This is how machine vision and deep learning can be used in agriculture

This is how machine vision and deep learning can be used in agriculture



There are many kinds of problems in our daily life. We are always adopting new methods for the solution of which. Technology has developed so much in recent times that every field is incomplete without it.



Today I am trying to do something different, but relevant. You must have heard the name of Computer Vision. To understand this, it is necessary to examine some experimental contexts.


Imagine a mango orchard. Now think about how to solve this space problem using machine-related resources.




In fact, computer vision is related to the visual spectrum. It has different colors that penetrate the eye and you see.



The reason why color is added here is that if a lot of light comes from an object, it looks bright. But if there is less light on an object, it looks dark.


In this sense, light is very important in machine and computer vision. Now let's talk about some of the challenges of machine vision in the external scenario.


There is no problem if you create an interior landscape with the same light in machine vision software and algorithms. But if you want to use it in agriculture, you have to go to a large area.


Where there are large fields between many trees. There the state of light may be different than usual. If you go during the day, you will see that everything is 'overexposed' there.


As the sun shines brightly, the shadow also becomes clear. Some branches, leaves, and fruits fall in the shade. Thus, not all trees have the same amount of sunlight.


How to deal with it, there is a question. For indoor rooms, you need to go to the image processing algorithm. For example, histogram equalization, where you can generalize and equalize the intensity of light.


It works to improve the part of the tree that is in the dark. But over-exposure is still a problem.


If something is over-exposed, you see white in it and do not know its true color. This affects the rest of the programming.


If you are in the field, you can use artificial light. Since it has the same level of light, it doesn't make much difference. You can use algorithms for that.


If you can solve this problem easily and cheaply, you can use LED light. Or you can take the help of a good type of straw light. Which helps you to see the object clearly.


A mobile app that removes shadows and shapes fruit

We have developed an app to take pictures of fruits. We use a backboard to prevent photo noise in the background when taking a picture of a fruit.


Then we put the size, length, width of the fruit. At the same time, we keep their weight. You can also find the weight from the available details.


When you try to segment the image by placing it on white paper, the shadow of the fruit at the bottom of the image may not be good for segmentation and its dimensions may be wrong.


So we use color space conversion to get the actual shape by removing the noise of this shadow. Generally, we have an RGB image and we can use some other software based on open source.


This color changes from one color space to another. In some cases changing the color space helps to normalize the problem to a great extent. For this, you need to change the LAB space.


When changed in this way, the blue color disappears. After that, you can see two pix holograms of its picture. If both of these are fun, you can use the dynamic threshold method.


Therefore, you do not have to specify any threshold value within the program. You can use some dynamic threshold method, which segments the fruit by looking at two pixels.


This happens behind LAB space conversion and thresholding. You can then measure everything by making a box outside the picture of the fruit, its size, and weight.


To measure the size of the fruit is a yellow square. It has a square mark with a diameter of five centimeters. Because when you measure the size of the fruit from the picture, the fruit looks big if it is close to the camera and small if it is close.


So to know the actual size of the fruit, you need to measure the distance between the camera and the fruit. For this, the yellow square acts as a scale.


Scalability

When you design a solution, you have to think about scalability experimentally in real life as well. Mobile phones have many sensors that you can use.


But that doesn't increase the scale. If you want to use this app, you can only use it on a small farm with few trees. But if you want to build a business solution for a large firm, the previous method is not possible.


You need a lot of data. One is to record a video of a farm with a camera. Another is to fly a drone or take another satellite image.


But it depends on what kind of application you are using. Satellite images are usually taken and analyzed for a visitation index to determine if plants are in good health. But if there is fruit on the side of the tree, then the satellite cannot take photos. You have to take a side image of it.


Picture from artificial light at night

We had LED lights, computers, GPS, cameras to take pictures at night. We set the camera on the car and drove off. He was recording the video.


We later analyzed the video images for fruit and flower detection. Due to the lack of variety in light at night, the fruit can be seen better and it is more accurate.


Farmers go to spray at night, then by setting the camera and spraying they can also collect data and get more information for firm management.


There is another way to do fruit size estimation using machine vision. This is also for scalability. For this, you need to take a picture of the tree. Then you take a sample of the size of the fruits on the farm.


The depth camera is used for this. It could be a stereo camera, or it could be a real-sense, time-of-flight Linux camera used in robotics.


