Revolutionizing agriculture: Detecting plant diseases early with computer vision

Crop Health is of primordial concern when it comes to thriving agriculture. In the age of smart agriculture, one of the significant challenges crop caretakers face is early plant disease detection. If gone unnoticed, these crops and plants can bring catastrophe to agricultural land. It reduces yields and can cost billions in agricultural losses across the globe. As a subset of Artificial Intelligence, computer vision is creating a brainstorming revolution, helping farmers recognise plant diseases faster at scale.  

As per the latest research made by the Food and Agriculture Organisation of the United Nations, nearly 20 to 40 per cent of global crop production is lost each year owing to biotic threats. Meanwhile, when invasive plant insects are causing 70 billion dollars in losses, plant diseases are estimated to cause over 220 billion dollars in annual losses.     

Several sustainable farming practices, as well as integrated pest management, have potentially lowered risks and advanced effective ways for detecting diseases early. Computer vision for plant disease detection and recognition enables faster and exact health monitoring across different growing conditions. 

In this blog post, we are going to explore how AI-powered computer vision is transforming agriculture, its techniques and the ways it is applied to crop management operations.

How does the early detection of diseases matter

Several plant diseases can spread widely, and by the time they show visible signs, it is already too late. Several traditional processes, such as manual monitoring and inspections, can be time-consuming and subjective. The early diagnosis enables farmers to take timely action, employ the right treatments, and diminish chemical use.      

Herein, for understanding plant disease management, it is important to look at the major factors that are potentially responsible for delaying plant disease identification.  

Hidden infections & different causes

Some diseases begin inside the plant, and the infections caused by viruses often remain hidden for days. The time the plant colour changes or leaves change in appearance, the damage may have already started. 

Restricted access to professional help

Across remote villages, crop caretakers and farmers may not have trained professionals. Without expert supervision, people can miss out on the early signs of guessing the wrong cause. 

Lack of resources for regional crops

There are times when farmers growing local crops may not find the correct range of descriptions or resources. It creates confusion as they view unknown symptoms.

Time constraints for monitoring

The process of monitoring all plants on a regular basis requires time. It is quite challenging to monitor vegetation or plants manually. 

How computer vision contributes to early plant disease detection   

Traditional forms of plant disease detection are outdated. With advanced computer vision systems, the problems of late disease detection can be mitigated. Using image-based analysis, vegetation caretakers can check for signs of disease and prevent diseases from turning into a catastrophe.

Recognises plant images

Gathering images of plants utilising devices like drones, smartphones and fixed cameras, the process of vision AI begins. It assesses the collected images to detect possible signs of diseases.  

Identifies precise & early symptoms

Potential deep learning algorithms promote plant disease detection and classification by analysing the shape, patterns and texture of stems and leaves. Leveraging AI, the systems can identify symptoms that are challenging to detect manually.  

Delivering fast results 

As the system processes the gathered image data, it immediately provides results that indicate the type of disease present, focusing on impactful areas. It enables farmers to act promptly and helps in delivering fast and better results.      

Reduces manual overload

Manual inspections are now outdated, allowing farmers to use targeted image capture in relevant areas. It reduces the labour and time required for regular field inspections over largely populated areas. 

Sustainability

The benefits of computer vision analytics are beyond the scope of detection. It ensures healthier farming practices with minimal environmental impact, which promotes better sustainability.

Scalability & precision

Artificial Intelligence is capable of detecting thousands of images without any concerns. From diminishing false diagnoses to inessential pesticide use, the early crop disease detection system lowers labour and other kinds of costs that could have elevated expenses in the long run.  

The Process of computer vision that works for crop monitoring & protection

Computer vision systems have a history of accurate results and efficiency. We have curated the process:

Gathering of Images

Crop caretakers and farmers often use smartphones and cameras to capture images of crops.  

Preprocessing

In the preprocessing stage, the images can be cleaned, normalised and improved for removing noise and inconsistencies. For ensuring reliable results, the gathered images were improved through noise reduction, image segmentation and contrast adjustments.              

Feature extraction   

Artificial Intelligence models depending on deep learning (CNNs) are capable of extracting features like texture analysis, spots, wilting patterns and so forth. These features assist the system in differentiating between healthy and diseased plants.

Detection of diseases

AI algorithms are trained enough to compare features against a database of familiar pests and diseases. It promotes early detection of diseases such as fungal infections and insect infestations. 

Continuous learning & monitoring

Computer vision systems are evolving rapidly with the passing time. It keeps a real-time monitoring of the vegetation types, crop management growth stages, climate conditions, and emerging diseases.      

Critical considerations for implementing AI-powered crop monitoring

Crop management and vegetation detection using advanced video analytic software is one of the greatest revolutions in the 21st century. For a comprehensive understanding of crop monitoring with vision AI, we need to have clear concepts of the primary considerations.

Environment variability

Several conditions, such as weather, camera angles and others can heavily impact the accuracy of the results. Therefore, it should be tested in distinct situations.

Data quality

Several models involve diverse and lucid datasets that represent distinct stages of vegetation and crops.                                                                        

System integration

It is important to ensure that the tools can be integrated with existing platforms as well as workflows. 

Real-world applications 

Grape vineyards

AI-powered drones can inspect vines and identify downy mildew or black rot. 

Tomato leaf disease detection

Certain AI models are highly effective in detecting early signs of bacterial spots and leaf mould. 

Final thoughts

Vision AI has come a long way in supporting multiple industries. Vegetation and crop management are no exception. Leveraging computer vision, farm caretakers can detect problems and analyse plant images, seeking a prompt response. With increasing organic food demands and unpredictable climate challenges, it has become difficult to rely on traditional, outdated practices of crop monitoring. Computer vision powered by AI is effectively transforming plant disease detection, enabling farmers to act prior to any kind of problems. 

Ready to elevate your agricultural practices to the next level? Our experts are just a click away. Reach out to them and know more. 

Frequently asked questions

What is computer vision in Agriculture?

In agricultural sectors, computer vision and AI Video analytics software plays a major role in automatically detecting and interpreting several visual symptoms in vegetation and crops. The things to monitor involve discolouration, spots, yellow leaves and wilting. It helps farmers to monitor plant health without manual inspection. 

What kind of plant diseases can be detected?

Identifying a wide array of problems, vision AI is empowered to monitor and detect rust, blight, pest damage and nutrient deficiencies. Additionally, it notifies when the plant/crop requires maintenance.

In what way does computer vision detect plant diseases?

Trained AI models of Computer vision for agriculture can simplify analysing high-resolution images of crops. The process identifies different diseases. The models can classify diseases depending on shape, colour, texture and pattern changes. 

What perks make computer vision apt for plant disease identification?

A number of perks involve early diagnosis, treatment, lower labour costs, real-time crop monitoring, and scalability for large farms and greenhouses. 

What tools are important for implementation?

A bunch of tools and systems that can make it work for vegetation involve drones with imaging systems, fixed cameras with IoT connectivity, and AI software for analysis and reporting. 

Can computer vision prevent disease outbreaks?

At the time of training with premium quality datasets, early detection assists farmers in isolating impacted plants, applying targeted treatments and many more.