Agriculture is an industry with massive potential for disruption by technologies like artificial intelligence (AI) and computer vision. Computer vision systems that can automate tasks like crop monitoring, livestock tracking and disease detection promise to help farmers improve productivity, lower costs and meet sustainability goals.
This article provides an in-depth look at major applications of computer vision in agriculture, market adoption trends, real-world benefits for farmers, key challenges that need to be overcome and why the future is bright for AI in farming.
Applications of Computer Vision in Agriculture
Computer vision applies advanced cameras, data analytics and machine learning algorithms to extract meaningful insights from visual data in agriculture. Let‘s examine some major applications:
Precision Agriculture
Precision agriculture uses autonomous drones and robotic systems equipped with computer vision to monitor crop fields. These systems can:
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Create detailed visual maps of farmlands using aerial imagery from drone cameras. Object detection algorithms outline boundaries of crop beds, roads, buildings and bodies of water.
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Continuously monitor crop health by analyzing color, height, canopy cover and other visual indicators with image classifiers trained to spot disease or nutrient deficiencies early.
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Enable precise spraying of water, pesticides or fertilizers only on areas that need them using object/anomaly detection, saving costs.
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Track growth rate by identifying and counting fruits & vegetables as they develop using instance segmentation. This data feeds yield prediction models.
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Spot weeds and pick them precisely without damaging main crops using semantic segmentation to differentiate plant types.
For example, Blue River Technology has developed the LettuceBot weeder robot using computer vision which reduces herbicide use by 90% compared to traditional spraying.
Livestock Monitoring
Computer vision is revolutionizing large cattle and poultry farms by enabling continuous, automated monitoring of animal health and behavior such as:
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Detect injuries, disease or lameness by analyzing posture, gait and movement patterns of livestock using pretrained image classifiers.
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Monitor feeding levels by counting number of times an animal visits a feed dispenser using object detection networks. Any dramatic drop may indicate sickness.
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Send alerts on odd behavioral patterns like animals gathering in corners or attempting escape through perimeter fences based on movement heatmap analysis. This helps improve animal welfare.
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Track growth rate of each animal over weeks/months by extracting 3D visual data on individual animals with depth sensors and LiDAR.
For instance, the Spanish startup Innovet uses computer vision with depth cameras to monitor commercial chicken farms. Their system doubled early disease detection rates leading to healthier birds.
Crop Phenotyping
Crop phenotyping means measuring physical traits like plant size, leaf shape, height etc to link with genomic data and inform plant breeding efforts. Traditionally a manual process, computer vision automated phenotyping by:
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Capturing thousands of images of plants under different conditions with cameras and depth sensors from multiple angles over time.
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Perform morphological analysis – algorithms identify and measure stalks, leaves, kernels etc in 3D point cloud data using segmentation.
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Correlate visual trait data like flowering time, height etc with genomic information to uncover relations between genes and physical attributes.
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Use this insight to genetically modify seeds and accelerate development of climate resilient, high yielding crops.
For example Phenospex, a German company, offers high throughput phenotyping platforms embedding computer vision and robotics to rapidly phenotype massive numbers of plants in labs or greenhouses.
Sorting & Grading
Fruits, vegetables and grains need to be sorted and graded by various quality attributes like size, color, freshness, variety etc before sale or processing. Computer vision enables automation of:
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Identification: Detect and classify objects into variety, species etc based on learned visual features using deep learning classifiers – apples vs oranges.
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Quality Assessment: Evaluate color, size, surface defects etc to determine grade or classification. Bayesian models combine multiple CV Extracted attributes into overall quality score.
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Anomaly Detection: Spot fruits with disease, bruises or foreign materials using pretrained defect classifiers and unsupervised ML techniques to flag outliers.
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Sorting & Binning: Group into appropriate grade, size etc categories based on product-specific classification rules engines and direct items into correct packaging or downstream processes via robotic pickers.
California startup Abundant Robotics developed an automated apple harvester robot using computer vision to identify ripe apples and vacuum pick without bruising. Their system achieves picking efficiencies comparable to human workers.
