JPWO2021119346A5 - - Google Patents

Download PDF

Info

Publication number
JPWO2021119346A5
JPWO2021119346A5 JP2022535746A JP2022535746A JPWO2021119346A5 JP WO2021119346 A5 JPWO2021119346 A5 JP WO2021119346A5 JP 2022535746 A JP2022535746 A JP 2022535746A JP 2022535746 A JP2022535746 A JP 2022535746A JP WO2021119346 A5 JPWO2021119346 A5 JP WO2021119346A5
Authority
JP
Japan
Prior art keywords
formulation
autonomous vehicle
product
field
agrochemical product
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
JP2022535746A
Other languages
Japanese (ja)
Other versions
JP2023509344A (en
Publication date
Application filed filed Critical
Priority claimed from PCT/US2020/064360 external-priority patent/WO2021119346A1/en
Publication of JP2023509344A publication Critical patent/JP2023509344A/en
Publication of JPWO2021119346A5 publication Critical patent/JPWO2021119346A5/ja
Pending legal-status Critical Current

Links

Claims (19)

農薬製品の散布を制御するための方法であって、
遠隔的に感知されたデジタル画像データを取得することであって
ホットスポット遠隔画像サブシステムから第1データセットを受信することと、
前記第1データセットに基づいて診断命令セットを作成することと、
前記診断命令セットを使用して、偵察任務において診断偵察サブシステムを遠隔制御することにより、第2データセットを取得すること、を含むことと、
少なくとも前記デジタル画像データに基づいて、少なくとも1つの農薬製品を可変的な態様で散布するための処方を策定するとともに、前記処方を、前記少なくとも1つの農薬製品を散布するために特定の自律型ビークルがフィールドの上方を複数回にわたって通過することを記述したものとすることと、
前記処方に従って、前記特定の自律型ビークルによって、前記少なくとも1つの農薬製品を前記可変的な態様で作物に対して散布することと、を含む、方法。
1. A method for controlling the application of a pesticide product, the method comprising:
Obtaining remotely sensed digital image data, the method comprising :
receiving a first data set from a hotspot remote imaging subsystem;
creating a diagnostic instruction set based on the first data set;
obtaining a second data set by remotely controlling a diagnostic reconnaissance subsystem in a reconnaissance mission using the diagnostic instruction set;
formulating a recipe for applying at least one agrochemical product in a variable manner based on at least the digital image data; and applying the formulation to a particular autonomous vehicle for applying the at least one agrochemical product. shall be described as passing over the field multiple times, and
applying the at least one agrochemical product to the crop in the variable manner by the specific autonomous vehicle according to the formulation.
前記特定の自律型ビークルは、無人航空機である、請求項1に記載の方法。 2. The method of claim 1, wherein the particular autonomous vehicle is an unmanned aerial vehicle. 前記処方を、前記少なくとも1つの農薬製品を散布するために前記特定の自律型ビークルが前記フィールドの上方を通過する前記複数回のうち、少なくとも1回に関しては、異なる作用機構を記述したものとする、請求項1に記載の方法。 The formulation describes a different mechanism of action for at least one of the plurality of passes of the particular autonomous vehicle over the field to apply the at least one agrochemical product. , the method of claim 1. 前記処方を、前記少なくとも1つの農薬製品を散布するために前記特定の自律型ビークルが前記フィールドの上方を通過する前記複数回のうち、少なくとも1回に関しては、前記少なくとも1つの農薬製品について、カテゴリが異なる1つの農薬製品を記述したものとする、請求項1に記載の方法。 categorizing the formulation for at least one of the plurality of times that the particular autonomous vehicle passes over the field to apply the at least one agrochemical product; 2. The method of claim 1, wherein the method describes one agrochemical product having different values. 前記処方を、前記フィールドの上方を前記特定の自律型ビークルが後続で通過する際に前記農薬製品を再散布することを記述したものとし、前記後続の通過は、前記フィールドの上方を前記特定の自律型ビークルが前記複数回にわたって通過するうちの、1回目の通過の後に行われるものである、請求項1に記載の方法。 The recipe describes reapplication of the pesticide product during subsequent passes of the particular autonomous vehicle over the field, and the subsequent passes include reapplication of the pesticide product over the field. 2. The method of claim 1, wherein the method occurs after the first of the multiple passes of the autonomous vehicle. 前記処方を、少なくとも1つの以前の栽培シーズンからの履歴データに基づいて策定する、請求項1に記載の方法。 2. The method of claim 1, wherein the recipe is developed based on historical data from at least one previous growing season. 前記処方を、予測される気象現象に基づいて策定する、請求項1に記載の方法。 2. The method of claim 1, wherein the prescription is formulated based on predicted weather phenomena. 前記処方を、害虫駆除処方を含むものとし、前記処方を、事前に策定し、前記農薬製品を、前記フィールド内で害虫が検出される前に、または前記フィールド内で害虫の症状が検出される前に、散布する、請求項1に記載の方法。 The formulation comprises a pest control formulation, wherein the formulation is formulated in advance and the pesticide product is applied before a pest is detected in the field or before symptoms of a pest are detected in the field. 2. The method according to claim 1, wherein the method is applied to 前記処方を、出芽前処方を含むものとし、前記処方を、事前に策定し、前記農薬製品を、前記フィールド内で雑草が検出される前に散布する、請求項1に記載の方法。 2. The method of claim 1, wherein the formulation comprises a pre-emergence formulation, wherein the formulation is pre-formulated and the agrochemical product is applied before weeds are detected in the field. 