You can see the RGB image and his map of the same picture. On the hit map, you can see different depths indicated by different colors. First of all, you detect the fruit.


But when you detect the size of the fruit, you should only detect the undisturbed fruit that is in good condition. Otherwise, you can't get accuracy.


We have enclosed the fruit using the open CV algorithm. If the ellipse is mixed with all the other fruits except the sample fruit, then you can tell that the fruit is good and not spoiled.


If you look at its hit map, you know its depth value. This means you can know the distance of that fruit. In this way, once you have the distance and the picture, you can know the size, length, and width of the fruit.


You need to have data for the machine, deep learning supervised pieces of training. Most bound box methods are used, other methods are depending on the use.


You can use pixel-level segmentation, instant segmentation, semantic segmentation. But if you can use the bounding box method, it takes less time than pixel segmentation. This can be an easy solution.


First, we talked about a kind of model. Now we come to the part where we talk about the middle ground. You can't detect flowers directly. First, you have to classify the flowers into different stages.


There are three stages of flowering. Where stage A is a very early stage. Stage B is the middle stage and stage C is the final stage.


After sorting and detection in this way, it is possible to know how many flowers and fruits my farm has reached the stage of production. Usually, our textbooks are written about the eight stages of the mango flower.


But since it will confuse you, I have only mentioned about three stages. When you easily label it. Then machine learning becomes easier and more accurate.


Is the deep learning model a black box?

Deep learning models are not black boxes. Because they are based on mathematics and can be visualized.


I have used the Deep Learning CNN model to count the number of fruits from the picture. It projects the number of fruits directly from the picture.


Flower evaluation and fruit mapping

You have already counted the number from the projection that bears fruit. The number of flowers has been counted by classifying them in different conditions.


You also have a GPS point. You can block it on Google Maps. For example, make a block in your garden and thread the number of flowers in it.


By displaying your block in any software, you can see where there are thick flowers based on the color. You can see that the fruit may come soon.


You can send people accordingly after seeing the fast ripening fruit in a block. This way you can talk in the market by projecting the size, number, and ripening of the fruit sooner or later.


In this way, you can organize market planning, storage, vehicles, and everything that works. It works as a decision support system for you.


Fruit picking time

Now is the time to pick the fruit. You can also find out the distance between the fruits. Now is the time to bring in crops with automated technology.


For this, the standard industrial arm works. Such robots have up to 10 arms. The new robot has 16 more arms. These arms pick the fruit. Different robots pick different fruits, but because of the structure of your tree, the task of picking you is a bit of a challenge for the robot.


Are they available?

There are many uses of machine vision and deep learning in agriculture. It works from quality assurance to projection of results.


We have new industrial-grade computers to use for large scale agriculture. These computers have GPU computing so that everything in the field can be processed in real-time.


High-Performance Virtual Computers have internet and cloud computing for processing and various IoT devices and sensors are available to collect data.


There are various deep learning algorithms and Tron models for this.


Where are we going

The agricultural sector has also accepted machine vision and AI as the technology of the future. Automatic vehicles without drivers have started running on a large acre of land.


Commercial growers have begun to change the size of their trees and the structure of their fields to match machine vision and robotic harvesting.


Apple trees are beginning to grow in two-spindle trees. Mango plants are also being tested.


Good visibility, accurate machine vision is easy for robotic picking arm and can also launch.


Even if you are an expert in machine learning and its software, data acquisition is always important in machine and computer vision. You can't do a good job if you don't have a good signal.


Always pay attention during data acquisition

The choice of machine vision hardware and computational resources depends on its use. It all depends on what kind of camera, what kind of machine to choose. In the beginning, you should always pay attention to the scalability of your program or productivity.

Comments

  1. Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. big data projects for students But it’s not the amount of data that’s important.Project Center in Chennai


    Spring Framework has already made serious inroads as an integrated technology stack for building user-facing applications. Corporate TRaining Spring Framework the authors explore the idea of using Java in Big Data platforms.

    Spring Training in Chennai


    The new Angular TRaining will lay the foundation you need to specialise in Single Page Application developer. Angular Training

    ReplyDelete

Post a Comment

If you have any doubts. Please let me know.

Popular posts from this blog

Artificial intelligence (AI) - the ability of a digital computer.

Facebook's name has been changed to 'rebranding'

What is SEO and how to do search engine optimization?

