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Intelligent Systems in Precision Agriculture: Data, Applications and Techniques

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 11430

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Special Issue Editors

Prof. Dr. Myung Hwan Na

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Guest Editor

Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea
Interests: productivity; machine learning; big data analysis; climate change; computer vision; statistical method; smart farm

Prof. Dr. Wanhyun Cho

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Guest Editor

Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea
Interests: pattern recognition; big data analysis; 3D image; multispectral image

Dr. Inseop Na

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Guest Editor

Associate Professor, Division of Culture Contents, Graduate School of Data Science, AI Convergence and Open Sharing System, Chonnam National University, Republic of Korea
Interests: object/image detection; segmentation; recognition; tracking; image understanding; action/behavior/gesture recognition; emotion recognition
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Special Issue Information

Dear Colleagues,

In a globalized world, the need to produce more agricultural and livestock products, including quality grains, meats, fruits and vegetables, has increased exponentially in recent decades to meet the growing demands of the world's population. These expectations are being met through active action at several levels, but primarily through the introduction of new technologies in sectors such as agriculture, livestock and even fisheries.

In this context, it is possible only through the establishment of a state-of-the-art automatic system using calculation models or computer programs for the cultivation and breeding conditions of grains or livestock, such as the environment, climate, soil, greenhouses and breeding facilities. It is necessary to promote the research and dissemination of results in the field of automation and agricultural productivity improvement using state-of-the-art artificial intelligence technology, especially in relation to facility farm building and management, water use and environmental conditions in open land, information systems and precision agriculture and processing and follow-up management. This includes the development of grain yield prediction technology and management, livestock raising and disease and weight prediction technology and fruit and vegetable cultivation systems (greenhouse structure and environmental control, plant protection and horticulture, horticultural harvesting and fruit and vegetable cultivation).

This Special Issue focuses on the development of all the advanced technologies in agriculture, animal husbandry and fisheries to produce high-quality crops and high-quality meat and fish. For this reason, we are targeting new technology development research content that examines the building of new automation systems by applying statistical models or machine learning and deep learning technologies of all big data obtained from the agriculture, livestock and fishing industries. We also welcome highly interdisciplinary collaborations in disparate research fields, including agricultural design, computation and modeling, environmental issues and even bioengineering and cultivation technologies.

Prof. Dr. Myung Hwan Na
Prof. Dr. Wanhyun Cho
Dr. In Seop Na
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Prof. Dr. Myung Hwan Na
Prof. Dr. Wanhyun Cho
Dr. In Seop Na
Guest Editors

Manuscript Submission Information

Keywords

productivity
machine learning
big data analysis
climate change
computer vision
statistical method
smart farm

Published Papers (9 papers)

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Research

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22 pages, 12086 KiB

Open AccessArticle

A Korean Cattle Weight Prediction Approach Using 3D Segmentation-Based Feature Extraction and Regression Machine Learning from Incomplete 3D Shapes Acquired from Real Farm Environments

by Chang Gwon Dang, Seung Soo Lee, Mahboob Alam, Sang Min Lee, Mi Na Park, Ha-Seung Seong, Min Ki Baek, Van Thuan Pham, Jae Gu Lee and Seungkyu Han

Agriculture 2023, 13(12), 2266; https://doi.org/10.3390/agriculture13122266 - 12 Dec 2023

Viewed by 1059

Abstract

Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. Firstly, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment 3D mesh data into two dominant parts: torso and center body. From these segmented parts, the body length, chest girth, and chest width of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, polynomial regression, random forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.85% using the random forest regression model. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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15 pages, 2042 KiB

Open AccessArticle

Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth

by Myung Hwan Na, Wanhyun Cho, Sora Kang and Inseop Na

Agriculture 2023, 13(10), 1895; https://doi.org/10.3390/agriculture13101895 - 27 Sep 2023

Viewed by 841

Abstract

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. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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15 pages, 453 KiB

Open AccessArticle

Analysis of the Fruit Drop Rate Caused by Typhoons Using Meteorological Data

by Su-Hoon Choi, So-Yeon Park, Ung Yang, Beomseon Lee, Min-Soo Kim and Sang-Hyun Lee

Agriculture 2023, 13(9), 1800; https://doi.org/10.3390/agriculture13091800 - 12 Sep 2023

Viewed by 773

Abstract

Typhoons, which are a common natural disaster in Korea, have seen a rapid increase in annual economic losses over the past decade. The objective of this study was to utilize historical crop insurance records to predict fruit drop rates caused by typhoons from 2016 to 2021. A total of 1848 datasets for the fruit drop rate were generated based on the impact of 24 typhoons on 77 cities with typhoon damage histories. Three different types of measures—the average value, the maximum or minimum value, and the value at a specific point during the typhoon—were applied to four meteorological factors, yielding a total of twelve variables used as model inputs. The predictive performance of the proposed models was compared using five evaluation metrics, and SHAP analysis was employed to assess the contribution of predictor variables to the model output. The most significant variable in explaining the vulnerability to typhoons was found to be the maximum wind speed. The categorical boosting model outperformed the other models in all evaluation metrics, except for the mean absolute error. The proposed model will assist in estimating the potential crop loss caused by typhoons, thereby aiding in the establishment of mitigation strategies for the main crop-producing areas. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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14 pages, 463 KiB

