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Artificial Intelligence and Key Technologies of Smart Agriculture
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Artificial Intelligence and Key Technologies of Smart Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 11310

Special Issue Editors

Prof. Dr. Wenli Zhang

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: computer vision and pattern recognition; 3D reconstruction; intelligent bionics and control
Dr. Wei Guo

E-Mail Website
Guest Editor
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 188-0002, Japan
Interests: agricultural informatics; plant phenomics; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence holds a lot of promise for the agricultural industry. It helps us to analyze big data on crops and make useful inferences, and provides effective decision support to farmers. It effectively addresses the issues of poor productivity and manpower shortages. Artificial intelligence is now widely applied in all aspects of crop production, boosting the efficiency of resource allocation and the exploitation of agricultural production factors.

This Special Issue seeks to present the most recent artificial intelligence research results in the subject of smart agriculture. Authors are encouraged to submit high-quality research papers on intelligent field sensors, field operation robots (picking, weeding, fertilization, etc.), autonomous navigation technology, crop detection and identification, yield prediction, pest and disease prediction, growth state prediction, and other related topics. Review of theoretical techniques as well as real-world examples are welcome.

Prof. Dr. Wenli Zhang
Dr. Wei Guo
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. Sensors is an international peer-reviewed open access semimonthly 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

  • smart agriculture
  • smart orchard
  • intelligent sensors in the field
  • field operation robot
  • crop detection and identification
  • yield prediction
  • pest and disease prediction
  • crop growth state analysis and prediction
  • artificial Intelligence and deep Learning

Published Papers (6 papers)

