Artificial Intelligence in Greenhouses
Applications of Artificial Intelligence in Greenhouses
Current applications of machine learning and machine vision are increasingly recognized as key enablers of precision agriculture. Many applications using machine vision technology have been developed, including postharvest product quality, classification and sorting, and process automation. This is because machine vision systems not only recognize the size, shape, color, and texture of objects but also provide numerical attributes of the objects or scene being imaged. Moreover, machine learning algorithms enable the analysis of massive volumes of data quickly and accurately, providing a means for implementing machine vision applications in agriculture. This technology allows growers to take proactive measures—such as targeted application of fertilizers and pesticides—resulting in optimized yields, reduced crop losses, and minimized environmental impact.
Crop Monitoring
Machine learning algorithms gather and analyze extensive data for evaluating crop health indicators like leaf color, biomass, and disease symptoms. These algorithms facilitate the development of prediction models for early pest and disease detection, stress identification, and yield forecasting. Deep learning neural network models like convolutional neural network (CNN) models can be trained on large annotated image datasets to automatically identify and classify a wide range of crop species and cultivars from visual appearance. Recurrent neural networks analyze multi-temporal images to identify changes in crops over time and predict future states. Deep learning algorithms classify every pixel in images into categories like foliage, fruits, or background to provide richer details.
Irrigation Management
AI-driven irrigation solutions utilize a combination of machine learning algorithms, real-time data analytics, and sensor technologies to adjust water delivery to crops dynamically. Unlike traditional irrigation systems that often operate on fixed schedules, AI-based systems continually learn and adapt to changing conditions. These systems analyze a vast array of data, including weather patterns, substrate moisture levels, crop health metrics, and more, to make informed and responsive irrigation decisions.
Insect and Disease Detection
Machine learning algorithms are increasingly being used in agriculture to control pests, diseases, and insects. These algorithms help in the early detection, precise control, and management of crop health, reducing the dependence on manual labor and excessive chemical usage. Artificial intelligence is making significant strides in the early detection and diagnosis of crop diseases. To overcome the constraints of conventional machine learning, deep learning models have been constructed and used for the problem of plant disease identification in drone images.
Automatic Insect Detection Using AI
Significant progress has been made toward developing automated systems (e.g., smart pest traps) that integrate advanced sensing, imaging, and data processing capabilities, often including features like pheromone attractants to target specific insect types. These systems are designed to monitor insect populations continuously, providing valuable data that can inform pest control decisions.
Mobile App Technologies
Mobile applications, or mobile apps, are available to assist growers with plant disease detection, crop management, and yield improvement. Growers can take photos of their crops, and the app identifies pests, diseases, or nutrient deficiencies using machine learning models. Mobile apps can provide detailed information about the detected disease, its symptoms, and possible causes. Growers upload an image of an affected crop to the app.
Pesticide Spraying with Robots
Robots equipped with machine vision technology can identify specific areas infested with pests and diseases, allowing for targeted pesticide application. This pivotal approach ensures that only the necessary amount of chemicals is used, minimizing environmental impact and reducing costs.
Harvesting
Harvesting crops is labor-intensive, and securing staff during peak harvest seasons can be challenging, leading to delays and increased costs. Automated harvesting systems powered by machine vision technology can save time, reduce costs, and improve overall productivity. Automated harvesting systems excel in selecting crops based on their size, color, and maturity level. By putting in place strict quality control measures during the process, these systems can optimize selection, ensuring only the highest-quality produce is collected—this attention to detail results in improved quality, extended shelf life, and enhanced market value. Ultimately, the automated process reduces the risk of crop damage during harvesting, preserving their freshness and appeal.
Crop Sorting and Grading
Fruit sorting and grading refer to the sorting of fruit based on parameters like size, shape, and maturity level. By efficiently sorting and grading crops, machine vision systems drastically reduce the time and labor required for these repetitive tasks. With advanced image-classification algorithms, these systems can detect and sort various crop types swiftly and accurately. For instance, they can differentiate between large and medium-sized potatoes, meeting specific order requirements within moments.
Energy Efficiency in Greenhouses
Costs associated with energy consumption and labor are the primary contributors to the overall operating costs incurred in crop production with greenhouses. Energy utilization by control systems to maintain suitable climate conditions in the greenhouse makes up over 20 percent of the greenhouse farming costs and may even increase for greenhouse structures located in extreme climates. Indoor air temperature, relative humidity, carbon dioxide density, and lighting intensity are some of the vital environmental parameters that need to be monitored in a greenhouse environment to prevent any undue crop stress and optimize yield.
Supply Chain and Demand Forecasting
AI systems can accurately predict demand, aiding agricultural businesses in efficiently managing resources and inventory by conducting thorough analyses of market data. Advanced AI technologies ensure precise control and management at every stage of the agricultural product's journey from farm to market. AI is capable of predicting future market trends based on historical sales data and adjusting supply strategies in real time to accommodate sudden market shifts, ensuring the supply chain's flexibility and responsiveness.
Physical Security
In physical security, AI and ML (artificial intelligence and machine learning) are utilized primarily through video surveillance systems, where they analyze live camera footage to detect anomalies, suspicious behaviors, and potential threats in real-time, significantly improving security by identifying patterns and raising alerts before incidents occur; essentially, "MV" refers to "motion video" captured by security cameras, which AI/ML algorithms can analyze for intelligent insights.
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