WO2021198731A1 - Procédé de diagnostic de santé et d'évaluation du développement de caractéristiques physiques de plantes agricoles et horticoles basé sur l'intelligence artificielle - Google Patents

Procédé de diagnostic de santé et d'évaluation du développement de caractéristiques physiques de plantes agricoles et horticoles basé sur l'intelligence artificielle Download PDF

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Publication number
WO2021198731A1
WO2021198731A1 PCT/IB2020/053083 IB2020053083W WO2021198731A1 WO 2021198731 A1 WO2021198731 A1 WO 2021198731A1 IB 2020053083 W IB2020053083 W IB 2020053083W WO 2021198731 A1 WO2021198731 A1 WO 2021198731A1
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WO
WIPO (PCT)
Prior art keywords
plant
images
agricultural
physical characteristics
plants
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Application number
PCT/IB2020/053083
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English (en)
Inventor
Soroush SARABI
Mohammadreza Mohammadi
Ali SARABI
Mehdi RAZPOUSH NAZARI
Ameneh SHADLO
Ali SOLTANMORADI
Hanieh TAVASOLI
Original Assignee
Sarabi Soroush
Mohammadreza Mohammadi
Sarabi Ali
Razpoush Nazari Mehdi
Priority date (The priority date 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 date listed.)
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Publication date
Application filed by Sarabi Soroush, Mohammadreza Mohammadi, Sarabi Ali, Razpoush Nazari Mehdi filed Critical Sarabi Soroush
Priority to PCT/IB2020/053083 priority Critical patent/WO2021198731A1/fr
Publication of WO2021198731A1 publication Critical patent/WO2021198731A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
    • A01B79/00—Methods for working soil
    • A01B79/005—Precision agriculture
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01C—PLANTING; SOWING; FERTILISING
    • A01C21/00—Methods of fertilising, sowing or planting
    • A01C21/007—Determining fertilization requirements
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/17—Terrestrial scenes taken from planes or by drones
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the pixel matrix in the image is defined by deep learning techniques to pre-learned models of pixel values and the image library comprises images of agricultural and horticultural product's physical characteristics.
  • the problems are any physical plant problem (the diseases include any disease identifiable by visual inspection of plant leaves, stems, and entire plant or any physical changes).
  • extracting relevant images is performed by deep learning and machine vision methods and the pixel model results from multi images from a plurality of cameras received from an image capture device within the agricultural and horticultural products.
  • a method and a device for recommending food based on an artificial intelligence based user status are disclosed.
  • the method includes obtaining use information from a mobile terminal and an external terminal connected to the mobile terminal, determining a user status through an AI device, and determining a preferred food of the user based on the user status, thereby providing convenience for the user's life.
  • the device for recommending food based on the artificial intelligence based user status can be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.
  • UAV unmanned aerial vehicle
  • AR augmented reality
  • VR virtual reality
  • a modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture.
  • a harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.
  • ICT solutions such as precision equipment, the Internet of Things (IoT), sensors and actuators, geo-positioning systems, Big Data, Unmanned Aerial Vehicles (UAVs, drones), robotics, etc. that assessment the physical properties of plants faster and more efficiently and Provide value-added to farmers in the form of better decision-making or more efficient operation and management.
  • IoT Internet of Things
  • UAVs Unmanned Aerial Vehicles
  • robotics etc. that assessment the physical properties of plants faster and more efficiently and Provide value-added to farmers in the form of better decision-making or more efficient operation and management.
  • the method comprising in 3 ways: 1.Flying or moving the unmanned vehicle in the agricultural and horticultural products corridors, 2.Positioning the unmanned vehicle (with or without robotic arms) in the height of plants to capture whole or part of plant body, 3.Capturing images via the cameras, the images of whole or part of plant body agricultural and horticultural plants, and processing the images to diagnosing plant physical characteristics parameters.
  • Plant physiology studies processes that determine plant growth, development, and economic production.
  • Physiologist draws information from fundamental research and works on the whole plant level, solving practical agriculture problems, which limit plant growth and overall production. For example, how various environmental factors influence, nutrient/water uptake, air exchange, photosynthesis/respiration, and production and partitioning of different resources affecting growth.
  • Plant physiology is usually divided into three major parts:
  • the physiology of nutrition and metabolism which deals with the uptake, transformations, and release of materials, and also their movement within and between the cells and organs of the plant.
  • Environmental factors that affect plant growth include light, temperature, water, humidity, and nutrition. It is important to understand how these factors affect plant growth and development.
  • a mineral element is considered essential to plant growth and development if the element is involved in plant metabolic functions, and the plant cannot complete its life cycle without the element. Usually, the plant exhibits a visual symptom indicating a deficiency in a specific nutrient, which normally can be corrected or prevented by supplying the nutrient.
  • Insects and mites can cause plant diseases or transport and inoculate viruses and microorganisms such as bacteria and fungi that cause plant disease.
  • the direct damage to plants caused by insect feeding (herbivory) generally is considered in a separate category from plant disease.
  • Ozone causes considerable damage to plants around the world, including agricultural productions and plants in natural ecosystems. Ozone damages plants by entering leaf openings called stomata and oxidizing (burning) plant tissue during respiration. This damages the plant leaves and causes reduced survival.
  • Herbicide damage is any adverse, undesired effect on a plant that is caused by exposure of that plant to a pesticide designed for weed control. Any plant can be subject to this problem.
  • ICT Internet of Things
  • UAV Unmanned Aerial Vehicles
  • UAV Unmanned Aerial Vehicles
  • the method designed to assess the physical indices of plants, especially agricultural and horticultural products, is based on machine learning, deep learning, and machine vision algorithms. In this way, with the development of technology and automation, the evaluation of plant physical indicators and agricultural processes, including more accurate estimation, plant efficiency, and cost reduction, will be improved.
  • Product evaluation can be divided into two types of field assessment (pre-harvest) and post-harvest evaluation.
  • evaluation in the field and in the field under cultivation can help in the detection of the disease, the distribution of infections by insects, etc. in the early stages of growth.
  • Our goal in this approach is to optimize the appearance of the plant by eliminating manpower and to diagnose it in the event of any disease or problem.
