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Agronomy
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Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest
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Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 26746

Special Issue Editors

Dr. Nguyenthanh Son

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Guest Editor
Center for Space and Remote Sensing Research (CSRSR), National Central University, Taoyuan 32001, Taiwan
Interests: precision agriculture; remote sensing; climate change; crop management
Special Issues, Collections and Topics in MDPI journals
Dr. Chien-Hui Syu

E-Mail Website
Guest Editor
Division of Agricultural Chemistry, Taiwan Agricultural Research Institute (TARI), No.189, Zhongzheng Rd., Wufeng District, Taichung City 41362, Taiwan
Interests: soil chemistry; soil survey; rhizosphere chemistry; instrumental analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng-Ru Chen

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli City 32001, Taoyuan County, Taiwan
Interests: environmental education; remote sensing for earth environment (RS); geographic information systems (GIS); spatial data analysis; statistics on spatial data; time-series data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Studies using artificial intelligence and spatial datasets for crop monitoring have become important and attracted interest among scientists worldwide. The rich historical archives and continuing acquisition of Earth observation datasets provide opportunities for crop monitoring at local, regional, and global scales in response to the impacts of climate change. In addition, recent advances and applications of artificial intelligence algorithms make it possible to process a large number of spatiotemporal datasets for crop growth and damage assessment, crop health analysis, crop yield and water requirements, and crop yield forecasting, which is extremely important for agronomists to devise successful strategies for a country to address food security issues.

This Special Issue of Agronomy aims to collect research manuscripts related to applications of artificial intelligence and Earth observation datasets for such crop monitoring purposes at different scales around the globe. The topics include but are not limited to the following aspects:

  • Applications of artificial intelligence and Earth observation data (e.g., crop phenology monitoring, crop type mapping, yield forecasting, crop water requirement);
  • Multisensor image fusion for improved crop monitoring and management;
  • Data assimilation and crop growth models for crop yield modeling and forecasting;
  • Spatial modeling of spatial changes in farming practices, and driving forces of consequences of land surface dynamics.

Dr. Nguyenthanh Son
Dr. Chien-Hui Syu
Dr. Cheng-Ru Chen
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. Agronomy 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.

Keywords

  • artificial intelligence
  • earth observation
  • data assimilation
  • data fusion
  • agricultural systems
  • yield forecasting
  • crop type mapping
  • crop yield and water requirements
  • crop health and damage assessment
  • spatial change modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Editorial

Jump to: Research

4 pages, 179 KB  
Editorial
Towards Artificial Intelligence Applications in Precision and Sustainable Agriculture
by Nguyenthanh Son, Cheng-Ru Chen and Chien-Hui Syu
Agronomy 2024, 14(2), 239; https://doi.org/10.3390/agronomy14020239 - 23 Jan 2024
Cited by 15 | Viewed by 7047
Abstract
Agriculture is the backbone of many economies across the globe [...] Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest)