Labels

Social media Facebook of What a and phone on mobile This you are Do smartphone internet IT Android workforce Nepal app your from robot iPhone use Machine Learning for Python will company computer with account can data does password these twitter Apple digital feature Instagram Whatsapp YouTube like machine media not why Tiktok new ChatGPT China an be by free out people search that website without work Future India ML corona features find information make online or public video Elon Musk Microsoft One apps has market million social user users way year Intelligence Laptop US billion education history home protect service videos want Bitcoin Have Machine Learning Future Nepali Now Operators Scientists Wi-Fi Windows chrome code cyber download hacking money network photos tips world Amazon Artificial Intelligence Future Avoid Cryptocurrency Here If Know Learning TV Things artificial battery being browser human malware many need netflix photo security smart software study system there update which 10 15 Beginners Buy Deep Learning Did Privacy Who about business career chat cloud digital marketing down hacker marketing millions number phones sent virus when work force Agriculture Bug Deep Earth GPS Gmail Google Maps Kaggle Keep NASA RAM Than Top Windows 11 World Cup Xiaomi address after also as at available camera change dangerous difference drive earn easy email going its job jobs language life look may message news old open price really search engine settings storage store such two used version watch windows 10 working 14 2020 2022 4 5 6 7 Cambridge Content Dark Web GB GPT Global Health-care Lite Maps Messages More Oppo Pakistan PayPal Print Pro QR Risk SEE SEO Samsung So Some Telegram TensorFlow Tutorial Type Types Vision Ways WiFi Zoom advertising attack been best better biggest blue brain chip comments country created cyber attacks don't electricity engine eyes fake files first football function game get go government hacked hackers hidden hours image install lost medical mind misused monitor moon once pay percent play problem processing program quantum robots scan science send share signal smartphones space stay story take their them thousands time topics tricks up using was water web where while wireless workers 000 2024 5G AI Education Alan Musk America Analytica Applications Army Blockchain Bounty CCTV COVID-19 Chat GPT Choose Clean Close Clubhouse Computer Vision Crypto DL Developer Development Docs Electric Explain Factory Finally Gemini Google chrome Google drive Healthcare Help Here's I IBM Japan Keras Kernels Large Lifestyle Looking MDMS Mac Models Musk Natural Ncell Net Notebooks PC Preparing Reasons Russia SIM SMS Save Scikit-Learn Skills SpaceX Stephen Hawking Sun Tesla Theme Therefore Thinking VPN Variables Word WorldLink ability accounts ads age airplane all any aware background bandwidth bank become beneficial between blocked bring bully cable call cameras captions capture care cause charge chatbots check come coming companies complete computers consumption copyright corona-virus courses create currency cyber security dataset datasets days delete deleted deleting details developed device dislike doctor documents doing domain due during dynamic energy engineer engineering exactly forever found fraud full gadgets games getting given good got guest handle his humans iOS iPhone 14 iPhones important including increase industry keyboard known launch law learn listen live manager map meaning megapixel memory messenger mode model month months most movies much name nonsense nuclear opening over own phishing physics porn post posts prevent problems product production programming protection quickly real-world reduce reward robotics run safe same saving say scandal searched selfie show site sold someone speaking speed spyware stuck students subscription systems target techology television tick today torrent traffic trillion universe upload verification voice war weakest women worldwide years & 'Buy the Dip' 'HDR' 'I' 'Mr. Beast' 'Professional Mode' 'football intelligence' 'hidden' 'refill station' (IoT) (LLM) (NLP) 1 100 10:10 10th 12 145 16 17 19 2 200 2007 25 35 3D 40 4000 48 4K 5 P's 60 7 C's 8 @everyone on A17 AI Tool AI ethics API AR Adjust Adobe Adopt Adsense Adsense Supports Africa Alexa Ali Baba Altman Amazon Jungle Amazon Prime Ambani American Anaconda Android 11 Android TV Android phone Annoyed Appoints Arithmetic Art Art through NFTs Artficial Intelligence Artificial neural Artuficial Intellegence Ashika Tamang Assignment Assistant Astronauts Astronomy Atrificial Inteligence Attacks Audiobooks Augmented Reality Australia Auto-GPT AutoML Avatar 2 Bachelors Banned Bard AI Based Because Before Bernie Sanders Big data BigQuery Bill Gates Bitwise Blind Blockchain Developer Blockchain Technology Books Brave Brave Browser Brazil C charger