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Remote Sensing and AI Algorithms for Plant Disease and Tree Health Detection

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 12028

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Special Issue Editors

Dr. Xanthoula Eirini Pantazi

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Guest Editor

Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), 54124 Thessaloniki, Greece
Interests: crop health status; precision agriculture; remote sensing; data fusion; machine learning; hyperspectral sensors; sensor fusion; deep learning; hyperspectral imaging field spectroscopy; fluorescence kinetics
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoling Deng

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Guest Editor

College of Artificial Intelligence, College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: low attitude remote sensing platform research and development; crop phenomics and high-throughput phenotyping

Dr. Jian Zhang

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Guest Editor

College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China
Interests: agricultural remote sensing

Prof. Dr. Dimitrios Moshou

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Guest Editor

Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: remote sensing; multiscale fusion robotic agriculture; sensor networks; robotics; development of cognitive abilities; fusion of global and local cognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The combination of remote sensing and AI algorithms have become a research hotspot in the last five years, especially after the entry of artificial intelligence and UAV in commercial applications for Precision Agriculture domain. For large-scale agricultural and forestry monitoring, data from multiple sensors such as hyperspectral camera, multi-spectral camera, airborne thermal infrared imager and LiDAR, are becoming the main data source, combined effectively with AI algorithms comprise the prevalent data analysis methods, both serving smart applications in agriculture replacing the traditional time-consuming methods for crop and trees health status assessment. Using remote sensing and artificial intelligence algorithms to detect early crop diseases and tree health status at its early stage remains a challenge. The current special issue aims at novel approaches that employ efficiently AI algorithms with several remote sensing methods for crop and trees health status assessment.

This Special Issue aims at studies covering different analysing and AI modelling of remote sensing data acquired by different sensors and platforms. Topics may cover any application regarding from the crop disease assessment, and tree health monitoring. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal), multiscale approaches or studies focused on large scale monitoring in agriculture and forestry, among other issues, are welcome. Articles may address, but are not limited, to the following topics:

Detection of crop disease
Detection of tree disease and health
Monitoring of infection degree of crop diseases and insect pests
UAV remote sensing platform and application
Satellite remote sensing data analysis for crop disease and tree health status
Discrimination of biotic and abiotic stress in crops and trees
AI applications for crop and trees protection

Remote sensing and AI algoriths with application to agricultural domain are the suggested themes.

Research articles, review articles as well as short communications are invited.

Dr. Xanthoula Eirini Pantazi
Dr. Xiaoling Deng
Dr. Jian Zhang
Prof. Dr. Dimitrios Moshou
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. Remote Sensing 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 2700 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

crop disease detection
crop health status
precision agriculture
UAV hyperspectral imaging
multispectral sensors
sensor fusion
spectroscopy
deep learning abnormality detection

Published Papers (4 papers)

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Research

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15 pages, 5081 KiB

Open AccessArticle

Real-Time UAV Patrol Technology in Orchard Based on the Swin-T YOLOX Lightweight Model

by Yubin Lan, Shaoming Lin, Hewen Du, Yaqi Guo and Xiaoling Deng

Remote Sens. 2022, 14(22), 5806; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225806 - 17 Nov 2022

Cited by 6 | Viewed by 1981

Abstract

Using unmanned aerial vehicle (UAV) real-time remote sensing to monitor diseased plants or abnormal areas of orchards from a low altitude perspective can greatly improve the efficiency and response speed of the patrol in smart orchards. The purpose of this paper is to realize the intelligence of the UAV terminal and make the UAV patrol orchard in real-time. The existing lightweight object detection algorithms are usually difficult to consider both detection accuracy and processing speed. In this study, a new lightweight model named Swin-T YOLOX, which consists of the advanced detection network YOLOX and the strong backbone Swin Transformer, was proposed. Model layer pruning technology was adopted to prune the multi-layer stacked structure of the Swin Transformer. A variety of data enhancement strategies were conducted to expand the dataset in the model training stage. The lightweight Swin-T YOLOX model was deployed to the embedded platform Jetson Xavier NX to evaluate its detection capability and real-time performance of the UAV patrol mission in the orchard. The research results show that, with the help of TensorRT optimization, the proposed lightweight Swin-T YOLOX network achieved 94.0% accuracy and achieved a detection speed of 40 fps on the embedded platform (Jetson Xavier NX) for patrol orchard missions. Compared to the original YOLOX network, the model accuracy has increased by 1.9%. Compared to the original Swin-T YOLOX, the size of the proposed lightweight Swin-T YOLOX has been reduced to two-thirds, while the model accuracy has slightly increased by 0.7%. At the same time, the detection speed of the model has reached 40 fps, which can be applied to the real-time UAV patrol in the orchard. Full article

(This article belongs to the Special Issue Remote Sensing and AI Algorithms for Plant Disease and Tree Health Detection)

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16 pages, 5160 KiB

Open AccessArticle

Detection of Standing Dead Trees after Pine Wilt Disease Outbreak with Airborne Remote Sensing Imagery by Multi-Scale Spatial Attention Deep Learning and Gaussian Kernel Approach

by Zemin Han, Wenjie Hu, Shoulian Peng, Haoran Lin, Jian Zhang, Jingjing Zhou, Pengcheng Wang and Yuanyong Dian

