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Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture

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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 (20 December 2023) | Viewed by 11948

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Special Issue Editors

Prof. Dr. Luís Manuel Navas Gracia

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Guest Editor

Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain
Interests: precision farming; processes automation; artificial intelligence; renewable energies; greenhouse technology; urban farming
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Manuel Pérez-Ruiz

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Guest Editor

Department of Aerospace Engineering and Fluid Mechanics, University de Sevilla, Sevilla, Spain
Interests: precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, there has been a strong development of new technologies applied to the monitoring and control of different agricultural processes. This new growth has contributed to a significant increase in productivity, improvement in quality and reduction in the environmental impact of the agricultural processes. In the coming years, due to the high expectations of smart farming, there will be an increase in techniques that offer advanced data processing in digital environments. In this context, the use of drones, remote sensing and different artificial intelligence techniques are currently a reality.

Unmanned aerial vehicles (UAVs) or drones are now recognized as very useful tools to replace, help or assist humans in various missions, such as sensing, monitoring, mapping, inspection and surveillance. Many of these services involve the gathering of data and its processing with complex algorithms either in real-time or on the cloud, in many cases combining with data from remote sensing.

This Special Issue aims to present recent advances in technologies and algorithms, mainly based on artificial intelligence, to improve the applications of UAVs and remote sensing in precision agriculture. Topics of interest include, but are not limited to, plant disease and pest diagnosis; crop and orchard yield monitoring; advanced fertilization; variable operation prescription technologies; inspection of farms, vineyards or ranch animals; information management and management information systems.

In addition, this Special Issue is linked to the XII Iberian Congress of Agroengineering, organized by the University of Seville (Spain) and supported by the Spanish Society of Agroengineering and the Secção Especializada de Engenharia Rural da Sociedade de Ciências Agrárias from Portugal. This congress is a meeting point for researchers and technicians linked to agroengineering in Spain, Portugal, and the entire Ibero-American community. Papers submitted to the Congress on topics of precision agriculture, spatial analysis and data-driven technologies are invited to submit articles to this Special Issue.

Prof. Dr. Luís Manuel Navas Gracia
Prof. Dr. Manuel Pérez-Ruiz
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.

Keywords

Unmanned Aerial Vehicles (UAVs) and drones
precision agriculture and Farming 4.0
guidance, navigation and control
autonomy, perception and decision-making
smart farming (crops, dairy, grazing, etc.)
Site-Specific Management (SSM) of water, pesticide, fertilizer, etc.
Integrated Pest Management (IPM) and yield prediction
smart sensing and smart big data analytics
processing of data and information and communication technologies in agriculture
multiresource image processing technology
Artificial Intelligence and artificial neural network

Published Papers (9 papers)

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Research

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23 pages, 3522 KiB

Open AccessArticle

Cherry Tree Crown Extraction Using Machine Learning Based on Images from UAVs

by Vasileios Moysiadis, Ilias Siniosoglou, Georgios Kokkonis, Vasileios Argyriou, Thomas Lagkas, Sotirios K. Goudos and Panagiotis Sarigiannidis

Agriculture 2024, 14(2), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture14020322 - 18 Feb 2024

Viewed by 923

Abstract

Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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14 pages, 4542 KiB

Open AccessArticle

Development of a Detection System for Types of Weeds in Maize (Zea mays L.) under Greenhouse Conditions Using the YOLOv5 v7.0 Model

by Oscar Leonardo García-Navarrete, Oscar Santamaria, Pablo Martín-Ramos, Miguel Ángel Valenzuela-Mahecha and Luis Manuel Navas-Gracia

Agriculture 2024, 14(2), 286; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture14020286 - 09 Feb 2024

Cited by 2 | Viewed by 906

Abstract

Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of agrochemicals. The implementation of technology to mitigate this impact is also a challenge. In this work, an artificial vision system was implemented based on the YOLOv5s (You Only Look Once) model, which uses a single convolutional neural network (CNN) that allows differentiating corn from four types of weeds, for which a mobile support structure was built to capture images. The performance of the trained model had a value of mAP@05 (mean Average Precision) at a threshold of 0.5 of 83.6%. A prediction accuracy of 97% and a mAP@05 of 97.5% were obtained for the maize class. For the weed classes, Lolium perenne, Sonchus oleraceus, Solanum nigrum, and Poa annua obtained an accuracy of 86%, 90%, 78%, and 74%, and a mAP@05 of 81.5%, 90.2%, 76.6% and 72.0%, respectively. The results are encouraging for the construction of a precision weeding system. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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15 pages, 18746 KiB