前記農薬製品は、除草剤、殺虫剤、殺菌剤、微生物、微量栄養素、窒素ベースの肥料、植物成長調整剤、枯葉剤、土壌改良剤、または、これらの組合せ、を含む、請求項1に記載の方法。 2. The agrochemical product of claim 1, wherein the agrochemical product comprises a herbicide, an insecticide, a fungicide, a microorganism, a micronutrient, a nitrogen-based fertilizer, a plant growth regulator, a defoliant, a soil conditioner, or a combination thereof. the method of. 前記第1データセットは、第1解像度での第1マルチスペクトル画像を含み、前記第2データセットは、前記第1解像度より高い第2解像度での、第2マルチスペクトル画像を含む、請求項に記載の方法。 2. The first data set includes a first multispectral image at a first resolution, and the second data set includes a second multispectral image at a second resolution higher than the first resolution . The method described in. 前記マルチスペクトル画像の少なくとも1つのセットは、ハイパースペクトル画像を含む、請求項1に記載の方法。 12. The method of claim 11 , wherein the at least one set of multispectral images includes hyperspectral images. 前記第2データセットを、前記第1データセットよりも低い高度で取得する、請求項1に記載の方法。 12. The method of claim 11 , wherein the second data set is acquired at a lower altitude than the first data set. 農薬製品の散布を制御するための方法であって、
遠隔的に感知されたデジタル画像データを取得することと、
少なくとも1つの以前の栽培シーズンからの履歴データを取得することと、
前記デジタル画像データおよび前記履歴データに基づいて、少なくとも1つの農薬製品を可変的な態様で散布するための処方を策定するとともに、前記処方を、前記少なくとも1つの農薬製品を散布するために特定の自律型ビークルがフィールドの上方を複数回にわたって通過することを記述したものとすることと、
前記処方に従って、前記特定の自律型ビークルによって、前記少なくとも1つの農薬製品を前記可変的な態様で作物に対して散布することと、を含む、方法。
1. A method for controlling the application of a pesticide product, the method comprising:
obtaining remotely sensed digital image data;
obtaining historical data from at least one previous growing season;
formulating a recipe for applying at least one agrochemical product in a variable manner based on the digital image data and the historical data; shall describe multiple passes of the autonomous vehicle over the field; and
applying the at least one agrochemical product to the crop in the variable manner by the specific autonomous vehicle according to the formulation.
前記履歴データは、
現在の栽培シーズンの直前の栽培シーズンからの、過去の処方および対応する処方結果と、
前記直前の栽培シーズンにおける雑草の種および密度と、
前記直前の栽培シーズンにおける虫害と、
前記直前の栽培シーズンにおける病害の深刻度と、
前記直前の栽培シーズンにおける土壌データと、
前記直前の栽培シーズンにおける収量情報と、の少なくとも1つを含む、請求項1に記載の方法。
The historical data is
past formulations and corresponding formulation results from the growing season immediately preceding the current growing season;
Weed species and density in the immediately preceding cultivation season;
Insect damage in the immediately preceding cultivation season;
the severity of the disease in the immediately preceding cultivation season;
soil data in the immediately preceding cultivation season;
15. The method according to claim 14 , comprising at least one of: yield information for the immediately preceding cultivation season.
前記履歴データは、人工知能または機械学習アルゴリズムを使用して画像データから導出された空間マップを含む、請求項1に記載の方法。 15. The method of claim 14 , wherein the historical data includes a spatial map derived from image data using artificial intelligence or machine learning algorithms. 前記処方を、前記少なくとも1つの農薬製品を散布するために前記特定の自律型ビークルが前記フィールドの上方を通過する前記複数回のうち、少なくとも1回に関しては、異なる作用機構を記述したものとする、請求項1に記載の方法。 The formulation describes a different mechanism of action for at least one of the plurality of passes of the particular autonomous vehicle over the field to apply the at least one agrochemical product. , the method according to claim 14 . 前記処方を、前記少なくとも1つの農薬製品を散布するために前記特定の自律型ビークルが前記フィールドの上方を通過する前記複数回のうち、少なくとも1回に関しては、前記少なくとも1つの農薬製品について、カテゴリが異なる1つの農薬製品を記述したものとする、請求項1に記載の方法。 categorizing the formulation for at least one of the plurality of times that the particular autonomous vehicle passes over the field to apply the at least one agrochemical product; 15. The method of claim 14 , wherein the method describes one agrochemical product having different values. 遠隔的に感知された前記デジタル画像データの前記取得は、
ホットスポット画像サブシステムから第1データセットを受信することと、
前記第1データセットに基づいて診断命令セットを作成することと、
前記診断命令セットを使用して、偵察任務において診断偵察サブシステムを遠隔制御することにより、第2データセットを取得することと、を含む、請求項1に記載の方法。
The acquisition of the remotely sensed digital image data comprises:
receiving a first data set from the hotspot imaging subsystem;
creating a diagnostic instruction set based on the first data set;
and obtaining a second data set by remotely controlling a diagnostic reconnaissance subsystem in a reconnaissance mission using the diagnostic instruction set.
JP2022535746A 2019-12-11 2020-12-10 Responsive farming systems that are exceptionally optimized during the season Pending JP2023509344A (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201962946806P 2019-12-11 2019-12-11
US62/946,806 2019-12-11
PCT/US2020/064360 WO2021119346A1 (en) 2019-12-11 2020-12-10 Highly responsive farming systems with extraordinary in-season optimization

