Skip to content Skip to footer

Computer Vision in Agriculture: Crop Health Monitoring & More

On A Mission to Bring Gesopatial Intelligence

In recent years, the agriculture industry has witnessed a significant transformation through the integration of technology, particularly computer vision. This technology has revolutionized various aspects of agriculture, including crop health monitoring, yield prediction, and automation of tasks. In this article, we will delve into the application of computer vision in agriculture, focusing on its role in crop health monitoring and its broader implications.  

 

First, What is Computer Vision? 

Computer vision is a multidisciplinary field of artificial intelligence and computer science that focuses on enabling computers to interpret, understand, and make sense of visual information from the world, just as humans do with their eyes and brains. It involves the development of algorithms and techniques to extract meaningful information from images and videos. 

 

Types of Computer Vision for Agriculture Industry 

Image Classification: 

  • Involves assigning a label or category to an image. For example, recognizing whether an image contains a specific object, such as a fruit or disease symptom. 
    • Plantix uses AI to analyze pictures of diseased plants, instantly recognizing specific ailments and suggesting treatment options. This empowers farmers to act fast and save their crops. 

Object Detection: 

  • Identifies and locates objects within an image or video frame. It provides bounding boxes around objects of interest. 
    • VineView scans vineyards with drones, identifying ripe grapes with 99% accuracy. This allows for targeted harvesting, maximizing quality and yield. 

Image Segmentation: 

  • Divides an image into meaningful segments or regions, often with pixel-level accuracy. Useful for distinguishing different parts of plants or crops. 
    • SenseGrass leverages computer vision to analyze aerial images of fields, segmenting areas with different weed densities. This enables precision weed control, reducing herbicide use and environmental impact. 

Object Tracking: 

  • Follows the movement of objects in a video sequence over time. Essential for monitoring changes in crop health. 
    • Monarch Tractor uses AI-powered cameras to track individual cows in pastures, monitoring their health and behavior for improved animal welfare and milk production. 

Pose Estimation: 

  • Determines the position and orientation of objects within an image, which can be useful for assessing plant growth or monitoring machinery. 
    • Plenty, a vertical farming company, uses computer vision to estimate the growth stage of its microgreens, automatically adjusting lighting and nutrients for optimal yields. 

3D Vision: 

  • Focuses on creating a three-dimensional representation of the scene from two-dimensional images or video, allowing for depth perception. 
    • TerraClear utilizes LiDAR technology to create 3D maps of fields, identifying irrigation problems and optimizing water usage. This reduces waste and promotes sustainable farming practices. 

 

How Does Computer Vision Works in Agriculture? 

Computer vision in agriculture follows a series of steps to process visual data and make meaningful decisions. Here’s how it works, illustrated with a real-life example: 

  1. Image Acquisition:

In agriculture, cameras or drones equipped with cameras capture images of fields, crops, and plants. These images serve as the raw data for computer vision systems. For instance, a farmer uses a drone to take aerial images of a cornfield. 

  1. Preprocessing:

Raw images often contain unwanted elements like shadows, reflections, or variations in lighting that can interfere with analysis. Preprocessing techniques, such as image enhancement and noise reduction, are applied to clean up the images. In our example, the drone-captured images are processed to remove glare and improve clarity. 

  1. Feature Extraction:

Computer vision algorithms identify relevant features in the images. These features can include plant leaves, fruits, or disease symptoms. For our example, the algorithm identifies the leaves of corn plants and their variations. 

  1. Object Recognition:

The algorithm then uses these extracted features to recognize and interpret objects or regions of interest within the images. In our case, it identifies healthy corn plants and any signs of disease or stress on the leaves. 

  1. Postprocessing:

Additional steps may be applied to refine the results. This can involve eliminating false positives or aggregating data from multiple images to improve accuracy. In our example, the algorithm filters out any false disease identifications and calculates the overall health of the cornfield. 