Inventory Management
Computer vision is also helping modernize inventory management on farms by:
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Tracking storage bins holding fruits, vegetables or grain stockpiles using object recognition and instance counters as they get filled and emptied over seasons.
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Monitoring inventory on shelves in greenhouses containing tools, fertilizers etc ensuring adequate stock levels are maintained using object detectors.
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Identifying missing or misplaced inventory in warehouses or storage facilities by comparing real-time camera feeds to known layout maps and expected locations for objects using semantic scene understanding.
For example, the Israeli company Trigo offers computer vision powered systems to digitize grocery retail stockrooms and backrooms improving inventory accuracy and freeing staff from manual cycle counting. Similar solutions can help farms manage their consumable stocks better.
Additional Computer Vision Techniques Benefiting Agriculture
Beyond the categories above, farmers can leverage additional computer vision approaches like:
Hyperspectral Imaging: Hyperspectral cameras detect non-visible electromagnetic spectrum to identify crop stresses and diseases before any visible symptoms appear based on how plants reflect different wavelengths.
3D Modeling & Analysis: Photogrammetry algorithms create detailed 3D reconstructions of terrain, trees and plantations using drone imagery. Combined with LiDAR sensors, rich 3D data improves analytics and insights over traditional 2D data.
Synthetic Training Data: Lack of labelled training data hampers computer vision model development. Using generative adversarial networks (GANs), synthetic images of crops, livestock and farm scenarios can augment limited real datasets to improve model robustness.
Yield Prediction: Analyzing historical patterns between crop growth metrics captured by computer vision and actual harvest yields, Long Short Term Memory (LSTM) neural networks can create accurate yield forecast models.
Strawberry Harvesting: Several startups have created raspberry and strawberry picking robots integrating computer vision to detect ripe berries, robotic arms to pick without bruising and mobility systems to navigate beds without crushing plants.
Specific phenotype analysis use cases have been developed for tomatoes, corn, wheat and several other staple crops which can benefit from 21st century imaging techniques and data science.
The key to success involves tailoring these advanced computer vision approaches to address critical challenges and leverage unique aspects of agricultural environments.
The Current State of Computer Vision Adoption in Agriculture
Investment and innovation in agricultural technology has accelerated rapidly, with agritech VC funding tripling since 2014 to over $10 Billion. A significant portion targets precision agriculture, livestock monitoring, crop diagnostics and harvesting automation applications using computer vision, IoT sensors and big data analytics.
Over 200 startups just in the computer vision + agriculture domain have launched recently. Major corporations like IBM, NVIDIA, Intel, John Deere and Microsoft are all investing in developing solutions for farming powered by artificial intelligence and machine learning.
That said, adoption on farms currently remains low – less than 7% leverage any kind of computer vision technologies based on surveys. Building robust, reliable computer vision models capable of dealing with unconstrained outdoor environments remains technically challenging resulting in inconsistent performance.
However as sensors and algorithms improve, costs drop, and decision support systems simplify data interpretation for farmers, adoption rates are forecasted to grow at 25-30% CAGR reaching over $23 Billion in precision agriculture software and hardware revenue by 2030.
Real World Impact
Here are some real-world examples of computer vision systems transforming productivity, profitability and sustainability on farms through precision agriculture, automation and data-driven insights:
Improving Berry Farm Yield & Profitability
California-based berry producer Ohlone Berries saw yields stagnate at suboptimal levels and operating expenses rise beyond benchmarks. By deploying computer vision from Agerris enabled drones and cameras mounted across their 140 acre farm, Ohlone unlocked visibility into previously unknown microclimate and soil variation zones.
Adjusting irrigation, spraying and harvesting plans based on this new intelligence increased seasonal yields by over 20%. Reduction in fertilizer and pesticide overuse dropped input costs 18% and decreased runoff impact. Overall profits rose $280,000 within a year at margin expansion of 30 basis points even after technology spending.
