1 , wherein the flight plan includes a flight path.

6. An aircraft control system described in any one of claims 1 to 5, wherein the area is each section into which the field is divided, or each ridge or row of the field, and the quality information is information on the quality measured for each section, each ridge, or each row.

7. An aircraft control system for controlling an aircraft that sprays a pesticide in a farm field, comprising: a quality information acquisition unit that acquires, from a quality measuring device that measures the quality of agricultural products harvested in a predetermined area of ​​the farm field, quality information relating to the measured quality and the area in association with the measured quality information; A memory unit that stores the past quality information and the transition of the spraying status of the drug; A plan calculation unit that calculates a spray plan for the drug and a flight plan for the aircraft based on the quality information and the change in the spray status read from the memory unit; Equipped with The aircraft is an aircraft control system that sprays the pesticide on the field based on the spraying plan and the flight plan.

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