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Research

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23 pages, 3949 KiB  
Article
IMU Auto-Calibration Based on Quaternion Kalman Filter to Identify Movements of Dairy Cows
by Carlos Muñoz-Poblete, Cristian González-Aguirre, Robert H. Bishop and David Cancino-Baier
Sensors 2024, 24(6), 1849; https://doi.org/10.3390/s24061849 - 13 Mar 2024
Viewed by 485
Abstract
This work is focused on developing a self-calibration algorithm for an orientation estimation of cattle movements based on a quaternion Kalman filter. The accelerometer signals in the earth’s frame provide more information to confirm that the cow is performing a jump to mount [...] Read more.
This work is focused on developing a self-calibration algorithm for an orientation estimation of cattle movements based on a quaternion Kalman filter. The accelerometer signals in the earth’s frame provide more information to confirm that the cow is performing a jump to mount another cow. To obtain the measurements in the earth’s frame, we propose a self-calibration method based on a strapdown inertial navigation system (SINS), which does not require intervention by the user once deployed in the field. The self-calibration algorithm uses a quaternion-based Kalman filter to predict the angular orientation with bias correction, and update it based on the measurements of accelerometers and magnetometers. The paper also depicts an alternate update to adjust the inclination using only the accelerometer measurements. We conducted experiments to compare the accuracy of the orientation estimation when the body moves similarly to cow mount movements. The comparison is between the proposed self-calibration algorithm with the IvenSense MPU9250 and Bosch BNO055 and the quaternion attitude estimation provided in the BNO055. The auto-calibrating algorithm presents a mean error of 0.149 rads with a mean consumption of 308.5 mW, and the Bosch algorithm shows an average error of 0.139 rads with a mean consumption of 307.5 mW. When we executed this algorithm in an MPU9250, the average error was 0.077 rads, and the mean consumption was 277.7 mW. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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19 pages, 8730 KiB  
Article
Evaluation of Inference Performance of Deep Learning Models for Real-Time Weed Detection in an Embedded Computer
by Canicius Mwitta, Glen C. Rains and Eric Prostko
Sensors 2024, 24(2), 514; https://doi.org/10.3390/s24020514 - 14 Jan 2024
Cited by 1 | Viewed by 1074
Abstract
The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time. One of the driving factors for weed detection is to develop alternatives [...] Read more.
The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time. One of the driving factors for weed detection is to develop alternatives to herbicides, which are becoming less effective as weed species develop resistance. Advances in deep learning technology have significantly improved the robustness of weed detection tasks. However, deep learning algorithms often require extensive computational resources, typically found in powerful computers that are not suitable for deployment in robotic platforms. Most ground rovers and UAVs utilize embedded computers that are portable but limited in performance. This necessitates research into deep learning models that are computationally lightweight enough to function in embedded computers for real-time applications while still maintaining a base level of detection accuracy. This paper evaluates the weed detection performance of three real-time-capable deep learning models, YOLOv4, EfficientDet, and CenterNet, when run on a deep-learning-enabled embedded computer, an Nvidia Jetson Xavier AGX. We tested the accuracy of the models in detecting 13 different species of weeds and assesses their real-time viability through their inference speeds on an embedded computer compared to a powerful deep learning PC. The results showed that YOLOv4 performed better than the other models, achieving an average inference speed of 80 ms per image and 14 frames per second on a video when run on an imbedded computer, while maintaining a mean average precision of 93.4% at a 50% IoU threshold. Furthermore, recognizing that some real-world applications may require even greater speed, and that the detection program would not be the only task running on the embedded computer, a lightweight version of the YOLOv4 model, YOLOv4-tiny, was tested for improved performance in an embedded computer. YOLOv4-tiny impressively achieved an average inference speed of 24.5 ms per image and 52 frames per second, albeit with a slightly reduced mean average precision of 89% at a 50% IoU threshold, making it an ideal choice for real-time weed detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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9 pages, 6262 KiB  
Communication
A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images
by Anna Petrovskaia, Raghavendra Jana and Ivan Oseledets
Sensors 2022, 22(23), 9273; https://doi.org/10.3390/s22239273 - 28 Nov 2022
Viewed by 1294
Abstract
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth’s surface for more than 4 years and has become an important data source for a large number of [...] Read more.
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth’s surface for more than 4 years and has become an important data source for a large number of research and policy-making initiatives. Unfortunately, a scan line corrector (SLC) on Landsat-7 broke down in May 2003, which caused the loss of up to 22 percent of any given scene. We present a single-image approach based on leveraging the abilities of the deep image prior method to fill in gaps using only the corrupt image. We test the ability of deep image prior to reconstruct remote sensing scenes with different levels of corruption in them. Additionally, we compare the performance of our approach with the performance of classical single-image gap-filling methods. We demonstrate a quantitative advantage of the proposed approach compared with classical gap-filling methods. The lowest-performing restoration made by the deep image prior approach reaches 0.812 in r2, while the best value for the classical approaches is 0.685. We also present the robustness of deep image prior in comparing the influence of the number of corrupted pixels on the restoration results. The usage of this approach could expand the possibilities for a wide variety of agricultural studies and applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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18 pages, 4296 KiB  
Article
Augmentation Method for High Intra-Class Variation Data in Apple Detection
by Huibin Li, Wei Guo, Guowen Lu and Yun Shi
Sensors 2022, 22(17), 6325; https://doi.org/10.3390/s22176325 - 23 Aug 2022
Cited by 6 | Viewed by 1588
Abstract
Deep learning is widely used in modern orchard production for various inspection missions, which helps improve the efficiency of orchard operations. In the mission of visual detection during fruit picking, most current lightweight detection models are not yet effective enough to detect multi-type [...] Read more.
Deep learning is widely used in modern orchard production for various inspection missions, which helps improve the efficiency of orchard operations. In the mission of visual detection during fruit picking, most current lightweight detection models are not yet effective enough to detect multi-type occlusion targets, severely affecting automated fruit-picking efficiency. This study addresses this problem by proposing the pioneering design of a multi-type occlusion apple dataset and an augmentation method of data balance. We divided apple occlusion into eight types and used the proposed method to balance the number of annotation boxes for multi-type occlusion apple targets. Finally, a validation experiment was carried out using five popular lightweight object detection models: yolox-s, yolov5-s, yolov4-s, yolov3-tiny, and efficidentdet-d0. The results show that, using the proposed augmentation method, the average detection precision of the five popular lightweight object detection models improved significantly. Specifically, the precision increased from 0.894 to 0.974, recall increased from 0.845 to 0.972, and mAP0.5 increased from 0.982 to 0.919 for yolox-s. This implies that the proposed augmentation method shows great potential for different fruit detection missions in future orchard applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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20 pages, 39651 KiB  
Article
Identifying Irregular Potatoes Using Hausdorff Distance and Intersection over Union
by Yongbo Yu, Hong Jiang, Xiangfeng Zhang and Yutong Chen
Sensors 2022, 22(15), 5740; https://doi.org/10.3390/s22155740 - 31 Jul 2022
Cited by 5 | Viewed by 2317
Abstract
Further processing and the added value of potatoes are limited by irregular potatoes. An ellipse-fitting-based Hausdorff distance and intersection over union (IoU) method for identifying irregular potatoes is proposed to solve the problem. First, the acquired potato image is resized, translated, segmented, and [...] Read more.
Further processing and the added value of potatoes are limited by irregular potatoes. An ellipse-fitting-based Hausdorff distance and intersection over union (IoU) method for identifying irregular potatoes is proposed to solve the problem. First, the acquired potato image is resized, translated, segmented, and filtered to obtain the potato contour information. Secondly, a least-squares fitting method fits the extracted contour to an ellipse. Then, the similarity between the irregular potato contour and the fitted ellipse is characterized using the perimeter ratio, area ratio, Hausdorff distance, and IoU. Next, the characterization ability of the four features is analyzed, and an identification standard of irregular potatoes is established. Finally, we discuss the algorithm’s shortcomings in this paper and draw the advantages of the algorithm by comparison. The experimental results showed that the characterization ability of perimeter ratio and area ratio was inferior to that of Hausdorff distance and IoU, and using Hausdorff distance and IoU as feature parameters can effectively identify irregular potatoes. Using Hausdorff distance separately as a feature parameter, the algorithm achieved excellent performance, with precision, recall, and F1 scores reaching 0.9423, 0.98, and 0.9608, respectively. Using IoU separately as a feature parameter, the algorithm achieved a higher overall recognition rate, with precision, recall, and F1 scores of 1, 0.96, and 0.9796, respectively. Compared with existing studies, the proposed algorithm identifies irregular potatoes using only one feature, avoiding the complexity of high-dimensional features and significantly reducing the computing effort. Moreover, simple threshold segmentation does not require data training and saves algorithm execution time. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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Review

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16 pages, 710 KiB  
Review
Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods
by Éva Hajnal, Levente Kovács and Gergely Vakulya
Sensors 2022, 22(18), 6812; https://doi.org/10.3390/s22186812 - 08 Sep 2022
Cited by 5 | Viewed by 3683
Abstract
It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the [...] Read more.
It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the two conflicting demands is to continuously monitor the animals. In this article, rumen bolus sensor techniques are reviewed, as they can provide lifelong monitoring due to their implementation. The applied sensory modalities are reviewed also using data transmission and data-processing techniques. During the processing of the literature, we have given priority to artificial intelligence methods, the application of which can represent a significant development in this field. Recommendations are also given regarding the applicable hardware and data analysis technologies. Data processing is executed on at least four levels from measurement to integrated analysis. We concluded that significant results can be achieved in this field only if the modern tools of computer science and intelligent data analysis are used at all levels. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
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