Research

Jump to: Editorial

19 pages, 6126 KB  
Article
Research on Fruit Spatial Coordinate Positioning by Combining Improved YOLOv8s and Adaptive Multi-Resolution Model
by Dexiao Kong, Jiayi Wang, Qinghui Zhang, Junqiu Li and Jian Rong
Agronomy 2023, 13(8), 2122; https://doi.org/10.3390/agronomy13082122 - 13 Aug 2023
Cited by 9 | Viewed by 2551
Abstract
Automated fruit-picking equipment has the potential to significantly enhance the efficiency of picking. Accurate detection and localization of fruits are particularly crucial in this regard. However, current methods rely on expensive tools such as depth cameras and LiDAR. This study proposes a low-cost [...] Read more.
Automated fruit-picking equipment has the potential to significantly enhance the efficiency of picking. Accurate detection and localization of fruits are particularly crucial in this regard. However, current methods rely on expensive tools such as depth cameras and LiDAR. This study proposes a low-cost method based on monocular images to achieve target detection and depth estimation. To improve the detection accuracy of targets, especially small targets, an advanced YOLOv8s detection algorithm is introduced. This approach utilizes the BiFormer block, an attention mechanism for dynamic query-aware sparsity, as the backbone feature extractor. It also adds a small-target-detection layer in the Neck and employs EIoU Loss as the loss function. Furthermore, a fused depth estimation method is proposed, which incorporates high-resolution, low-resolution, and local high-frequency depth estimation to obtain depth information with both high-frequency details and low-frequency structure. Finally, the spatial 3D coordinates of the fruit are obtained by fusing the planar coordinates and depth information. The experimental results with citrus as the target result in an improved YOLOv8s network mAP of 88.45% and a recognition accuracy of 94.7%. The recognition of citrus in a natural environment was improved by 2.7% compared to the original model. In the detection range of 30 cm~60 cm, the depth-estimation results (MAE, RSME) are 0.53 and 0.53. In the illumination intensity range of 1000 lx to 5000 lx, the average depth estimation results (MAE, RSME) are 0.49 and 0.64. In the simulated fruit-picking scenario, the success rates of grasping at 30 cm and 45 cm were 80.6% and 85.1%, respectively. The method has the advantage of high-resolution depth estimation without constraints of camera parameters and fruit size that monocular geometric and binocular localization do not have, providing a feasible and low-cost localization method for fruit automation equipment. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest)
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17 pages, 39800 KB  
Article
A Visual Method of Hydroponic Lettuces Height and Leaves Expansion Size Measurement for Intelligent Harvesting
by Yidong Ma, Yin Zhang, Xin Jin, Xinping Li, Huankun Wang and Chong Qi
Agronomy 2023, 13(8), 1996; https://doi.org/10.3390/agronomy13081996 - 27 Jul 2023
Cited by 7 | Viewed by 4141
Abstract
Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing [...] Read more.
Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing method and intelligently adjusting the operating parameters of the harvesting device are the foundation of high-quality harvesting for lettuces. The overlapped leaves of adjacent hydroponic lettuces cause difficulties in measuring lettuce size, especially the leaves expansion size. Therefore, we proposed an image processing method for measuring lettuce height and leaves expansion size according to the upper contour feature of lettuces and an image included three lettuces. Firstly, the upper contours of the lettuces were extracted and segmented via image preprocessing. Secondly, lettuce height was measured according to the maximum ordinate of the contour. Lastly, the lettuce’s upper contour was fitted to a function to measure the leaves expansion size. The measurement results showed that the maximal relative error of the lettuce height measurements was 5.58%, and the average was 2.14%. The effect of the quadratic function in fitting the upper contour was the best compared with the cubic function and sine function. The maximal relative error of the leaves expansion size measurements was 8.59%, and the average was 4.03%. According to the results of the lettuce height and leaves expansion size measurements, the grabbing parameters of each lettuce were intelligently adjusted to verify the harvesting effect. The harvesting success rates of lettuces was above 90%, and the injured leaves areas of the left, middle, and right lettuces in each image were 192.6 mm2, 228.1 mm2, and 205.6 mm2, respectively. This paper provides a reference for the design and improvement of intelligent harvesters for hydroponic lettuces. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest)
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30 pages, 57915 KB  
Article
ECLPOD: An Extremely Compressed Lightweight Model for Pear Object Detection in Smart Agriculture
by Yuhang Xie, Xiyu Zhong, Jialei Zhan, Chang Wang, Nating Liu, Lin Li, Peirui Zhao, Liujun Li and Guoxiong Zhou
Agronomy 2023, 13(7), 1891; https://doi.org/10.3390/agronomy13071891 - 17 Jul 2023
Cited by 13 | Viewed by 2956
Abstract
Accurate pear sorting plays a crucial role in ensuring the quality of pears and increasing the sales of them. In the domain of intelligent pear sorting, precise target detection of pears is imperative. However, practical implementation faces challenges in achieving adequate accuracy in [...] Read more.
