Understanding plant growth dynamics is essential for applications in agriculture and plant phenotyping. We present the Growth Modelling (GroMo) challenge, which is designed for two primary tasks: (1) plant age prediction and (2) leaf count estimation, both essential for crop monitoring and precision agriculture. For this challenge, we introduce GroMo25, a dataset with images of four crops: radish, okra, wheat, and mustard. Each crop consists of multiple plants (p1, p2, ..., pn) captured over different days (d1, d2, ..., dm) and categorized into five levels (L1, L2, L3, L4, L5). Each plant is captured from 24 different angles with a 15-degree gap between images. Participants are required to perform both tasks for all four crops with these multiview images. We proposed a Multiview Vision Transformer (MVVT) model for the GroMo challenge and evaluated the crop-wise performance on GroMo25. MVVT reports an average MAE of 7.74 for age prediction and an MAE of 5.52 for leaf count. The GroMo Challenge aims to advance plant phenotyping research by encouraging innovative solutions for tracking and predicting plant growth. The GitHub repository is publicly available at https://github.com/mriglab/GroMo-Plant-Growth-Modeling-with-Multiview-Images.

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

The design made it possible to make powerful language models like term "Generative AI" is used to describe a subset of AI models OpenAI's GPT series, which included GPT-2 and GPT-3, that can generate new information by discovering relevant which were the versions that came before ChatGPT [6]. The trends and patterns in already collected information. These GPT-3.5 architecture is the basis for ChatGPT; it is an models may produce work in a wide range of media, from improved version of OpenAI's GPT-3 model. Even though written to visual to audio [2]. To analyse, comprehend, and GPT-3.5 has fewer variables, nevertheless produces excellent produce material that accurately imitates human-generated results in many areas of NLP, such as language understanding, outcomes, Generative AI models depend on deep learning text generation, and machine translation [6]. ChatGPT was approaches and neural networks. OpenAI's ChatGPT is one trained on a massive body of text data and fine-tuned on the such AI model that has quickly become a popular and versatile goal of creating conversational replies, allowing it to create resource for a number of different industries. Its humanoid text responses to user inquiries that are strangely similar to those of generation is made possible by its foundation in the Generative a person.