Remote Sens. 2022, 14(13), 3075; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133075 - 26 Jun 2022

Cited by 17 | Viewed by 2467

Abstract

The continuous and extensive pinewood nematode disease has seriously threatened the sustainable development of forestry in China. At present, many studies have used high-resolution remote sensing images combined with a deep semantic segmentation algorithm to identify standing dead trees in the red attack period. However, due to the complex background, closely distributed detection scenes, and unbalanced training samples, it is difficult to detect standing dead trees (SDTs) in a variety of complex scenes by using conventional segmentation models. In order to further solve the above problems and improve the recognition accuracy, we proposed a new detection method called multi-scale spatial supervision convolutional network (MSSCN) to identify SDTs in a wide range of complex scenes based on airborne remote sensing imagery. In the method, a Gaussian kernel approach was used to generate a confidence map from SDTs marked as points for training samples, and a multi-scale spatial attention block was added into fully convolutional neural networks to reduce the loss of spatial information. Further, an augmentation strategy called copy–pasting was used to overcome the lack of efficient samples in this research area. Validation at four different forest areas belonging to two forest types and two diseased outbreak intensities showed that (1) the copy–pasting method helps to augment training samples and can improve the detecting accuracy with a suitable oversampling rate, and the best oversampling rate should be carefully determined by the input training samples and image data. (2) Based on the two-dimensional spatial Gaussian kernel distribution function and the multi-scale spatial attention structure, the MSSCN model can effectively find the dead tree extent in a confidence map, and by following this with maximum location searching we can easily locate the individual dead trees. The averaged precision, recall, and F1-score across different forest types and disease-outbreak-intensity areas can achieve 0.94, 0.84, and 0.89, respectively, which is the best performance among FCN8s and U-Net. (3) In terms of forest type and outbreak intensity, the MSSCN performs best in pure pine forest type and low-outbreak-intensity areas. Compared with FCN8s and U-Net, the MSSCN can achieve the best recall accuracy in all forest types and outbreak-intensity areas. Meanwhile, the precision metric is also maintained at a high level, which means that the proposed method provides a trade-off between the precision and recall in detection accuracy. Full article

(This article belongs to the Special Issue Remote Sensing and AI Algorithms for Plant Disease and Tree Health Detection)

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16 pages, 2746 KiB

Open AccessArticle

Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer

by Yi Cen, Ying Huang, Shunshi Hu, Lifu Zhang and Jian Zhang

Remote Sens. 2022, 14(12), 2882; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122882 - 16 Jun 2022

Cited by 15 | Viewed by 3193

Abstract

As a kind of soil-borne epidemic disease, bacterial wilt (BW) is one of the most serious diseases in tomatoes in southern China, which may significantly reduce food quality and the total amount of yield. Hyperspectral remote sensing can detect crop diseases in the early stages and offers potential for BW detection in tomatoes. Tomatoes in southern China are commonly cultivated in greenhouses or bird nets, limiting the application of remote sensing based on natural sunlight. To resolve these issues, we collected the spectrum of tomatoes firstly using the HS-VN1000B Portable Intelligent Spectrometer, which is equipped with a simulated solar light source. We then proposed a tomato BW detection model based on some optimal spectral features. Specifically, these optimal features, including vegetation indexes and principal components (PCs), were extracted by the sequential forward selection (SFS), the simulated annealing (SA), and the genetic algorithm (GA) and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes. The results showed that the infected and healthy tomatoes exhibit different spectral characteristics for both leave and stem spectra, especially for near-infrared bands. In addition, the BW detecting model built by the combination of GA and SVM (GA-SVM) achieved the best performance with overall accuracies (OA) of 90.7% for leaves and 92.6% for stems. Compared with the results based on leaves, spectral features of stems provided better accuracy, indicating that the symptom of early infection of BW is more significant in tomato stems than in leaves. Further, the reliability of the GA-SVM tomato stem model was verified in our 2022 experiment with an OA of 88.6% and an F1 score of 0.80. Our study provides an effective means to detect BW disease of tomatoes in the early stages, which could help farmers manage their tomato production and effectively prevent pesticide abuse. Full article

(This article belongs to the Special Issue Remote Sensing and AI Algorithms for Plant Disease and Tree Health Detection)

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Other

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13 pages, 690 KiB

Open AccessTechnical Note

A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology

by Aaditya Prasad, Nikhil Mehta, Matthew Horak and Wan D. Bae

Remote Sens. 2022, 14(19), 4765; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194765 - 23 Sep 2022

Cited by 8 | Viewed by 2918

Abstract

Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that are representative of UAV images. The analysis of applications and methods is conducted on a database of high-fidelity apple tree images which are corrupted with class imbalance. The application begins by generating high-fidelity data using generative networks and then uses these novel data alongside the original high-fidelity data to produce low-fidelity images. A machine learning identifier identifies plants and labels them as potentially diseased or not. A machine learning classifier is then given the potentially diseased plant images and returns actual diagnoses for these plants. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system. Our drone technology shows promising results in accuracy when compared to labor-based methods of diagnosis. Full article

(This article belongs to the Special Issue Remote Sensing and AI Algorithms for Plant Disease and Tree Health Detection)

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