This paper is a study on a modular smart plant factory integrating intelligent solar module, LED module with high efficiency for plant growth, IoT module control system and image processing technology. The intelligent sun and modules have a corrugated structure, and the angle of the module can be adjusted to obtain a large amount of power generation. It is fully foldable for wider angles during the day and module protection at night. The LED module is designed and manufactured to distribute energy evenly over the entire wavelength range so that high efficiency can be obtained. The control system with IoT convergence technology enables control of all parts related to plant growth such as angle control of solar modules, LED lighting control, temperature/humidity control, and fan control. In particular, the control method is programmed to be controlled by a computer monitoring system and a smartphone app, so there are few places. In addition, this paper developed an image processing algorithm to extract the growth information of lettuce grown in the plant factory. The acquired images were separated into R, G, and B images using Matlab software. The applied algorithms are k-mean and improved morphological image processing. By applying this method, we can determine the area calculation and shipping of lettuce seedlings. As a result of the fusion and application of solar modules, LED modules, and IoT modules, information on plant growth and status was confirmed.

Background: Soybean cultivation faces challenges from pest infestations, necessitating advanced and proactive pest management strategies. Traditional methods often lag in early detection, resulting in substantial crop losses. This study addresses this gap by employing machine learning, specifically sequential convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to transform early pest detection in soybean fields. The approach leverages a comprehensive dataset comprising high-resolution crop images and environmental variables, offering insights into soybean ecosystems. Methods: Using CNNs for picture processing and RNNs for collecting temporal relationships in environmental factors, a complex deep learning model is created. The dataset contains 1050 images of soybeans including pests such as Anticarsia, Coccinellidae and without pests (healthy). The model is trained for 20 epochs. The model has been carefully validated for accuracy, sensitivity and efficacy in early insect identification. The diversity of the dataset ensures that the model may be adjusted to a range of soybean growing situations. Result: The outcomes demonstrate the model’s unparalleled effectiveness, routinely outperforming conventional techniques with an accuracy rate of 95%. Its exceptional sensitivity reduces financial and environmental expenses, highlighting its versatility in a range of soybean growing environments. In light of the difficult global agricultural landscape, this study offers a novel strategy for proactive and sustainable pest management, which is essential to guaranteeing strong soybean crop yields.

The sustainable agriculture practices demands new innovations identifying plant diseases and instead of crop disease detection and precision and efficacy. An extensive review of the literature found through PubMed searches indicates a gap in the present approaches, which highlights the need for sophisticated machine learning solutions in the field of plant pathology. This study involves a comprehensive review of relevant publications collected via PubMed searches. The methodology involves the analysis of machine learning algorithms, datasets utilized and techniques applied for plant disease detection. Special attention is given to recent advancements in the field, focusing on the development and optimization of models tailored for precise and reliable disease identification. The study reveals compelling results, underscoring the transformative impact of machine learning innovations on plant disease detection accuracy. Specific algorithms exhibit superior performance, with implications for widespread applications in precision agriculture. These outcomes not only enhance current disease identification capabilities but also lay the groundwork for future advancements in automated and high-precision plant pathology diagnostics. The integration of machine learning emerges as a pivotal force in reshaping the landscape of plant disease detection.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Background: The advancements achieved in artificial intelligence (AI) technology in recent decades have not yet been equaled by agricultural phenotyping approaches that are both rapid and precise. Efficient crop phenotyping technologies are necessary to enhance crop improvement endeavors in order to fulfill the projected demand for food in future. Methods: This work demonstrates a method for non-destructive physiological state phenotyping of plants using cutting-edge image processing methods in conjunction with chlorophyll fluorescence imaging. Key fluorescence metrics, such as fv/fm and NPQ, were extracted from images taken at different phases of development via processing. In addition, this research explores the transformative role of automated image analysis in high-throughput phenotyping of legume traits. A comprehensive examination of recent studies reveals the diverse applications of machine learning and deep learning algorithms in capturing morphological traits, assessing physiological parameters, detecting stress and diseases in various legume species. The comparative analysis underscores the superiority of automated systems over traditional methods, emphasizing scalability and efficiency. Challenges, including algorithm sensitivity and environmental variability, are identified, urging further refinement. Recommendations advocate for standardized metrics, interdisciplinary collaborations and user-friendly platforms to enhance accessibility. As the field evolves, the integration of automated image analysis holds promise for revolutionizing legume phenotyping, accelerating crop improvement and contributing to global food security in sustainable agriculture. Result: The findings demonstrate that the proposed method is effective in illuminating how plants respond to their environment, hence promoting advancements in plant phenotyping and agricultural research

With the rapid development of IT in the modern society, various kinds of information such as smart information age are provided in real time regardless of time and place. Especially, various IT devices and mobile devices using the ubiquitous concept...