?? AI for Plant Nurseries: Irrigation, Climate & more
If you'd like to receive the regular 'AI in Agriculture' newsletter in your inbox, simply add your email to my mailing list.
What's On: FREE mobile app Petiole Pro for Plant Nursery
Plant Nursery Tech is an emerging branch of artificial intelligence (AI), which is knocking on your nursery's door right now. Integrating AI into plant nurseries offers significant advantages, from optimizing growing conditions and managing pests to enhancing breeding programs and improving supply chain efficiency.
As AI technology continues to evolve, its applications in plant nurseries will expand, driving further advancements in horticulture.
1. Automated Monitoring and Control Systems
First and foremost in the plant nurseries - environmental monitoring. AI-powered sensors can continuously monitor environmental parameters such as temperature, humidity, light intensity, and soil moisture.
Machine learning algorithms analyze this data to optimize growing conditions, ensuring plants receive the ideal environment for growth. For example, Chinese ???? researchers developed a highly precise predictive model for optimizing the greenhouse microclimate by integrating meteorological data through open-source APIs, employing a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network-based model optimized with the sparrow search algorithm, and enhancing the attention mechanism with Squeeze-and-Excitation (SE) Networks, resulting in significant improvements in predictive accuracy and seedling cultivation efficiency.
The second priority - for precise irrigation systems. Smart irrigation systems use AI to determine the precise water requirements of plants, reducing water wastage and preventing over- or under-watering. These systems can be integrated with weather forecasts to further refine irrigation schedules. For example, researchers from University of Tsukuba ???? developed an IoT-based precision irrigation system for Momotaro tomato seedlings, comparing four soil moisture thresholds (5%, 8%, 12%, and 15%) and two irrigation methods (surface and subsurface drip irrigation).
The 12% moisture threshold with subsurface drip irrigation significantly enhanced seedling growth, using 10% less water than surface drip irrigation.
Additionally, in field tests, the subsurface drip system yielded 1243 g/plant compared to 1061 g/plant for surface drip irrigation. The study concluded that the LoRaWAN-based subsurface drip system is efficient and suitable for outdoor tomato production.
2. Pest and Disease Management
Pest and disease detection with IoT-sensors and smartphones is the direction where you need to look for the future.
领英推荐
The most popular way is to applying these technologies for early pest and disease detection. AI-based image recognition systems can identify early signs of pest infestations or diseases by analyzing plant images.
These systems can detect subtle changes in leaf colour, texture, or shape that may indicate problems, allowing for timely interventions. Research team from CREA Ricerca ???? published a review of digital-based detection trends and tools for treating fungal infections.
Technologies were tested on rose plants for diseases like rust and powdery mildew using hyperspectral, multispectral, and thermal imaging, and fluorescence sensors.
On practical level, enhancing these technologies with AI as well as conventional practices can improve disease management and reduce pesticide use. A few figures of this research are provided below.
In the figure above: Fungal disease detection through multispectral imaging on rose. A and C are red–green–blue (RGB) images while B and D are elaborations of the Normalised Difference Vegetation Index (NDVI). White arrows point out a healthy leaf (A,B) or a leaf affected by black spot (C,D). Images were acquired by the Agrowing System and elaborated upon with the Analist 2020 software.
In the figure below: RGB (A) and thermal (B) images of rose plants in greenhouse affected by powdery mildew acquired with an HD FLIR T1030sc thermal camera. White arrows point out a healthy leaf while black arrows a leaf covered by powdery mildew.
?? What's next in AI for Plant Nurseries?
In our next edition we'll look at plant phenotyping and breeding.
Particularly, how AI algorithms can process large datasets to identify desirable traits in plants as well as prediction of genetic potential of plants based on genomic data. Which crops shall we take as an example?
Please put them in the comments ??
Wishes of healthier plants in your nursery,
Maryna Kuzmenko, Ph.D ????, Chief Inspiration Officer at Petiole Pro Community
#plantnursery
Photo credit for cover: A schematic of an IoT-based smart irrigation system for water management in a container-based nursery. Mahmud, M.S.; Zahid, A.; Das, A.K. Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects. Sensors 2023, 23, 1818.
References for "AI for Plant Nurseries: Irrigation, Climate & more"
Engineer || AgTech || Precision Crop Protection Researcher || UAV's
5 个月Looking forward to the next edition, especially the part on plant phenotyping and breeding! Maryna Kuzmenko, Ph.D ????
I like to engage with horticulture operations (greenhouse, growth chambers, vertical farming) and provide opportunities to optimize the plant responses driven by supplemental or sole source lighting.
5 个月AI is a strong tool for driving success in Horticulture applications in various crops. Keep up the good work !
IT Manager na Global Blue Portugal | Especialista em Tecnologia Digital e CRM
5 个月Fascinating content on AI in plant nurseries! Optimizing climate, irrigation, and disease control is crucial for sustainable agriculture. Looking forward to the next edition! ??
Crop Improvement Researcher
5 个月Excellent work Maryna Kuzmenko, Ph.D ???? ??