Open AccessArticle

ICNet: A Dual-Branch Instance Segmentation Network for High-Precision Pig Counting

by Shanghao Liu, Chunjiang Zhao, Hongming Zhang, Qifeng Li, Shuqin Li, Yini Chen, Ronghua Gao, Rong Wang and Xuwen Li

Agriculture 2024, 14(1), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture14010141 - 18 Jan 2024

Viewed by 793

Abstract

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200,

A P

of 71.4% and

A P^{50}

of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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18 pages, 9813 KiB

Open AccessArticle

Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal

by Romeu Gerardo and Isabel P. de Lima

Agriculture 2023, 13(10), 1916; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13101916 - 29 Sep 2023

Viewed by 1348

Abstract

Nowadays, Unmanned Aerial Systems (UASs) provide an efficient and relatively affordable remote sensing technology for assessing vegetation attributes and status across agricultural areas through wide-area imagery collected with cameras installed on board. This reduces the cost and time of crop monitoring at the field scale in comparison to conventional field surveys. In general, by using remote sensing-based approaches, information on crop conditions is obtained through the calculation and mapping of multispectral vegetation indices. However, some farmers are unable to afford the cost of multispectral images, while the use of RGB images could be a viable approach for monitoring the rice crop quickly and cost-effectively. Nevertheless, the suitability of RGB indices for this specific purpose is not yet well established and needs further investigation. The aim of this work is to explore the use of UAS-based RGB vegetation indices to monitor the rice crop. The study was conducted in a paddy area located in the Lis Valley (Central Portugal). The results revealed that the RGB indices, Visible Atmospherically Resistant Index (VARI) and Triangular Greenness Index (TGI) can be useful tools for rice crop monitoring in the absence of multispectral images, particularly in the late vegetative phase. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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19 pages, 6646 KiB

Open AccessArticle

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

by Yuzhen Wu, Qingzhan Zhao, Xiaojun Yin, Yuanzhi Wang and Wenzhong Tian

Agriculture 2023, 13(9), 1679; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13091679 - 25 Aug 2023

Cited by 3 | Viewed by 1061

Abstract

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R² of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R² increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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19 pages, 1686 KiB

Open AccessArticle

An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field

by Tabassum Kanwal, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Santos Gracia Villar, Luis Alonso Dzul Lopez and Imran Ashraf

Agriculture 2023, 13(8), 1600; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13081600 - 13 Aug 2023

Cited by 5 | Viewed by 2528

Abstract

Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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11 pages, 2144 KiB

Open AccessArticle

Mapping Gaps in Sugarcane Fields Using UAV-RTK Platform

by Matheus Pereira de Oliveira, Paulo Henrique Cardoso, Romário Porto de Oliveira, Marcelo Rodrigues Barbosa Júnior and Rouverson Pereira da Silva

Agriculture 2023, 13(6), 1241; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13061241 - 14 Jun 2023

Cited by 2 | Viewed by 1372

Abstract

Unmanned aerial vehicles (UAVs) equipped with a global real-time kinematic navigation satellite system (GNSS RTK) could be a state-of-the-art solution to measuring gaps in sugarcane fields and enable site-specific management. Recent studies recommend the use of UAVs to map these gaps. However, low-accuracy GNSS provides incomplete or inaccurate photogrammetric reconstructions, which could easily generate an error in the gap measurement and constrain the applicability of these techniques. Therefore, in this study, we evaluated the potential of UAV RTK imagery for mapping gaps in sugarcane. To compare this solution with conventional UAV approaches, the precision and accuracy of RTK and non-RTK flights were evaluated. To increase the robustness of the research, flights were performed to map gaps found naturally in the field and with plants at different stages of development. Our results showed that the lengths of gaps identified by both RTK and non-RTK UAV imagery were similar, with differences in precision and accuracy of about 1% for both systems. In contrast, RTK was much more efficient and provides stakeholders with guidelines for accurate and precise mapping gaps, allowing them to make confident decisions on site-specific management. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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14 pages, 1339 KiB

Open AccessArticle

AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text

by Demeng Zhang, Guang Zheng, Hebing Liu, Xinming Ma and Lei Xi

Agriculture 2023, 13(6), 1220; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13061220 - 09 Jun 2023

Cited by 1 | Viewed by 942

Abstract

Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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Review

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19 pages, 2042 KiB

Open AccessReview

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

by Oscar Leonardo García-Navarrete, Adriana Correa-Guimaraes and Luis Manuel Navas-Gracia

Agriculture 2024, 14(4), 568; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture14040568 - 02 Apr 2024

Viewed by 704

Abstract

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures used to detect and identify weeds in different crops; 61 articles applying CNN architectures were analyzed during the last five years (2019–2023). The results show the used of different devices to acquire the images for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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