Accurate pear sorting plays a crucial role in ensuring the quality of pears and increasing the sales of them. In the domain of intelligent pear sorting, precise target detection of pears is imperative. However, practical implementation faces challenges in achieving adequate accuracy in pear target detection due to the limitations of computational resources in embedded devices and the occurrence of occlusion among pears. To solve this problem, we built an image acquisition system based on pear sorting equipment and created a pear dataset containing 34,598 pear images under laboratory conditions. The dataset was meticulously annotated using the LabelImg software, resulting in a total of 154,688 precise annotations for pears, pear stems, pear calyxes, and pear defects. Furthermore, we propose an Extremely Compressed Lightweight Model for Pear Object Detection (ECLPOD) based on YOLOv7’s pipeline to assist in the pear sorting task. Firstly, the Hierarchical Interactive Shrinking Network (HISNet) was proposed, which contributed to efficient feature extraction with a limited amount of computation and parameters. The Bulk Feature Pyramid (BFP) module was then proposed to enhance pear contour information extraction during feature fusion. Finally, the Accuracy Compensation Strategy (ACS) was proposed to improve the detection capability of the model, especially for identification of the calyces and stalks of pears. The experimental results indicate that the ECLPOD achieves 90.1% precision (P) and 85.52% mAP50 with only 0.58 million parameters and 1.3 GFLOPs of computation in the homemade pear dataset in this paper. Compared with YOLOv7, the number of parameters and the amount of computation for the ECLPOD are compressed to 1.5% and 1.3%, respectively. Compared with other mainstream methods, the ECLPOD achieves an optimal trade-off between accuracy and complexity. This suggests that the ECLPOD is superior to these existing approaches in the field of object detection for assisting pear sorting tasks with good potential for embedded device deployment. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest)
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16 pages, 2654 KB  
Article
Applying the SIMPLE Crop Model to Assess Soybean (Glicine max. (L.) Merr.) Biomass and Yield in Tropical Climate Variation
by Quang V. Pham, Tanh T. N. Nguyen, Tuyen T. X. Vo, Phuoc H. Le, Xuan T. T. Nguyen, Nha V. Duong and Ca T. S. Le
Agronomy 2023, 13(4), 1180; https://doi.org/10.3390/agronomy13041180 - 21 Apr 2023
Cited by 6 | Viewed by 3405
Abstract
Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model [...] Read more.
Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model to investigate the responses of soybeans to the climate. We conducted two experiments on crop growth in the Summer–Autumn season of 2020, and Winter–Spring 2021 in the Hoa Binh Commune, in the Mekong Delta, Vietnam, which is an area that is vulnerable to climate change impacts, to obtain data for our model input and assessment. The assessment was concerned with the effects of climate variables (temperature and CO2) on soybean biomass and yield. The results indicated that the SIMPLE model performed well in simulating soybean yields, with an RRMSE of 9–10% overall. The drought stress results showed a negative impact on the growth and development of soybeans, although drought stress due to less rainfall seemed more serious in Spring–Winter 2021 than in Summer–Autumn 2020. This study figured out the trend that higher temperatures can shorten biomass development and lead to yield reduction. In addition, soybeans grown under high CO2 concentrations of 600 ppm gave a higher biomass and a greater yield than in the case with 350 ppm. In conclusion, climate variance can affect the soybean yield, which can be well investigated using the SIMPLE model. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest)
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12 pages, 10134 KB  
Article
An Automatic Jujube Fruit Detection and Ripeness Inspection Method in the Natural Environment
by Defang Xu, Huamin Zhao, Olarewaju Mubashiru Lawal, Xinyuan Lu, Rui Ren and Shujuan Zhang
Agronomy 2023, 13(2), 451; https://doi.org/10.3390/agronomy13020451 - 2 Feb 2023
Cited by 30 | Viewed by 3954
Abstract
The ripeness phases of jujube fruits are one factor mitigating against fruit detection, in addition to uneven environmental conditions such as illumination variation, leaf occlusion, overlapping fruits, colors or brightness, similar plant appearance to the background, and so on. Therefore, a method called [...] Read more.
The ripeness phases of jujube fruits are one factor mitigating against fruit detection, in addition to uneven environmental conditions such as illumination variation, leaf occlusion, overlapping fruits, colors or brightness, similar plant appearance to the background, and so on. Therefore, a method called YOLO-Jujube was proposed to solve these problems. With the incorporation of the networks of Stem, RCC, Maxpool, CBS, SPPF, C3, PANet, and CIoU loss, YOLO-Jujube was able to detect jujube fruit automatically for ripeness inspection. Having recorded params of 5.2 m, GFLOPs of 11.7, AP of 88.8%, and a speed of 245 fps for detection performance, including the sorting and counting process combined, YOLO-Jujube outperformed the network of YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, and YOLOv7-tiny. YOLO-Jujube is robust and applicable to meet the goal of a computer vision-based understanding of images and videos. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Agriculture: Cultivation, Management and Harvest)
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