 

Let’s See How it Works in Real-Life 

Imagine a corn farmer who wants to monitor the health of their crop using computer vision. They deploy a drone equipped with a camera over their cornfield to capture images. These images are then processed by computer vision algorithms. 

  • Image Acquisition: The drone captures high-resolution images of the entire cornfield. 
  • Preprocessing: The images are processed to remove any shadows or reflections that may obscure the view of the corn plants. 
  • Feature Extraction: Computer vision algorithms identify key features like the shape and color of corn leaves. 
  • Object Recognition: The algorithms analyze the features and detect any signs of disease or stress on the leaves, such as discoloration or unusual patterns. 
  • Postprocessing: The system eliminates false positives, ensuring accurate results. It then generates a report indicating the overall health of the cornfield and highlights areas that require attention. 

 

Uses and Applications of Computer Vision in Agriculture 

Crop Health Monitoring: This use involves the application of AI and imaging technology to assess and manage the health of crops, identifying diseases, stress factors, and growth progress. 

  • PlantVillage: Offers a mobile app powered by AI that diagnoses plant diseases based on user-uploaded photos, aiding in crop health monitoring. 
  • Greeneye Technology: Uses advanced imaging technology to detect internal fruit defects before harvest, ensuring better quality produce and contributing to crop health monitoring. 

Weed Detection: In this use, AI-guided robots and open-source farming robots are employed to identify and remove weeds, reducing the need for herbicides. 

  • Weedbuster Robotics: Employs AI-guided robots that use high-frequency radio waves to eliminate weeds without herbicides, offering an innovative solution for weed detection. 
  • FarmBot: Manufactures open-source farming robots capable of weed scouting and targeted weed removal, contributing to effective weed detection and control. 

Yield Estimation: This use involves the utilization of drone imagery, satellite data, and AI to predict crop yields accurately, aiding in informed decision-making. 

  • HarvestEye: Leverages drone imagery and AI to assess the maturity and size of apples in orchards, optimizing harvest timing and resource allocation, enhancing yield estimation. 
  • Y Combinator-backed Arable: Utilizes satellite imagery and AI to predict crop yields with high accuracy, helping farmers make informed decisions about planting and marketing, enhancing yield estimation practices. 

Irrigation Control: In this use, smart irrigation systems with soil sensors and AI are provided to optimize water usage based on crop needs. 

  • CropX: Provides farmers with smart irrigation systems that use soil sensors and AI to automatically adjust water application based on crop needs, contributing to efficient irrigation control. 
  • Spidernet Sensors: Offers a network of wireless soil moisture sensors that connect to a central platform for real-time water management insights, aiding in precise irrigation control. 

Harvesting Automation: This use involves the development of AI-powered robots and robotic arms with computer vision to automate the harvesting of crops, reducing manual labor. 

  • Tevel Robotics: Develops AI-powered robots that harvest a variety of fruits and vegetables, including tomatoes and bell peppers, with minimal damage and high efficiency, contributing to harvesting automation. 
  • Fero Robotics: Designs robotic arms equipped with computer vision and tactile sensors for delicate fruit picking and handling, enhancing the automation of harvesting processes. 

Disease Early Warning: This use employs drones and AI-powered image analysis to detect early signs of crop diseases and pests, enabling timely intervention. 

  • Precision Hawk: Utilizes drones and AI-powered image analysis to detect early signs of crop diseases and pests, enabling early intervention and minimizing losses, providing disease early warning. 
  • EnviroFly: Leverages drone-mounted hyperspectral cameras and AI to monitor crop health at a cellular level, allowing for proactive disease prevention by detecting issues at an early stage, enhancing disease early warning systems. 