Early Disease Detection in Greenhouses
Hummingbird Technologies provides computer vision powered crop analytics to large vegetable greenhouse operators. Using custom deep learning algorithms trained on leaf images their systems can detect onset of diseases like powdery mildew faster than human scouts.
By alerting growers days earlier, treatments can be applied sooner avoiding up to 20% potential crop losses. And preventative application is far cheaper than reactive spraying after infections spread. Hummingbird customers see 5-10X ROI within the first year through cost avoidance. Their systems monitor millions of plants daily across 150+ greenhouses globally.
Enhancing Dairy Cow Health & Welfare
Tracking individual animals manually on large dairy farms with thousands of cattle is impossible. Spain’s Cowtrix developed a smart tag system powered by computer vision that monitors each cow‘s activity levels, rumination, eating patterns and even social graph.
By analyzing varied aspects of behavior their algorithms can detect sickness, injuries and distress accurately days before overt symptoms. Earlier vet interventions and improved nutrition planning based on insights has reduced mortality rates up to 7% on customer farms. The system has increased average milk yield per cow on some farms by over 10%.
Accelerating Banana Disease Resistance Research
Panama Disease Tropical Race 4 is a fungal infection threatening Cavendish bananas which comprise 99% of global export supply. To accelerate breeding resistance, Honduran research facility FHIA deployed high throughput computer vision phenotyping technology from German company Lemnatec in their genetic modification labs and greenhouses.
By capturing over 200 images daily of thousands of banana plants and algorithmically analyzing disease expression visually, the phenotyping platforms expedited understanding linkage between genetic markers and disease resilience cutting trait mapping timelines by 70%. This will enable rapid development of Panama disease resistant Cavendish varieties before the epidemic can decimate crops.
These case studies showcase the transformative potential of computer vision and AI in tackling critical agricultural challenges – from boosting productivity, improving animal welfare to accelerating crop innovation pipelines.
Key Business Benefits Driving Adoption
Beyond the technological capabilities, growing investment and promising use cases, what specifically is attracting farmers and agribusinesses to deploy computer vision solutions?
1. Increased Yields & Profits
By enabling precision agriculture and early disease detection, computer vision consistently helps farmers improve crop yields over traditional practices by 15-30% based on multiple studies. Combined with associated cost savings in water, fertilizer and pesticide usage, overall profitability sees dramatic improvements.
2. Cost Savings
Targeted application of farm chemicals only on areas that need them using computer vision guided sprayers reduces associated costs significantly – up to 90% fewer chemicals. With manual spraying of entire fields as the status quo currently, savings can stack up dramatically at scale.
3. Sustainability
Reduction in excess use of pesticides and fertilizers via precision approaches cuts runoff that pollutes ecosystems. Computer vision optimized feeding and animal care also boosts welfare. These help meet growing expectations on ag‘s environmental impact.
4. Operational Efficiency
Automating slow, repetitive tasks like crop scouting, livestock monitoring or fruit picking using computer vision assists or replaces inefficient manual processes freeing up precious staff time for high level decision making.
5. Data-Driven Insights
The rich datasets generated from numerous computer vision tracked variables across crops, equipment and livestock inform smarter real-time decisions as well as longer term planning on investments into seed varieties, machinery purchases or herd management policies aligned to data-driven insights.
Key Challenges Hindering Adoption
However, effectively harnessing computer vision in commercial farm environments comes with formidable technology and data related hurdles needing systematic resolution:
Robustness Across Conditions
Unlike controlled factory floor or warehouse settings, agricultural computer vision models need incredible robustness to varied weather, seasons, light conditions, camera angles, occlusion, plant varieties and other unconstrained scenariosseen on real farms.
Lack of Labelled Training Data
The cornerstone of supervised computer vision algorithms is solid, representative training datasets. But capturing, cleaning and accurately labelling thousands of images across diverse agricultural environments remains highly expensive limiting model quality.
Edge Computing Infrastructure
Running advanced neural networks using streamed visual data from cameras across massive acreages requires significant edge computing resources on farm exceeding capabilities of most current platforms. Ruggedized solutions are needed given harsh outdoor, remote conditions.