CPU CPU temperature CTEVT CV Cases Casting Changed ChatGBT Chery Chinese Citroën C5 Cloud Factory Cloud Factory Nepal Club House Colab Command Comparison Compute Concatenate Contactless Contactless payment system Copa America Copilot Couple Challenge Crash test Create your first Project on Python Crossover Cup DNS DRS Gaming Dark mode Datalab Deep Fake Deep Learinig Deep Learning with Python Deep Neural Networks Deepfake Demat Dept Development in predictive analytics Didn't Digital avatars Discontinuing Do not Dodge Dogecoin DuckDuckGo E-task EA ETF EU EV Earbuds Earth 2 Earthquake Edge Computing El Salvador Elected Electric Vehicles Electrical Elon Embedded Application Embedded Application (EA) Emoji Estimators Ethical Hacking Euro NCAP European Even Everyone Evolve Explained Explosion Express WiFi FPS Facebook Messenger Facebook's Facets Fears Federal Reserve System Finance Firefox FiveG Fixed wireless Follow Forge Fraud Call Freefire Freelancing GIF Git Gold Google Chat Google Cloud Google Meet Google Play Music Google Plus Google Plus code Google Workspace Google search Green room Greenroom. Spotify Guest Mode HDMI Happy Birthday Health sector Holi Honest Honeygain Huawei Hyundai ID IMD IP ISP Identify Implementing Includes Increasing Indonesia Inflation InfoSec Input Inspiration Installation Integrated circuit Intel Intelligent Internet of Things (IoT) Introduction Iranian Island Isn't JBL JPG JPMorgan Chase & Co Jack Ma January JavaScript Jio Joker Virus Jungle Jupyter Jupyter Notebooks Keys Korean LAN LLM LP Large Language Models Launch of better autonomous systems Lee Kun-hee Library Line Linux Logical Lucky MDMS Nepal ML Engine MSN MaAfee Mark Zuckerberg Max Meet Membership Mero Share Metaverse Microsoft Office Microsoft Teams Military Military weapons Mobile Operating System Module Mouse Mukesh Ambani Music Must NASA's NEA NFT NFTs Natural language processing (NLP) Nepal. radio mapping Nepali businesses Nepali game Nepali youth Nepalis NetTV Neural Network Neural Networks New Technology No Nokia North Korea Note Object Detection Open-source Opera Operating PDF PNG PPT PUBG Pandas Paytm Pendrive Photoshoot Pi Network Pip Plan Play Store Pokémon Pokémon Go Police Premium Preparations Prerequisite Prime Pro's Process Process discovery Pycharm Pyenv Python Programming Python Tutorial Python Tutorials Python for Beginners Python on Windows Quick Draw RCS Race Radically Ransomware Rashtra Bank Reboot Recommender Recommender Systems Redmi Reinforcement Reinforcement learning Reliance Reliance Jio Remove. bg Replacing Revolution Rice that grows for years once planted Rises Robot Sophia Roles Ronaldo Routine of Nepal Banda S&P 500 S&P Global Ratings SD Scale Scaling Scikit Screen Pinning Selection Seven Shorts Singapore Sitting SixG Snapchat Sophia South Korea Space X Spam Stable Coin Starlink Steve Jobs Stock market String Success Sundar Pichai Supermarket Supervised Supervised Learning Supervised Machine Learning Supply Chain Attack Supports Swift TIFF Telecom TensorBoard TensorFLow Hub Thes Tiktok stop Time Travel Tool Training Data Transforming Trojan Truecaller Trump Trusting Type-C US Congress USA USB United States Unnecessary Unsupervised Unsupervised Learning Unsupervised LearningUnsupervised Machine Learning Unsupervised Machine Learning Upcoming Upcoming Technology Urges Using a drone VPNs VR Vehicles Virtual reality Virtualenv Visualize WWW Wait Walkthrough Walmart WeChat Wha What are Assignment Operators in Python What are Comparison Operators in Python What are Logical Operators in Python What are Operators in Python What are the basic laws of quantum physics What is What is Chat GPT What is Google Adsense What is Pycharm What is Python What is String in Python What is Variable in Python Whose Wi-Fi 6 Wikipedia WordPress Wrangling data Write X X8 series XAI XOR XSS YouTuber Ziglar Zipty Zuckerberg admin advertisers again agency agricultural ai beauty air aircraft aired alert algorithm almost along alpha alternative analytics ancient angles announcement announces another answer answering antivirus anyone anything appear appearance appliances approach approaching approaching science meaning apps. google article artificial blood vessels arts associated attention audience automatic automatically autonomous avatars back backed ban bans bar basic batteries becoming beginner benefit benefits bitcoin mine bitcoins black block boarding bogged book bought box brand break brings broadband brought browsing bug bounty build but buttons buying bypass cable internet cables calculus calls campaign can't cancer cannot car cards careeer carry cave center challenge channel charger charging chat.com cheap cheaper checkmarks chess child children choose. a class clicking climbers clock closest club coding colleges color combat common communicate compensates compete