How AI Is Used to Identify Diseases In Plants
Artificial Intelligence Is Improving Farming Across the World
Plant diseases significantly threaten global food security, causing substantial economic losses and affecting millions of people worldwide. Fortunately, advances in artificial intelligence (AI) have made it possible to identify plant diseases more accurately and quickly than ever before.
In this article, we will explore how AI is revolutionizing plant disease identification, as well as its benefits to the agriculture industry. Keep reading to learn more.
What Is Machine Learning Imaging Technology?
To understand machine learning imaging technology, we first need to break it down into two components: machine learning and image processing.
Machine Learning: Machine learning (ML) uses complex adaptive computer algorithms to make decisions and learn autonomously. ML algorithms use statistical data to create inferences from patterns in data, thus drawing contextual conclusions and results.
Imagine Processing: Image processing is where images are transformed into digital data that can be used in analytical and functional scenarios.
The data obtained from image processing is utilized by machine learning algorithms to understand and analyze their features. So, machine learning imaging technology is software capable of understanding images, learning from them, and drawing conclusions as a result of algorithmic observations.
AI Plant Disease Identification Technology
AI plant disease identification technology is used to determine the health of targeted plants and identify what specific diseases they may have. Diseases are identified through comparative image processing.
This technology is largely based on comparative image processing, meaning that ML algorithms analyze and compare large volumes of images to determine how a healthy plant specimen should appear.
The process behind AI-driven plant identification can be broken down into four key steps:
Image Acquisition: Images of plants are captured using cameras and imaging devices; they may also be ascertained through online sources.
Preprocessing: In this stage, the images are preprocessed to improve their appearance and better define their features. This involves adjusting the contrast and brightness, removing noise, and enhancing edges.
Feature Extraction: The preprocessed images are then used to train machine learning algorithms to understand which features indicate healthy and unhealthy plants, such as color, texture, shape, and blemishes.
Deployment: After the ML algorithms are trained on a large set of diverse data, they can then be used in practice to accurately identify diseased plants.
Uses of AI-Driven Plant Disease Identification
The ability to identify plant diseases without having an expert eye boasts many valuable use cases, from house plant maintenance to flower shop upkeep; however, it’s particularly useful in agriculture.
Here are just a few uses of AI-driven plant disease identification in farming:
Precision Agriculture: AI plant disease identification tools can be used to precisely identify plant diseases in crops, thereby enabling farmers to use targeted treatments for the affected plants. This leads to reduced costs and increased yield.
Early Disease Detection: Early detection of plant diseases is critical for effective control and prevention. Disease detection tools can help identify diseases early, reducing the spread of the disease and saving crops.
Large-Scale Monitoring: AI can be used to monitor large areas of crops for disease, allowing farmers to quickly identify and respond to outbreaks.
Agricultural Research: AI plant disease identification tools can be used to study and analyze crop diseases, leading to a better understanding of their causes and potential treatments.
What would you use AI-driven plant disease identification for?
Case Study: Indian Potato Blight
A recurring problem for Indian farmers is a fungal disease that infects potato crops, causing significant damage, reduced yields, and lower-quality products. Traditional methods of identifying the disease in crops are time-consuming, unreliable, and costly, ultimately burdening farmers.
Potato blight is a common problem affecting Indian farmers, which is why startup company Croplyn stepped in to help with their AI technology. The startup developed an AI-driven mobile app that enables farmers to swiftly and accurately identify diseased potatoes.
Croplyn’s app uses machine learning algorithms to analyze images of potato leaves and identify the presence of fungal diseases; it then provides farmers with recommendations for treatment and spread prevention.
By using the user-friendly app, Indian farmers have been able to identify signs of potato blight in its early stages and take swift action to prevent the spread of the disease.
Plant Diagnosis in Your Pocket
Croplyn’s app was designed for mobile devices to make it accessible and easy to use for farmers. Similar technology is available to everyday users on iOS and Android devices, which can be utilized to identify diseases in house plants and homegrown crops.
Here are some of the top apps for plant disease identification you can download right now:
Plantix: Plantix is a free app that helps farmers, gardeners, and plant enthusiasts to identify plant diseases quickly and accurately. The app uses AI algorithms to analyze photos of plant leaves, fruits or stems to detect diseases, pests, and nutritional deficiencies. The app also provides recommendations for treatment and prevention.
PictureThis: PictureThis is a popular plant identification app that also includes a plant disease diagnosis feature. The app offers a comprehensive database of plant diseases, their symptoms, and suggested treatments. Users can upload photos of plants to the app to get instant diagnosis and recommendations for treatment.
MyIPM: MyIPM is a plant disease diagnosis app developed by the University of California’s Statewide Integrated Pest Management Program. The app offers a database of over 900 plant diseases, pests, and disorders, along with their symptoms, causes, and recommended treatments. Users can search the database or upload photos to get diagnoses and treatment suggestions.
The Future of Farming?
The use of AI in plant disease identification is just one example of how technology is transforming the agricultural industry. From precision farming to automated harvesting, AI is driving innovation that promises to make farming more sustainable, efficient, and profitable.
By harnessing the power of AI-driven disease identification and other technologies, farmers can build a more resilient and sustainable food system that benefits both farmers and consumers alike. AI in agriculture is an excellent example of artificial intelligence acting as a force for good in the world.
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