Interoperability Challenges
There exists little standardization and compatibility across different hardware, software, connectivity protocols and data schemas in agriculture. Getting sensor readings, equipment interfaces, weather station data etc to interface with computer vision and analytics systems poses integration headaches stymieing value realization.
Organizational Learning Curves
Insights are only as good as ability to act on them. Farm owners and workers need significant training to effectively harness exponentially increasing data from computer vision systems without getting overwhelmed to drive better decisions.
Investment Risk
Despite rapid technological advancements, computer vision in agriculture remains an early stage, capital intensive arena with extended payback periods given seasonal crop cycles. Returns rely heavily on inconsistent external variables. Assessing true ROI and risk still proves tricky limiting large scale commitments.
While challenging, concerted efforts to drive robustness, interoperability, edge computing standards and availability of curated agricultural data repositories will smoothen adoption at scale. Participative design thinking incorporating farmer feedback when building solutions can de-risk technology investments. Corporations can play a key role through financing pilot projects and bolstering regional extension services.
Future Outlook
The future opportunity for computer vision (and AI in general) to help the agriculture sector enhance productivity, profitability and sustainability appears boundless. Here are some major trends that will shape precision agriculture over the next decade:
1. Autonomous Farm Robots
Swarms of self-driving drones, robotic pickers and electric tractor-like farm equipment continuously collecting visual data across fields and autonomously executing actions will minimize direct human labor. These smart robots will transform data and insight velocity.
2. Crop Experimentation Platforms
Indoor urban vertical farms stacked with multi-spectrum sensors, expansion of high throughput phenotyping labs and private seed companies leveraging computer vision to rapidly iterate crop trials will accelerate innovation pipelines for climate resilient plants.
3. Alternative Proteins Adoption
Startups growing meat directly from cells and dairy without cows will harness hyperspectral imaging to dramatically enhance process optimization, quality control and production capacity predicting accelerated consumer adoption of alt proteins by 2030.
4. Blockchain enabled Ag Data Marketplaces
As computer vision data generation on farms explodes, blockchain powered data exchanges will unlock ability for agriculture stakeholders to combine datasets across value chains, consent management and drive standards to tap collective intelligence while preserving privacy.
5. Democratization of Technology Access
Government programs expanding rural broadband initiatives and corporations launching shared services models for small holder farmers will drive tools like computer vision enabled irrigation optimization, marketplace linkages and agronomy advisory to be accessible even on highly resource constrained farms.
The net impact of these shifts will be further consolidation with early technology adopters gaining efficiency, sustainability and market access advantages that will pressure slower moving organizations to either catchup or exit the market. Specialized technology partners can help navigate this disruption.
Ultimately over the next decade, computer vision promises to help the agriculture sector enhance transparency, traceability, efficiency and sustainability while tackling rising food insecurity. But to unlock its full potential, all stakeholders – from farmers to regulators – need to embrace technology fueled transformation of production systems that have seen minimal changes in generations.
Conclusion
Key Takeaways on Computer Vision in Agriculture:
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Applications like precision agriculture, livestock monitoring and crop phenotyping imaging illustrate computer vision allows extraction of visual insights difficult for humans to discern on scale.
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Adoption remains early at under 7% of farms but growth forecasts predict up to 30% of large farms will adopt some form of computer vision technologies for competitive advantage by 2030.
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Case studies prove computer vision solutions consistently improve productivity by over 15%, cut costs and enhance sustainability when effectively implemented.
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However computer vision projects need extremely thoughtful design given technology barriers on model robustness across agricultural edge environments along with data labeling, interoperability and change management challenges requiring mitigation.
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Corporations and governments can accelerate proliferation by financing pilot projects, expanding rural connectivity & computing infrastructure and nurturing data sharing platforms.
While the road ahead has obstacles, computer vision systems have immense potential to help the agriculture sector enhance transparency, traceability, efficiency and sustainability while tackling food security – making future outlook decidedly sunny.
