Artificial Intelligence in Greenhouses
(book excerpts)Artificial intelligence (AI) in agriculture represents a fundamental shift from traditional farming methods, enabling more innovative, more efficient practices. This transformative power of AI is reshaping how we approach greenhouse challenges and opportunities. Artificial intelligence is a branch of computer science that employs machine and deep learning algorithms, among others, to learn from and interpolate data to mimic human intelligence. The foundation of AI is the assumption that human intellect can be described in a way that makes it simple for a computer to duplicate and carry out activities of all sizes. Learning, reasoning, and perception are all goals of AI. Artificial intelligence is having a significant impact across the board in greenhouse agriculture. On an operational level, AI uses machine learning, machine vision, robotics, fuzzy logic, image processing, wireless sensor networks, and other cutting-edge technologies to tackle agricultural challenges. The current scope of AI in smart greenhouses encompasses artificial lighting using LEDs, air ventilation, water and nutrition pumps, plant nutrition, humidity control, moisture control, and temperature control subsystems with a unique combination of sensors and controls with distinct attributes. This method, grounded in data, allows for the meticulous application of resources such as water, fertilizers, and pesticides, leading to a reduction in waste and an increase in crop productivity. In addition, AI's predictive capabilities extend to anticipating weather patterns, pest attacks, and potential crop diseases. This foresight enables farmers to proactively safeguard their crops, ensuring better yield protection. The integration of AI in these processes not only boosts agricultural efficiency but also contributes to more sustainable management practices by optimizing resource use and minimizing environmental impact.
Click on the following topics for more information on artificial intelligence in greenhouses.
Topics Within This Chapter:
- Machine Learning
- Machine Learning Algorithms
- Deep Learning
- Fuzzy Logic
- Machine Vision
- Machine Vision Algorithms
- Machine Vision Image Training Datasets
- Image Annotation
- Robotics
- Common Types of Robots
- Autonomous Mobile Robots (AMRs)
- Automated Guided Vehicles (AGVs)
- Articulated Robots (Robotic Arms)
- Cobots
- Robotic System Components
- Robotic Arms
- End Effectors
- Types of Robotic Sensors
- Artificial Intelligence Applications in Crop Fields Versus Greenhouses
- Applications of Artificial Intelligence in Greenhouses
- Crop Monitoring
- Irrigation Management
- Insect and Disease Detection
- Automatic Insect Detection Using AI
- Mobile App Technologies
- Pesticide Spraying with Robots
- Harvesting
- Crop Sorting and Grading
- Energy Efficiency in Greenhouses
- Supply Chain and Demand Forecasting
- Physical Security
