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23 pages, 7047 KiB

Open AccessArticle

Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation

by Xiaoyu Xie, Jianchun Hua, Jiahao Ding, Yang Le, Yi Huang, Lizhi Miao and Donglai Jiao

Appl. Sci. 2025, 15(6), 3148; https://doi.org/10.3390/app15063148 - 13 Mar 2025

Viewed by 433

Abstract

To reduce labor costs in aquaculture and enhance the level of automated management, this study designed and developed a multifunctional unmanned boat platform (UBP) by integrating technologies such as sensors, satellite positioning, and artificial intelligence. The platform contains three major modules for data [...] Read more.

To reduce labor costs in aquaculture and enhance the level of automated management, this study designed and developed a multifunctional unmanned boat platform (UBP) by integrating technologies such as sensors, satellite positioning, and artificial intelligence. The platform contains three major modules for data collection, underwater vision, and motion control, enabling functions like cruise path planning, water quality monitoring, identification of aquaculture products, and bait feeding. To verify its reliability and practicality, verification experiments were conducted in the aquaculture area of Lianyungang, China. The results show that the UBP can efficiently distribute feed to an area of 10,000 square meters within 20 min based on feeding points, outperforming the 47 min required for manual feeding. Over a two-month period, the weight of sea cucumbers raised by unmanned boats increased by 67.7% compared to those raised manually, with a 24.33% reduction in feed usage. Additionally, the unmanned boat reduced daily aquaculture costs from 225 RMB to 120.2 RMB, a total reduction of 46.7%. In conclusion, this platform reduces labor costs by improving aquaculture efficiency, and addresses limitations of the existing aquaculture feeding machinery in adaptability and real-time responsiveness, which can provide a feasible solution for aquaculture automation. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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15 pages, 1746 KiB

Open AccessArticle

Effect of Genotype × Environment Interactions on the Yield and Stability of Sugarcane Varieties in Ecuador: GGE Biplot Analysis by Location and Year

by Luis Henry Torres-Ordoñez, Juan Diego Valenzuela-Cobos, Fabricio Guevara-Viejó, Purificación Galindo-Villardón and Purificación Vicente-Galindo

Appl. Sci. 2024, 14(15), 6665; https://doi.org/10.3390/app14156665 - 30 Jul 2024

Cited by 2 | Viewed by 1764

Abstract

Yield and stability are desirable characteristics that crops need to have high agronomic value; sugarcane stands out globally due to its diverse range of products and by-products. However, genotype-environment (G × E) interactions can affect the overall performance of a crop. The objective [...] Read more.

Yield and stability are desirable characteristics that crops need to have high agronomic value; sugarcane stands out globally due to its diverse range of products and by-products. However, genotype-environment (G × E) interactions can affect the overall performance of a crop. The objective of this study is to identify genotypes with the highest yield and stability, as well as to understand their independent and interactive effects. A collection of 10 sugarcane varieties was evaluated, including Colombian, Dominican, Ecuadorian lines, and a group of clones planted across five different locations from 2018 to 2020. A two-way ANOVA along with the GGE biplot technique were used to analyze yield and stability. The ANOVA model shows highly significant effects in all cases (p < 0.001) except for the genotype by year and sector interaction (G × Y × S); however, the decomposition by sectors reveals a significant triple interaction in sector 04 (p < 0.05). The GGE biplot model accounted for up to 74.77% of the total variance explained in its PC1 and PC2 components. It also highlighted the group of clones as having the highest yield and environmental instability, and the Ecuadorian varieties EC-07 and EC-08 as having the best yield-stability relationship. We conclude that the combined results of the ANOVA and GGE biplot models provide a more synergistic and effective evaluation of sugarcane varieties, offering theoretical and practical bases for decision-making in the selection of specific varieties. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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21 pages, 3356 KiB

Open AccessArticle

A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks

by Zekai Cheng, Rongqing Huang, Rong Qian, Wei Dong, Jingbo Zhu and Meifang Liu

Appl. Sci. 2022, 12(15), 7378; https://doi.org/10.3390/app12157378 - 22 Jul 2022

Cited by 18 | Viewed by 2536

Abstract

Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of [...] Read more.

Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of the method is derived from a simplified version of YOLOv3, namely YOLOLite, and k-means++ is utilized to improve the generation process of the prior boxes. In addition, a lightweight sandglass block and coordinate attention are used to optimize the structure of residual blocks. The method was evaluated on the CP15 crop pest dataset. Its detection precision exceeds that of YOLOv3, at 82.9%, while the number of parameters is 5 million, only 8.1% of the number used by YOLOv3, and the number of computations is 9.8 GFLOPs, only 15% of that used by YOLOv3. Furthermore, the detection precision of the method is superior to all other commonly used object detection methods evaluated in this study, with a maximum improvement of 10.6%, and it still has a significant edge in the number of parameters and computation required. The method has excellent pest detection precision with extremely few parameters and computations. It is well-suited to be deployed on equipment for detecting crop pests in agricultural environments. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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14 pages, 3310 KiB

Open AccessArticle

Towards Optimizing Garlic Combine Harvester Design with Logistic Regression

by Zhengbo Zhu, Wei Li, Fujun Wen, Liangzhe Chen and Yan Xu

Appl. Sci. 2022, 12(12), 6015; https://doi.org/10.3390/app12126015 - 13 Jun 2022

Cited by 3 | Viewed by 2409

Abstract

In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel [...] Read more.

In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel and a lifting chain. Each part had unique structural parameters and motion parameters, as different parameters would deeply affect the performance of the machine. A logistical regression algorithm was utilized to analyze the working speed of the reel, the digging depth of the reciprocating cutter and the lifting speed of the lifting chain. This paper also discussed the influence of these three functions on the damage rate based on the collected data when harvesting garlic. Specifically, each function was tested 60 times for collecting data. The experimental results showed that the order of influence of the three functions on the damage rate was the digging depth, working speed and lifting speed. Moreover, the lowest damage rate was 0.18% when the digging depth was 100 mm, the working speed was 1.05 km·h⁻¹ and the lifting speed was 0.69 m·s⁻¹. A validation test was taken out based on the three functions of the analysis results, and the damage rate was 0.83%, which was close to the analysis results, and proved that the analysis results were accurate and meaningful. The research results are beneficial to the development and application of the garlic combine harvester. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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12 pages, 2147 KiB

Open AccessArticle

Hyperspectral Identification of Ginseng Growth Years and Spectral Importance Analysis Based on Random Forest

by Limin Zhao, Shumin Liu, Xingfeng Chen, Zengwei Wu, Rui Yang, Tingting Shi, Yunli Zhang, Kaiwen Zhou and Jiaguo Li

Appl. Sci. 2022, 12(12), 5852; https://doi.org/10.3390/app12125852 - 8 Jun 2022

Cited by 12 | Viewed by 2377

Abstract

The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional [...] Read more.

The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional operations related to the growth year identification method is needed. The characteristics of ginseng reflectance spectral data were analyzed, and the growth year recognition model was constructed by a decision-tree-based random forest machine learning method. After independent verification, the accuracy of distinguishing ginseng food and medicine can reach 92.9%, with 6-year growth as the boundary, and 100%, with 5-year growth as the boundary. The research results show that the spectral change of ginseng is the most obvious in the fifth year, which provides a reference for the key research years based on chemical analyses and other methods. For the application of growth year recognition, the NIR band (1000–2500 nm) had little contribution to the recognition of ginseng growth years, and the band with the largest contribution was 400–650 nm. The recognition model based on machine learning provides a non-destructive, fast, and simple scheme with high accuracy for ginseng year recognition, and the spectral importance analysis conclusion of ginseng growth years provides a design reference for the development of special lightweight spectral equipment for year recognition. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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13 pages, 7580 KiB

Open AccessArticle

Food Risk Entropy Model Based on Federated Learning

by Jiaojiao Yu, Yizhou Chen, Zhenyu Wang, Jin Liu and Bo Huang

Appl. Sci. 2022, 12(10), 5174; https://doi.org/10.3390/app12105174 - 20 May 2022

Cited by 5 | Viewed by 2137

Abstract

The safety of agricultural products is a guarantee of national security. The increasing variety of pesticides used on crops has led to an increasing abundance of pesticide residues in agricultural products, making pesticide residues an important factor in threatening health. Traditional indicators for [...] Read more.