Open AccessArticle

A Smart Farm DNN Survival Model Considering Tomato Farm Effect

by Jihun Kim, Il Do Ha, Sookhee Kwon, Ikhoon Jang and Myung Hwan Na

Agriculture 2023, 13(9), 1782; https://doi.org/10.3390/agriculture13091782 - 08 Sep 2023

Viewed by 1173

Abstract

Recently, smart farming research based on artificial intelligence (AI) has been widely applied in the field of agriculture to improve crop cultivation and management. Predicting the harvest time (time-to-harvest) of crops is important in smart farming to solve problems such as planning the production schedule of crops and optimizing the yield and quality. This helps farmers plan their labor and resources more efficiently. In this paper, our concern is to predict the time-to-harvest (i.e., survival time) of tomatoes on a smart farm. For this, it is first necessary to develop a deep learning modeling approach that takes into account the farm effect on the tomato plants, as each farm has multiple tomato plant subjects and outcomes on the same farm can be correlated. In this paper, we propose deep neural network (DNN) survival models to account for the farm effect as a fixed effect using one-hot encoding. The tomato data used in our study were collected on a weekly basis using the Internet of Things (IoT). We compare the predictive performance of our proposed method with that of existing DNN and statistical survival modeling methods. The results show that our proposed DNN method outperforms the existing methods in terms of the root mean squared error (RMSE), concordance index (C-index), and Brier score. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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13 pages, 1396 KiB

Open AccessArticle

Influence Relationship between Online News Articles and the Consumer Selling Price of Agricultural Products—Focusing on Onions

by Jiyoung Ha, Seunghyun Lee and Sangtae Kim

Agriculture 2023, 13(9), 1707; https://doi.org/10.3390/agriculture13091707 - 29 Aug 2023

Viewed by 959

Abstract

This study aimed to verify the influence relationship between the news articles on onions produced in Korea and the consumer selling price of onions. The analysis methods were the LDA topic modeling technique and the multiple regression analysis. As a result of the analysis, a total of eight topics were found in onion-related news articles. This study analyzed which articles out of the eight topics affected the consumer selling price of onions. As a result, Topic 1 (hypermarket onion sales-related articles), Topic 5 (onion supply and demand stabilization measures), and Topic 6 (inflation) had a statistically significant influence relationship. These results meant that as the number of hypermarket-related articles increased, the consumer selling price increased, and as the macroeconomic articles such as supply and demand stabilization measures and inflation increased, the selling price decreased. The significance of this study was that it revealed that news articles related to onions did not affect the selling price in the consumer market as a whole, and that only the articles directly related to the consumption market (distributors, macroeconomic indicators, etc.) had an effect. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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13 pages, 9184 KiB

Open AccessArticle

SSMDA: Self-Supervised Cherry Maturity Detection Algorithm Based on Multi-Feature Contrastive Learning

by Rong-Li Gai, Kai Wei and Peng-Fei Wang

Agriculture 2023, 13(5), 939; https://doi.org/10.3390/agriculture13050939 - 25 Apr 2023

Cited by 1 | Viewed by 1415

Abstract

Due to the high cost of annotating dense fruit images, annotated target images are limited in some ripeness detection applications, which significantly restricts the generalization ability of small object detection networks in complex environments. To address this issue, this study proposes a self-supervised cherry ripeness detection algorithm based on multi-feature contrastive learning, consisting of a multi-feature contrastive self-supervised module and an object detection module. The self-supervised module enhances features of unlabeled fruit images through random contrastive augmentation, reducing interference from complex backgrounds. The object detection module establishes a connection with the self-supervised module and designs a shallow feature fusion network based on the input target scale to improve the detection performance of small-sample fruits. Finally, extensive experiments were conducted on a self-made cherry dataset. The proposed algorithm showed improved generalization ability compared to supervised baseline algorithms, with better accuracy in terms of mAP, particularly in detecting distant small cherries. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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18 pages, 6997 KiB

Open AccessArticle

Design of 4UM-120D Electric Leafy Vegetable Harvester Cutter Height off the Ground Automatic Control System Based on Incremental PID

by Wenming Chen, Lianglong Hu, Gongpu Wang, Jianning Yuan, Guocheng Bao, Haiyang Shen, Wen Wu and Zicheng Yin

Agriculture 2023, 13(4), 905; https://doi.org/10.3390/agriculture13040905 - 20 Apr 2023

