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(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested.

Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine the models: the weighted regression model (WRM), Gaussian process regression model (GPRM), and Gaussian process panel model (GPPM). A posterior distribution was derived using these parameters, and a weight prediction system was implemented. An experiment was conducted using image data to evaluate model performance. The GPRM with the squared exponential kernel had the best predictive power. Next, GPRMs with polynomial and rational quadratic kernels, the linear model, and WRM had the next-best predictive power. Finally, the GPRM with the linear kernel, the linear model, and the latent growth curve model, and types of GPPM had the next-best predictive power. GPRM and WRM are statistical probability models that apply predictions to the entire cattle population. These models are expected to be useful for predicting cattle growth on farms at a population level. However, GPPM is a statistical probability model designed for measuring the weight of individual cattle. This model is anticipated to be more efficient when predicting the weight of individual cattle on farms.

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms. [ABSTRACT FROM AUTHOR]

Text line segmentation is a critical task in handwritten document recognition. In this paper, we propose a novel text-linesegmentation method using baseline estimation and watershed. The baseline-detection algorithm estimates the baseline using Adaptive Head-Tail Connection (AHTC) on the document. Then, the watershed method segments the line region using the baselinedetection result. Finally, the text lines are separated by watershed result and a post-processing algorithm defines the lines more correctly. The scheme successfully segments text lines with 97% accuracy from the handwritten document images in the ICDAR database. [ABSTRACT FROM AUTHOR]