• Journal of Bio-Environment Control
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• v.33 no.2
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• pp.120-128
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• 2024

Since ChatGPT was released in 2022, the generative artificial intelligence (AI) industry has seen massive growth and is expected to bring significant innovations to cognitive tasks. AI-based image generation, in particular, is leading major changes in the digital world. This study investigates the technical foundations of Midjourney, Stable Diffusion, and Firefly-three notable AI image generation tools-and compares their effectiveness by examining the images they produce. The results show that these AI tools can generate realistic images of tomatoes, strawberries, paprikas, and cucumbers, typical crops grown in greenhouse. Especially, Firefly stood out for its ability to produce very realistic images of greenhouse-grown crops. However, all tools struggled to fully capture the environmental context of greenhouses where these crops grow. The process of refining prompts and using reference images has proven effective in accurately generating images of strawberry fruits and their cultivation systems. In the case of generating cucumber images, the AI tools produced images very close to real ones, with no significant differences found in their evaluation scores. This study demonstrates how AI-based image generation technology can be applied in agriculture, suggesting a bright future for its use in this field.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.229-238
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• 2022

Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.7
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• pp.949-956
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• 2005

The weekly ultrasound scanner (US) observations of reproductive organs in a commercial dairy herd with the popular stanchion style management were conducted for over 26 months. Based on reproductive records, the following were evaluated: 1) the effect of postpartum period commencement of US monitoring on herd reproductive efficacy, and 2) the effectiveness of a US monitoring-based diagnosis and subsequent treatments of reproductive disorders on postpartum reproductive efficiency. The reproductive parameters of cows, which were subjected to US monitoring between Days 30-40 (Day 0 = day of parturition), Days 41-50, Days 51-60, and above Day 61, were compared. The reproductive parameters of cows diagnosed as having reproductive disorders (RD) with US monitoring before or after the first artificial insemination (AI) were also compared. It was found that the day of commencement of US monitoring in cows diagnosed with and without RD significantly affected the period towards the first AI and the open period. In particular, cystic follicles and anoestrus detected either before or after the first AI significantly affected herd reproductive efficiency. The implementation of US monitoring improved reproductive efficiency by reducing the open period and increasing the number of milking cows in the herd. The results of this field trial indicate that the postpartum reproductive management of dairy cows with the use of the US monitoring system is one strategy to improve reproductive efficiency, especially in a high-yielding dairy herd reared stanchion management style.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.883-894
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• 2024

This study aims to develop an AI-based aquaculture water quality monitoring and control system. Reliable and durable sensors were developed through the design of embedded boards and PCB fabrication, and communication modules were integrated for data collection and transmission. Water quality data from various tanks were collected and analyzed using machine learning techniques to build predictive and control models for water quality changes. The results showed that the AI-based water quality control system demonstrated high prediction accuracy and was effective in real-time monitoring and controlling the water quality.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.9
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• pp.1268-1273
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• 2003

Sixty postpartum lactating Friesian cows in 3 treatments at a commercial dairy farm were used to study the effect of using progesterone supplementation with GnRH and PGF2$\alpha$ synchronization with and without timed AI on fertility during summer. Cows in treatment1($Tr_1$) and treatment2 ($Tr_1$) were fitted with progesterone releasing intravaginal device (PRID) device and injected with 10 g GnRH agonist on $51{\pm}3$ d postpartum (pp). Seven days later, PRID was removed and cows received 25 mg PGF2$\alpha$. Two days later, $Tr_1$ cows received another injection of 10 g GnRH and timed AI 16-20 h later. Control cows received only 25 mg PGF2$\alpha$ $58{\pm}3d\;pp$. $Tr_2$ and control cows were AI at detected estrus. Serum progesterone for all cows was determined on days of injection, AI and 21, 23 and 28 d postinsemination. Pregnancy rates from first AI based on serum P4 concentrations on d 21, 23 and 28 postinsemination (50, 40 and 35%) and that based on rectal palpation 40-45 d postinsemination (30, 15 and 15% for $Tr_1$, $Tr_2$ and control cows, respectively) did not differ among the three groups. Whereas, pregnancy rate at 120 d pp for $Tr_1$ (65%) was higher (p<0.05) than that in $Tr_2$ (30%) or control (30%). The overall pregnancy rate was not significantly different (90, 90 and 75% for $Tr_1$, $Tr_2$ and control, respectively). Days open for cows in $Tr_1$ ($100.3{\pm}9$) was less (p<0.03) than that in $Tr_2$ ($130.9{\pm}9$) or control ($135.1{\pm}10$). Results indicate that using PRID device with Ovsynch program had significantly increased pregnancy rate and decreased days open compared to AI at detected estrus after synchronization with GnRH, PRID and PGF2$\alpha$ or synchronization with one injection of PGF2$\alpha$.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1669-1683
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• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.1027-1034
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• 2024

This study aims to address the issues of outliers and missing values in AI-based smart farming to improve data quality and enhance the accuracy of agricultural predictive activities. By utilizing real data provided by the Rural Development Administration (RDA) and the Korea Agency of Education, Promotion, and Information Service in Food, Agriculture, Forestry, and Fisheries (EPIS), outlier detection and missing value imputation techniques were applied to collect and manage high-quality data. For successful smart farm operations, an IoT-based AI automatic growth measurement model is essential, and achieving a high data quality index through stable data preprocessing is crucial. In this study, various methods for correcting outliers and imputing missing values in growth data were applied, and the proposed preprocessing strategies were validated using machine learning performance evaluation indices. The results showed significant improvements in model performance, with high predictive accuracy observed in key evaluation metrics such as ROC and AUC.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8_spc
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• pp.1284-1295
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• 2019

Considerable progress in reproduction of dairy goats has been made, with advances in reproductive technology accelerating dairy goat production since the 1980s. Reproduction in goats is described as seasonal. The onset and length of the breeding season is dependent on various factors such as breed, climate, physiological stage, male effect, breeding system, and photoperiod. The reproductive physiology of goats was investigated extensively, including hypothalamic and pituitary control of the ovary related to estrus behavior and cyclicity etc. Photoperiodic treatments coupled with the male effect allow hormone-free synchronization of ovulation, but the kidding rate is still less than for hormonal treatments. Different protocols have been developed to meet the needs and expectations of producers; dairy industries are subject to growing demands for year round production. Hormonal treatments for synchronization of estrus and ovulation in combination with artificial insemination (AI) or natural mating facilitate out-of-season breeding and the grouping of the kidding period. The AI with fresh or frozen semen has been increasingly adopted in the intensive production system, this is perhaps the most powerful tool that reproductive physiologists and geneticists have provided the dairy goat industry with for improving reproductive efficiency, genetic progress and genetic materials transportation. One of the most exciting developments in the reproduction of dairy animals is embryo transfer (ET), the so-called second generation reproductive biotechnology following AI. Multiple ovulation and ET (MOET) program in dairy goats combining with estrus synchronization (ES) and AI significantly increase annual genetic improvement by decreasing the generation interval. Based on the advances in reproduction technologies that have been utilized through experiments and investigation, this review will focus on the application of these technologies and how they can be used to promote the dairy goat research and industry development in the future.