Cited by 2 | Viewed by 1505

Abstract

In this study, a 4UM-120D electric leafy vegetable harvester was employed as the research object. An automatic control system was created to maintain the cutter’s height above the ground within ±2% of the desired value. The intention was to reduce the operators’ work intensity while improving the leafy vegetable harvester’s working quality. The automatic control system for the cutter height from the ground was explained, along with its structure and operating philosophy. MATLAB was used to establish the two-phase hybrid stepper motor’s mathematical electrical equation and mechanical equation models. An analysis was carried out on the fundamentals and differences between position PID and incremental PID control algorithms. Utilizing incremental PID in combination, the control strategy for the harvester cutter height from the ground was built, and an automatic control system was produced under the corresponding control strategy. The stability, accuracy, and rapidity of the automatic control system of the cutter height from the ground under the incremental PID control strategy were analyzed by simulating different actual working conditions with MATLAB/Simulink and taking the steady-state transition time as the evaluation index. The test results show that when the deviation between the current value and the set value was greater than 2%—that is, when the harvester was in the condition of suddenly crossing the ditch or suddenly climbing the slope—the automatic control system based on the incremental PID control strategy had a good dynamic response performance and stability. This resulted in the automatic control function of the harvester cutter height off the ground being achieved. When the rotation angle PID control algorithm’s proportional coefficient is Kp = 4.665, the rotation speed PID control algorithm’s proportional coefficient is Kp = 5.65 and its integral coefficient is Ki = 3.86, and the current PID control algorithm’s proportional coefficient is Kp = 0.5455 and its integral coefficient is Ki = 30.4578. The harvester abruptly crossed a ditch while operating steadily, and the automatic control system’s steady-state transition time for the height of the cutter off the ground was 1.0811 s. The harvester abruptly climbed a slope while operating steadily, and the automatic control system’s steady-state transition time for the height of the cutter off the ground was 1.1185 s. Data from the field tests revealed a degree of reliability in the simulation test results. The study offered a strategy for raising the harvester quality for leafy vegetables while lowering the operator workload. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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22 pages, 8149 KiB

Open AccessArticle

Design and Experiment of an Automatic Row-Oriented Spraying System Based on Machine Vision for Early-Stage Maize Corps

by Kang Zheng, Xueguan Zhao, Changjie Han, Yakai He, Changyuan Zhai and Chunjiang Zhao

Agriculture 2023, 13(3), 691; https://doi.org/10.3390/agriculture13030691 - 16 Mar 2023

Cited by 3 | Viewed by 1819

Abstract

Spraying pesticides using row alignment in the maize seedling stage can effectively improve pesticide utilization and protect the ecological environment. Therefore, this study extracts a guidance line for maize crops using machine vision and develops an automatic row-oriented control system based on a high-clearance sprayer. First, the feature points of crop rows are extracted using a vertical projection method. Second, the candidate crop rows are obtained using a Hough transform, and two auxiliary line extraction methods for crop rows based on the slope feature outlier algorithm are proposed. Then, the guidance line of the crop rows is fitted using a tangent formula. To greatly improve the robustness of the vision algorithm, a Kalman filter is used to estimate and optimize the guidance line to obtain the guidance parameters. Finally, a visual row-oriented spraying platform based on autonomous navigation is built, and the row alignment accuracy and spraying performance are tested. The experimental results showed that, when autonomous navigation is turned on, the average algorithm time consumption of guidance line detection is 42 ms, the optimal recognition accuracy is 93.3%, the average deviation error of simulated crop rows is 3.2 cm and that of field crop rows is 4.36 cm. The test results meet the requirements of an automatic row-oriented control system, and it was found that the accuracy of row alignment decreased with increasing vehicle speed. The innovative spray performance test found that compared with the traditional spray, the inter-row pesticide savings were 20.4% and 11.4% overall, and the application performance was significantly improved. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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18 pages, 8317 KiB

Open AccessTechnical Note

Development of Seeding Rate Monitoring System Applicable to a Mechanical Pot-Seeding Machine

by Seung-Jun Kim, Hyeon-Seung Lee, Seok-Joon Hwang, Jeong-Hun Kim, Moon-Kyeong Jang and Ju-Seok Nam

Agriculture 2023, 13(10), 2000; https://doi.org/10.3390/agriculture13102000 - 15 Oct 2023

Viewed by 812

Abstract

In this study, we developed a monitoring system to accurately track the seeding rate and to identify the locations where the mechanical pot-seeding machine failed to sow seeds correctly. The monitoring system employs diverse image processing techniques, including the Hough transform, hue–saturation–value color space conversion, image morphology techniques, and Gaussian blur, to accurately pinpoint the seeding rate and the locations where seeds are missing. To determine the optimal operating conditions for the seeding rate monitoring system, a factorial experiment was conducted by varying the brightness and saturation values of the image data. When the derived optimal operating conditions were applied, the system consistently achieved a 100% seed recognition rate across various seeding conditions. The monitoring system developed in this study has the potential to significantly reduce the labor required for supplementary planting by enabling the real-time identification of locations where seeds were not sown during pot-seeding operations. Full article

(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)

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