competing computer mouse computer science concept connect cons control controls controversies could countries credit crisis criteria crore crores crowdsourcing culture cyberattack d about damaged danger dark data center data science dating apps day debit dedicated delete data depression destination devices diary die different digit digital cameras digital land digital privacy disappeared disappearing discovered discovery displaced display document dog dollars doodle door downloads dream drone drug trafficking e features e-Rupee e-books e-passport e-sewa eBooks ePassport each earn money from Nepal easier eating economy edit effective electronic else email server emails emerged emergency emojis employee employees end enough espionage etflix ethics except excessive excuse existence expected expire extracts eye face app facial verification facts family far farm fax fdown.net fee feet fiber fight file film final five flying foldable food footprint forced foreigners forget forgotten form formats foundation free upgrade frequency freshman from search fruit fuel game tips gamer gas gasoline geometry gets gives glasses goes good content goodbye goods google docs gossip granted great groups growing had hall hand handy happen happy harmful he head headphones headset heater hobby human brain human intelligence human trafficking hundreds hurting hydrogen hype iCloud iPhone 12 Pro illegal data illicit trade image processing processor images impair inbox incidents income increased incur insecure instant instrument interest internal storage internet speed into intranet introduced invented invention invest investment invites jack join journalists journey kit laboratory lakh languages last later latest launched launching lawmakers laws leak leaks legalize let letter letters light likes link lives loaded location locked longest lose loss love machine vision made main main features makes man manage management system mango marketplace martial mask matches measuring meetings melting meme messaging meta microphone middle million. downloads mine mistake mistakes mobile number moble moment monitors mountain move movie moving mute name-x naming near necessary neural neural networking new code new look new windows news anchor night mode non notes notifications now.gg nuclear energy obscene official offline open source opened operate operated operating system opposed optic optical fiber optimization option options other others our outbreak oversold owner page paid pandemic paper participant participate passports password. passwords patent pattern paying payment pen drive permanent permission person personal perspective phone confidential picture pictures pirated placed planting platform platforms political pop-up popular popularity port possible powered practice predictive pregnant prepared principles private prize processor product key programmatically programming languages project prompt property pros protected proxies proxy quantum computer quantum internet quires quota r daily radio rain rainy season rate reach reading ready real reason rebranding record recovery reform refresh refreshes refrigerator regarding registered registration regulators relationship released remain remove removes removing repairing replace report requiring reset residence resolution responsibilities restaurants returned revenue review rings risks risky road robotic dog rocket room rooms round ruin rules running safely safety sale satellite saying says scary schedule scheme schools screen screens search engines secret secretly secure selectric cars sell semi-final semiconductor sending series server services shared ships shocked shortage should shoulders shuffled shuts shutting sidebar simple since sites sky sleeping smartblock smartly social engineering hacking software. tech solve somewhere soon source sources space center space debris spacecraft spaceships special spectrum spend spending sponsors sports spying star starship start starting starvation steps stocks stolen stop stories strategy streaming student studying subject subscribers successful suggested suggestions suitable suitcase surface surprised survive t are tag tagging talent talk teach team technlogy technoloy technonlogy telecommunication terminology test text they think thousand thread threat to through throwaway timer tinder toilet too took topic tossing touch pad tracking trackpad trading transact transport travel trending trends trip turn turns tweets unbuyable unemployed unemployment unpleasant unregistered unsafe unseen unveils upgrades useful uses various versatility very view viral virtual virtual currency virtual world vishing visit visiting vulnerabilities warning washing waterproof we weapons web design websites week well went were wet willing woman works workspace world war worrie worth writer written wrong young
Show more