 

How Computer Vision Has Impacted The Agriculture Industry (in Numbers) 

The vast fields of agriculture are no longer passive landscapes; they’re buzzing with the invisible power of computer vision. This transformative technology is reshaping farming practices, not just improving aesthetics, but driving real-world impacts on productivity, profitability, and sustainability. Let’s delve into some concrete examples of how computer vision is changing the face of agriculture: 

Boosting Productivity: 

  • Automated Harvesting: Abundant Robotics’ strawberry-picking robots can harvest 400% faster than human workers, reducing labor costs and increasing harvest speed. This translates to significantly higher yields and quicker market reach for farmers. 
  • Precision Weed Control: Blue River Technology’s robots distinguish crops from weeds with 99% accuracy, eliminating herbicide use by up to 90%. This not only saves money on chemicals but also improves soil health and protects the environment. 

Enhancing Profitability: 

  • Optimized Irrigation: Tensiometer.io’s AI-powered irrigation systems reduce water waste by up to 50%, while simultaneously improving crop yields by 20%. This translates to significant cost savings and increased profits for farmers. 
  • Early Disease Detection: Iona Technologies’ drone-based disease detection allows for early intervention against fungal diseases in vineyards, reducing crop losses by up to 30%. This translates to higher quality grapes and increased revenue for winemakers. 

Promoting Sustainability: 

  • Targeted Fertilization: LeafSpec’s hyperspectral imaging technology identifies nutrient deficiencies in individual plants, allowing for precise fertilizer application. This reduces fertilizer waste by up to 70%, minimizing environmental impact and improving resource efficiency. 
  • Reduced Pesticide Use: Weedbuster Robotics’ weed-eliminating robots eliminate herbicide reliance, protecting soil health and promoting biodiversity. This contributes to a more sustainable and resilient agricultural ecosystem. 

Beyond these, computer vision is also: 

  • Improving food safety: by identifying contaminants and ensuring fruit quality before packing. 
  • Empowering small farmers: with affordable AI tools for disease detection and yield estimation. 
  • Opening doors to precision agriculture: with real-time data insights for informed decision-making. 

 

The Future of Computer Vision and Agriculture 

 
The Numbers Paint a Clear Picture: Computer Vision Revolutionizing Agriculture 

When it comes to investments in agriculture, the buzzword on everyone’s lips is computer vision. And for good reason! The expected growth is staggering, with forecasts predicting: 

  • A global market for agricultural computer vision reaching $5.2 billion by 2026, up from $1.2 billion in 2021. That’s a staggering 333% increase in just five years! 
  • Investments in AI for agriculture expected to reach $8 billion by 2025, with a significant portion dedicated to computer vision applications. 

This rapid growth will undoubtedly create a digital divide between farms embracing computer vision and those clinging to traditional methods. Let’s take a closer look at the potential gap: 

Productivity: Farms using computer vision for automated harvesting, weed control, and irrigation can expect: 

  • Yield increases of up to 20%. 
  • Labor cost reductions of up to 70%. 
  • Water savings of up to 50%. 

Profitability: 

  • Early disease detection can reduce crop losses by up to 30%, leading to significant revenue increases. 
  • Precision fertilization can optimize nutrient use, potentially boosting profits by 10-15%. 

Sustainability: 

  • Reduced reliance on herbicides and pesticides minimizes environmental impact, attracting eco-conscious consumers and potentially opening doors to premium markets. 

 

The Future GAP Between Agriculture Companies with Computer Vision  

The gap in efficiency, profitability, and sustainability between farms with and without computer vision will widen over time. Those who embrace the technology will be better equipped to adapt to changing market demands and weather patterns, leaving their less digitized counterparts struggling to compete. 

However, it’s crucial to remember that computer vision is not a silver bullet. The technology comes with its own set of challenges, including: 

  • High initial investment costs. 
  • Lack of infrastructure and digital literacy in some rural areas. 
  • Data privacy and security concerns. 

Addressing these challenges will be crucial in ensuring equitable access to computer vision in agriculture and bridging the digital divide. That’s when a trusted computer vision partner must come into the computer vision implementation process. 

The future of agriculture is bright, and it’s being painted with the pixels of computer vision. By embracing this technology, we can ensure a more productive, profitable, and sustainable future for farmers and consumers alike. 

 

Computer Vision in Agriculture: Crop Health Monitoring & More

Get the best blog stories in your inbox!