Related Child Applications (1)

Application Number Title Priority Date Filing Date
JP2024071902A Division JP2024099747A (en) 2019-12-11 2024-04-25 Highly responsive farming system that is specifically optimized in season

Publications (2)

Publication Number Publication Date
JP2023509344A JP2023509344A (en) 2023-03-08
JPWO2021119346A5 true JPWO2021119346A5 (en) 2023-12-19

Family

ID=76316996

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2022535746A Pending JP2023509344A (en) 2019-12-11 2020-12-10 Responsive farming systems that are exceptionally optimized during the season

Country Status (8)

Country Link
US (2) US11908025B2 (en)
EP (1) EP4072940A4 (en)
JP (1) JP2023509344A (en)
AR (1) AR120733A1 (en)
AU (1) AU2020403003A1 (en)
BR (1) BR112022009534A2 (en)
CA (1) CA3162410A1 (en)
WO (1) WO2021119346A1 (en)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111712843A (en) * 2017-10-13 2020-09-25 巴斯夫农化商标有限公司 Personalized and customized plant management using autonomous clustered drones and artificial intelligence
US11440659B2 (en) * 2019-09-12 2022-09-13 National Formosa University Precision agriculture implementation method by UAV systems and artificial intelligence image processing technologies
US11585960B2 (en) * 2019-12-04 2023-02-21 International Business Machines Corporation Effective agriculture and environment monitoring
US11858630B2 (en) * 2020-02-13 2024-01-02 Biocarbon Engineering Ltd. Planting system having oscillating seed agitator
US11583882B2 (en) * 2020-02-13 2023-02-21 Cnh Industrial America Llc System and method for controlling the ground speed of an agricultural sprayer based on a spray quality parameter
BR102020011062A2 (en) * 2020-06-02 2021-12-07 Xmobots Aeroespacial E Defesa Ltda REMOTELY PILOTTED AIRCRAFT INTENDED FOR AIR-LIFT AND SPRAY ACTIVITIES AND AIR-LIFT AND SPRAY SYSTEM
CA3197358A1 (en) * 2020-11-10 2022-05-19 Mark William Miller Irrigation system with unmanned aerial vehicles
US20220211025A1 (en) * 2021-01-06 2022-07-07 Cnh Industrial America Llc System and method for performing spraying operations with an agricultural sprayer
WO2022182644A2 (en) * 2021-02-26 2022-09-01 Zamir Itay Tayas Mobile and or stationary micro-fulfilment method for automated packages delivery by humans and or autonomous vehicles ground vehicles or aerial drones
BR112023027300A2 (en) * 2021-06-25 2024-03-12 Basf Agro Trademarks Gmbh COMPUTER IMPLEMENTED METHOD FOR CONTROLLING THE OPERATION OF MULTIPLE TREATMENT DEVICES, METHODS FOR TREATMENT OF WEEDS, SYSTEM FOR TREATING AN AGRICULTURAL FIELD, USE OF A DEVICE AND COMPUTER PROGRAM ELEMENT
US11610157B1 (en) * 2022-05-09 2023-03-21 Advanced Agrilytics Holdings, Llc Machine learning methods and systems for characterizing corn growth efficiency
WO2024017731A1 (en) * 2022-07-22 2024-01-25 Basf Agro Trademarks Gmbh Computer-implemented method for providing combined application data

