AI-based Pest detection in hydroponics farming

AI-based pest detection in hydroponics farming involves using artificial intelligence and computer vision techniques to identify and classify plant pests and diseases. 

Here are more specific details on how it works:

1. Image Acquisition: High-resolution images of plants are captured using cameras or smartphones. These images can be taken at regular intervals or when specific triggers, such as changes in environmental conditions, are met.
2. Image Preprocessing: The images are preprocessed to enhance their quality and remove any noise or unwanted elements before analysis. Preprocessing may involve resizing, cropping, filtering, or adjusting the color balance.
3. Feature Extraction: AI algorithms extract relevant features from the preprocessed images. These features may include color, texture, shape, size, and spatial distribution of plant parts.
4. Training Data Preparation: A large dataset of images is required for training the AI model. The dataset should include labeled images of healthy plants and images of plants affected by various pests and diseases.
5. Model Training: The AI model is trained using machine learning techniques like convolutional neural networks (CNNs). The model learns to recognize patterns and features associated with different pests and diseases during training.
6. Classification and Prediction: Once the AI model is trained, it can accurately classify new images of plants into various categories, such as healthy, infected with a specific pest, or showing symptoms of a particular disease.
7. Real-Time Detection: As new images are captured, the trained AI model can rapidly analyze them in real time, detecting any signs of pests or diseases. The system can trigger alerts to notify the grower if an issue is identified.
8. Continuous Learning: The AI model can be continuously updated and improved with more data over time. This makes the system more accurate and adapts to new pests or disease strains.

Benefits of AI-Based Pest Detection

Early Detection: AI can identify pests and diseases at their early stages, enabling prompt intervention before significant damage occurs.

Precision: AI algorithms can distinguish between different pests and diseases, providing specific identification and targeted treatment options.

Automation: The system operates automatically, reducing the need for manual monitoring and saving time for growers.

Scalability: AI-based pest detection can be deployed in large-scale hydroponic setups, covering extensive growing areas effectively.

Cost-Effectiveness: Once the AI model is trained, the ongoing operational costs are relatively low compared to labor-intensive manual scouting.

Further reading

A Beginner’s Guide to Hydroponics Farming: From Seed to Harvest

Different types of hydroponics systems and how they work

Challenges of Hydroponics Farming and how to overcome them 

The Potential for Combining Hydroponics and Crop Circle Farming with Traditional Practices

Technologies in Hydroponics – Automation, Control and High Performance

Automation in harvesting and processing of hydroponic crops

AI-based Pest detection in hydroponics farming

References :

• Tao, L. Zhao, G. Wang, and R. Liang, “Review of the internet of things communication technologies in smart agriculture and challenges,” Computers and Electronics in Agriculture, vol. 189, article 106352, 2021.
• Qazi, B. A. Khawaja, and Q. U. Farooq, “IoT-equipped and AI-enabled next generation smart agriculture: a critical review, current challenges and future trends,” IEEE Access, vol. 10, pp. 21219–21235, 2022.
• Das, J. Sahoo, M. B. Raza, M. Barman, and R. Das, “Ongoing soil potassium depletion under intensive cropping in India and probable mitigation strategies. A review,” Agronomy for Sustainable Development, vol. 42, no. 1, pp. 1–26, 2022.
• DIGITAL AGRICULTURE 2 – e-Book June 2023 – Marco Brini 33 MARCO BRINI linkedin.com/in/marcobrini/
• Sharma, M. Georgi, M. Tregubenko, A. Tselykh, and A. Tselykh, “Enabling smart agriculture by implementing artificial intelligence and embedded sensing,” Computers & Industrial Engineering, vol. 165, article 107936, 2022.
• Adidrana, A. R. Iskandar, A. Nurhayati et al., “Simultaneous hydroponic nutrient control automation system based on Internet of Things,” JOIV: International Journal on Informatics Visualization, vol. 6, no. 1, pp. 124–129, 2022.
• G. Rezk, E. E.-D. Hemdan, A.-F. Attia, A. El-Sayed, and M. A. El-Rashidy, “An efficient IoT based smart farming system using machine learning algorithms,” Multimedia Tools and Applications, vol. 80, no. 1, pp. 773–797, 2021.
• Manikandan, S. Prabhu, P. Chakraborty, T. Manthra, and M. Kumaravel, “IoT-Based Smart Irrigation and Monitoring System in Smart Agriculture,” Futuristic Communication and Network Technologies, Springer, Singapore, pp. 45–56, 2022.