Controlling environmental conditions and monitoring plant status in greenhouses is critical to promptly making appropriate management decisions aimed at promoting crop production. The primary objective of this research study was to develop and test a smart aeroponic greenhouse on an experimental scale where the status of Geranium plant and environmental conditions are continuously monitored through the integration of the internet of things (IoT) and artificial intelligence (AI). An IoT-based platform was developed to control the environmental conditions of plants more efficiently and provide insights to users to make informed management decisions. In addition, we developed an AI-based disease detection framework using VGG-19, InceptionResNetV2, and InceptionV3 algorithms to analyze the images captured periodically after an intentional inoculation. The performance of the AI framework was compared with an expert's evaluation of disease status. Preliminary results showed that the IoT system implemented in the greenhouse environment is able to publish data such as temperature, humidity, water flow, and volume of charge tanks online continuously to users and adjust the controlled parameters to provide an optimal growth environment for the plants. Furthermore, the results of the AI framework demonstrate that the VGG-19 algorithm was able to identify drought stress and rust leaves from healthy leaves with the highest accuracy, 92% among the other algorithms.

-- Accurate temporal reconstructions of plant growth are essential for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline. Our method begins by reconstructing Gaussian Splats from multi-view camera data, then leverages a two-stage registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species.

We developed a simulator to quantify the effect of changes in environmental parameters on plant growth in precision farming. Our approach combines the processing of plant images with deep convolutional neural networks (CNN), growth curve modeling, and machine learning. As a result, our system is able to predict growth rates based on environmental variables, which opens the door for the development of versatile reinforcement learning agents.

Off the Italian coast, something amazing is happening under the surface of the sea. Resembling a bloom of giant jellyfish anchored to the ocean floor, a new form of agriculture is growing in Nemo's Garden. These large clear domes, or biospheres, are a unique new type of underwater greenhouse. These biospheres harness the positive environmental factors of the ocean – temperature stability, evaporative fresh-water generation, CO2 absorption, and natural protection from pests – to create an environment ideal for growing all manner of produce. The brainchild behind Nemo's Garden is Sergio Gamberini, president of diving equipment manufacturer Ocean Reef, who in 2012 was challenged by a friend to combine his experiencing designing diving equipment with his passion for gardening – but he had no idea that it would turn into a new business with a vision of creating more food for the population.

Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach's core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time-series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.

The recent advancements in Computer Vision technology are upgrading the overview of its applications in agriculture. The combination of Artificial Intelligence, Computer Vision, and Machine Vision is making the world of farming more advanced than humankind ever discovered. Employing Agri-Tech is disrupting the traditional dynamics of farming by helping farmers in better crop yielding. Using the framing robots to plant diagnosis applications, Computer Vision and Deep Learning models bring tremendous change. As the prominence of Computer Vision in agriculture continues to grow, the Zion market research has estimated that this technology can provide a sustainable future for farming.

Scientists from Skoltech have trained neural networks to evaluate and predict the plant growth pattern taking into account the main influencing factors and propose the optimal ratio between the nutrient requirements and other growth-driving parameters. The results of the study were published in the IEEE journal Transactions on Instrumentations and Measurements. Over the past few years, multiple attempts have been made to use artificial intelligence (AI) in nearly all spheres of life. It has proven useful, helping people to make the right decisions and achieve the goal. Using AI to grow plants in artificial environments is no exception.

Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented.

Stretchable plant wearables and smart tags dropped by drones aim to help give farming a big data makeover. The relatively cheap technologies for mass monitoring of individual plants across large greenhouses or crop fields could get field tests in three countries starting in 2019. The idea came from researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia with expertise in flexible electronics. After talking with colleagues who were cultivating genetically engineered plants in greenhouses, they recognized the need for inexpensive sensors that could be deployed en masse and report on individual plant conditions. Their early offerings include a stretchable sensor for measuring micrometer-level changes in plant growth and a "PlantCopter" temperature and humidity sensor designed to be dropped from a drone and corkscrew its way through the air for a gradual descent.

A group of scientists from the Space Center (SC) and the Center for Data-Intensive Science and Engineering (CDISE) at Skoltech have developed a method for predicting an increase in plant biomass using 2-D and 3-D images. Their findings will help improve the efficiency of precision farming, both on Earth and in space. The results of their research were presented at the IEEE Instrumentation and Measurement Technology Conference and accepted for publication in a special issue of IEEE Pervasive Computing. With the rising global population, pervasive agriculture research is highly relevant. High technology opens up broad horizons for combating hunger in developing countries, enhancing food security, mitigating human impact on the environment and making agriculture more cost-effective.