The safety of agricultural products is a guarantee of national security. The increasing variety of pesticides used on crops has led to an increasing abundance of pesticide residues in agricultural products, making pesticide residues an important factor in threatening health. Traditional indicators for evaluating the safety of agricultural products, such as pass rates and residue rates, can only qualitatively describe the level of pesticide residues. Isolated data leads to low data utilization, data is distributed between different terminals or departments and cannot be shared, while the security of private data needs to be ensured. Therefore, we propose a risk entropy model based on federated learning. The model is able to quantitatively describe the risk level of agricultural products and achieve data fusion without exposing private data in the federated learning model. In this paper, a total of 90,510 agricultural product data samples from 2015 to 2019 are collected, with each sample containing 58 indicators. The experimental results show that the developed food safety risk entropy model can quantitatively reflect the level of risk in the target region and time interval. In addition, we have developed a multidimensional data analysis tool based on federated learning, which can achieve data integration across multiple regions and departments. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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Review

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19 pages, 9929 KiB

Open AccessReview

Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer

by Haixia Qi, Zihong Chen, Guangsheng Liang, Riyao Chen, Jinzhuo Jiang and Xiwen Luo

Appl. Sci. 2025, 15(6), 3333; https://doi.org/10.3390/app15063333 - 18 Mar 2025

Viewed by 315

Abstract

Detecting the daily behavior of broiler chickens allows early detection of irregular activity patterns and, thus, problems in the flock. In an attempt to resolve the problems of the slow detection speed, low accuracy, and poor generalization ability of traditional detection models in [...] Read more.

Detecting the daily behavior of broiler chickens allows early detection of irregular activity patterns and, thus, problems in the flock. In an attempt to resolve the problems of the slow detection speed, low accuracy, and poor generalization ability of traditional detection models in the actual breeding environment, we propose a chicken behavior detection method called FCBD-DETR (Faster Chicken Behavior Detection Transformer). The FasterNet network based on partial convolution (PConv) was used to replace the Resnet18 backbone network to reduce the computational complexity of the model and to improve the speed of model detection. In addition, we propose a new cross-scale feature fusion network to optimize the neck network of the original model. These improvements led to a 78% decrease in the number of parameters and a 68% decrease in GFLOPs. The experimental results show that the proposed model is superior to the traditional network in the speed, accuracy and generalization ability of broiler behavior detection. (1) The detection speed is improved from 49.5 frames per second to 68.5 frames per second, which is 22.6 frames and 10.9 frames higher than Yolov7 and Yolov8, respectively. (2) mAP0.5 reaches 99.4%, and MAP0.5:0.95 increases from 84.9 to 88.4%. (3) Combined with the multi-target tracking algorithm, the chicken flock counting, behavior recognition, and individual tracking tasks are successfully realized. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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Other

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26 pages, 1828 KiB

Open AccessSystematic Review

Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review

by Rameez Ahsen, Pierpaolo Di Bitonto, Pierfrancesco Novielli, Michele Magarelli, Donato Romano, Domenico Diacono, Alfonso Monaco, Nicola Amoroso, Roberto Bellotti and Sabina Tangaro

Appl. Sci. 2025, 15(8), 4228; https://doi.org/10.3390/app15084228 - 11 Apr 2025

Viewed by 409

Abstract

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time agricultural environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) and data-driven insights, optimizing water usage. [...] Read more.

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time agricultural environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) and data-driven insights, optimizing water usage. In this study, from an initial pool of 48 papers retrieved from well-known databases such as Scopus and Web of Science, etc., a rigorous eligibility criterion was applied, narrowing the focus to 11 pertinent studies. This review highlights major disciplines where DT technology is being applied: hydroponics, aquaponics, vertical farming, and irrigation. Additionally, the literature identifies two key sub-applications within these disciplines: the simulation and prediction of water quality and soil water. This review also explores the types and maturity levels of DT technology and key concepts within these applications. Based on their current implementation, DTs in agriculture can be categorized into two functional types: monitoring DTs, which emphasize real-time response and environmental control, and predictive DTs, which enable proactive irrigation management through environmental forecasting. AI techniques used within the DT framework were also identified based on their applications. These findings underscore the transformative role that DT technology can play in enhancing efficiency and sustainability in agricultural water management. Despite technological advancements, challenges remain, including data integration, scalability, and cost barriers. Further studies should be conducted to explore these issues within practical farming environments. Full article

(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)

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