  • Remote sensing data provides more useful information than the physical indices of the plant because of its significant advantages over other compilation methods.
  • Remote sensing can be divided into three categories: aerial, satellite, and short-range sensing.
  • a spatial and spectral resolution should be considered.
  • the benefits of remote sensing data in satellite imagery are wide coverage of the area, high-resolution aerial imagery, and the ability to collect data at arbitrary intervals, and in short-range sensing, it is possible to assess altitude.
  • Drones are usually lighter, less expensive, which are good for gathering information.
  • UAVs have many benefits. They can fly fast and frequently. In terms of flight altitude and time, missions are flexible and can receive high-resolution images. Winds, difficult navigation, data overlap, and flight time constraints on UAVs are limitations.
  • satellite data The most important applications of satellite data are the detection and differentiation of different plant species, the calculation of agricultural and horticultural products cultivation levels, the study of irrigated areas affected by water shortages or the attack of various pests. Other uses of such information are the preparation of comprehensive vegetation cover of each area, mapping of waterways and their relationship with susceptible areas, and estimation of agricultural and horticultural product yield.
  • UAVs fly in the aisles of agricultural and horticultural products up to the height of the plant and receive different images of leaves, stems, and bodies of plants using machine-like visual techniques of the human eye.
  • images are transmitted to the operator, and the signals received by the cameras are processed by systems provided in the way that software solutions rely on machine vision, machine learning, deep learning, and image processing.
  • Image processing is divided into two categories of machine vision and image processing.
  • Image processing is concerned with improving images, but machine vision involves ways of understanding images. It is commonly used in agriculture and horticulture in two ways. One is in processing high altitude images, and the other is near-ground images of plant height, which is our second approach, short-range imaging.
  • Each machine vision system consists of two parts: hardware and software.
  • this system consists of five steps: imaging, initial processing to improve original images, segmenting images into separate areas, measuring the properties of plants, and classifying plants into different groups.
  • These matrices each contain data on the physical properties of plants, such as leaves, stems, and plant bodies.
  • Machine learning is divided into two parts Supervised Learning and Unsupervised Learning.
  • Supervised Learning algorithms are practiced in accordance with specific inputs, such as machine-defined inputs, and human-labeled outputs.
  • Unsupervised learning will play no role in labeling outputs and will allow the system to find commonalities between the data and its outputs.
  • the method developed for evaluating plant physics is based on observational learning, in which the computer or robot is introduced to examples of different inputs to the properties of plants and their appropriate outputs.
  • This machine learning approach is to make the computational algorithm able to gradually compare the outputs of the inputs to learn which approach and algorithm work best for a particular input, thereby enabling the system to detect and adjust the inputs to be more precise the model.
  • This method helps us to identify the plant's physical properties indices in the least time and with high accuracy if there is a problem or disease in the plant.
  • the method designed to assess the physical indices of plants is based on machine learning, deep learning, and machine vision algorithms.
  • Product evaluation can be divided into two types of field assessment (pre-harvest) and post-harvest.
  • Remote sensing data provides more useful information than the physical indices of the plant because of its significant advantages over other compilation methods.
  • Image processing is divided into two categories of machine vision and image processing. In agriculture and gardening, this system consists of five steps: imaging, initial processing to improve original images, segmenting images into separate areas, measuring the properties of plants, and classifying plants into different groups. After initial processing, to improve the original images using machine learning techniques and deep learning models of 2D and 3D pixel matrices are created. These matrices each contain data on the physical properties of plants, such as leaves, stems, and plant bodies and helps us to identify the plant's physical properties indices in the least time and with high accuracy if there is a problem or disease in the plant.
  • a method of the plant's health diagnosing having a plant assessing physical characteristics parameters using an aerial and terrestrial plant health diagnosing system, the aerial and terrestrial plant health diagnosing comprising an unmanned vehicle, capable of being controlled by embedded smart algorithms including machine learning and machine vision with pre-defined maps or real-time pathfinder, a gimbal(s), attached to the unmanned vehicle and a camera(s) attached to the gimbal(s), gimbals attached to the robotic arms or vehicle, the method comprising:
  • This method can be used to control gardens and agricultural fields. and for developing plant physical characteristics data by imaging methods for specific agricultural and horticultural products to be used in conjunction with any machine learning, machine vision, and deep learning methods (all of the deep learning algorithms) to diagnose plants physical characteristics status.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Environmental Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Soil Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Data Mining & Analysis (AREA)
  • Remote Sensing (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Botany (AREA)
  • Ecology (AREA)
  • Forests & Forestry (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Mechanical Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé de collecte de données sur des caractéristiques physiques de plantes en développement par des procédés d'imagerie pour des produits agricoles et horticoles spécifiques, à utiliser en conjonction avec une éventuelle utilisation de procédés d'apprentissage automatique, de vision artificielle et d'apprentissage profond (la totalité des algorithmes d'apprentissage profond) pour diagnostiquer un état de caractéristiques physiques des plantes, la matrice de pixels dans l'image étant définie par des techniques d'apprentissage profond comme étant des modèles pré-appris de valeurs de pixels et la bibliothèque d'images comportant des images de caractéristiques physiques du produit agricole et horticole, les problèmes étant un problème physique quelconque de la plante (les maladies incluent toute maladie identifiable par inspection visuelle des feuilles de la plante, des tiges, et de la plante entière ou d'éventuels changements physiques), l'extraction d'mages pertinentes étant effectuée par des procédés d'apprentissage profond et de vision artificielle et le modèle de pixels résultant de multiples images issues d'une pluralité de caméras reçues en provenance d'un dispositif de capture d'images au sein des produits agricoles et horticoles.
PCT/IB2020/053083 2020-04-01 2020-04-01 Procédé de diagnostic de santé et d'évaluation du développement de caractéristiques physiques de plantes agricoles et horticoles basé sur l'intelligence artificielle WO2021198731A1 (fr)