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7723660B2 (en) 2007-07-03 2010-05-25 Kyle Holland Sensor-based chemical management for agricultural landscapes
US9113590B2 (en) * 2012-08-06 2015-08-25 Superior Edge, Inc. Methods, apparatus, and systems for determining in-season crop status in an agricultural crop and alerting users
US9852644B2 (en) 2013-03-24 2017-12-26 Bee Robotics Corporation Hybrid airship-drone farm robot system for crop dusting, planting, fertilizing and other field jobs
US10492361B2 (en) * 2013-05-26 2019-12-03 360 Yield Center, Llc Apparatus, system and method for generating crop nutrient prescriptions
US9489576B2 (en) * 2014-03-26 2016-11-08 F12 Solutions, LLC. Crop stand analysis
US9974226B2 (en) * 2014-04-21 2018-05-22 The Climate Corporation Generating an agriculture prescription
US9401030B2 (en) * 2014-04-25 2016-07-26 Tazco Soil Service Co. Image processing system for soil characterization
EP2980669B1 (en) * 2014-08-01 2017-09-20 AGCO Corporation Determining field characterisitics using optical recognition
US9922405B2 (en) 2014-08-22 2018-03-20 The Climate Corporation Methods for agronomic and agricultural monitoring using unmanned aerial systems
CN105197243B (en) 2015-09-22 2017-05-17 北京农业信息技术研究中心 Airborne variable pesticide application system and method for agricultural unmanned aerial vehicle
US10154624B2 (en) * 2016-08-08 2018-12-18 The Climate Corporation Estimating nitrogen content using hyperspectral and multispectral images
US10524409B2 (en) * 2017-05-01 2020-01-07 Cnh Industrial America Llc System and method for controlling agricultural product application based on residue coverage
CN111712843A (en) 2017-10-13 2020-09-25 巴斯夫农化商标有限公司 Personalized and customized plant management using autonomous clustered drones and artificial intelligence
US11079725B2 (en) * 2019-04-10 2021-08-03 Deere & Company Machine control using real-time model
US11467605B2 (en) * 2019-04-10 2022-10-11 Deere & Company Zonal machine control
JP7423631B2 (en) 2018-12-10 2024-01-29 クライメイト、リミテッド、ライアビリティー、カンパニー Mapping field anomalies using digital images and machine learning models
EP3895067A4 (en) 2018-12-11 2022-09-14 Climate LLC Mapping soil properties with satellite data using machine learning approaches
US11357153B2 (en) * 2019-12-11 2022-06-14 Cnh Industrial Canada, Ltd. System and method for determining soil clod size using captured images of a field

Similar Documents

Publication Publication Date Title
Burkart et al. Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution
US11373288B2 (en) Apparatus for plant management
Vega et al. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop
US20220254155A1 (en) Method for plantation treatment based on image recognition
JPWO2021119346A5 (en)
De Castro et al. Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control
JP2022542764A (en) Method for generating application maps for treating farms with agricultural equipment
Yang et al. Site-specific management of cotton root rot using airborne and high-resolution satellite imagery and variable-rate technology
WO2021198731A1 (en) An artificial-intelligence-based method of agricultural and horticultural plants' physical characteristics and health diagnosing and development assessment.
Kumar et al. Unmanned aerial vehicle and its application in Indian Agriculture: A perspective
US20220172467A1 (en) Mini drone and agbot based distributed system and method of offering agronomics services to farmers
Gupta et al. DRONES: The Smart Technology In Modern Agriculture
Phade et al. IoT‐Enabled Unmanned Aerial Vehicle: An Emerging Trend in Precision Farming
Guizzo Your next salad could be grown by a robot
Clay et al. Pest measurement and management
Lottes et al. UAV-based field monitoring for precision farming
Adaka et al. Drones: a modern breakthrough for smart farming
Kaya et al. The Use of Drones in Agricultural Production
US11393193B1 (en) Zone management generation from point samples
Thakur et al. Importance of Artificial intelligence in agriculture
Sarmila et al. Smart farming: sensing technologies
Joe William Adoption of drone technology for effective farm management and adequate food availability: The prospects and challenges
Vemula UAS-based remote sensing for weed identification and cover crop termination determination
Reagan et al. Agriculture revolution
ERDOGAN THE IMPORTANCE OF AGRICULTURAL AVIATION IN PLANT PROTECTION