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PCT/IB2020/053083 WO2021198731A1 (fr) 2020-04-01 2020-04-01 Procédé de diagnostic de santé et d'évaluation du développement de caractéristiques physiques de plantes agricoles et horticoles basé sur l'intelligence artificielle

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PCT/IB2020/053083 WO2021198731A1 (fr) 2020-04-01 2020-04-01 Procédé de diagnostic de santé et d'évaluation du développement de caractéristiques physiques de plantes agricoles et horticoles basé sur l'intelligence artificielle

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IL291800A (en) * 2022-03-29 2022-12-01 Palm Robotics Ltd Aerial spectral system and method, for detecting infection of the red palm weevil in palm trees
CN117344053A (zh) * 2023-12-05 2024-01-05 中国农业大学 一种评估植物组织生理发育进程的方法

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Publication number Priority date Publication date Assignee Title
US20170015416A1 (en) * 2015-07-17 2017-01-19 Topcon Positioning Systems, Inc. Agricultural Crop Analysis Drone
CN107392091A (zh) * 2017-06-09 2017-11-24 河北威远生物化工有限公司 一种农业人工智能作物检测方法、移动终端和计算机可读介质

Patent Citations (2)

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US20170015416A1 (en) * 2015-07-17 2017-01-19 Topcon Positioning Systems, Inc. Agricultural Crop Analysis Drone
CN107392091A (zh) * 2017-06-09 2017-11-24 河北威远生物化工有限公司 一种农业人工智能作物检测方法、移动终端和计算机可读介质

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Title
DHARMARAJ V., VIJAYANAND C.: "Artificial Intelligence (AI) in Agriculture", INTERNATIONAL JOURNAL OF CURRENT MICROBIOLOGY AND APPLIED SCIENCES, EXCELLENT PUBLISHERS, INDIA, vol. 7, no. 12, 20 December 2018 (2018-12-20), India , pages 2122 - 2128, XP055935607, ISSN: 2319-7692, DOI: 10.20546/ijcmas.2018.712.241 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IL291800A (en) * 2022-03-29 2022-12-01 Palm Robotics Ltd Aerial spectral system and method, for detecting infection of the red palm weevil in palm trees
IL291800B2 (en) * 2022-03-29 2023-04-01 Palm Robotics Ltd Aerial spectral system and method, for detecting infection of the red palm weevil in palm trees
CN117344053A (zh) * 2023-12-05 2024-01-05 中国农业大学 一种评估植物组织生理发育进程的方法
CN117344053B (zh) * 2023-12-05 2024-03-19 中国农业大学 一种评估植物组织生理发育进程的方法

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