Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Louis Kouadio
 -- 2581 2023-10-24 09:03:46 |
2 Reference format revised.
Lindsay Dong
-1 word(s) 2580 2023-10-25 02:37:05 | |
3 Main text format revised.
Lindsay Dong
Meta information modification 2580 2023-10-25 02:38:25 | |
4 Changes in Section 4. One paragraph was duplicated. It has now been removed.
Louis Kouadio
 -126 word(s) 2454 2023-10-27 04:41:28 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Kouadio, L.; El Jarroudi, M.; Belabess, Z.; Laasli, S.; Roni, M.Z.K.; Amine, I.D.I.; Mokhtari, N.; Mokrini, F.; Junk, J.; Lahlali, R. UAV-Based Applications for Plant Disease Detection and Monitoring. Encyclopedia. Available online: https://encyclopedia.pub/entry/50712 (accessed on 17 May 2024).
Kouadio L, El Jarroudi M, Belabess Z, Laasli S, Roni MZK, Amine IDI, et al. UAV-Based Applications for Plant Disease Detection and Monitoring. Encyclopedia. Available at: https://encyclopedia.pub/entry/50712. Accessed May 17, 2024.
Kouadio, Louis, Moussa El Jarroudi, Zineb Belabess, Salah-Eddine Laasli, Md Zohurul Kadir Roni, Ibn Dahou Idrissi Amine, Nourreddine Mokhtari, Fouad Mokrini, Jürgen Junk, Rachid Lahlali. "UAV-Based Applications for Plant Disease Detection and Monitoring" Encyclopedia, https://encyclopedia.pub/entry/50712 (accessed May 17, 2024).
Kouadio, L., El Jarroudi, M., Belabess, Z., Laasli, S., Roni, M.Z.K., Amine, I.D.I., Mokhtari, N., Mokrini, F., Junk, J., & Lahlali, R. (2023, October 24). UAV-Based Applications for Plant Disease Detection and Monitoring. In Encyclopedia. https://encyclopedia.pub/entry/50712
Kouadio, Louis, et al. "UAV-Based Applications for Plant Disease Detection and Monitoring." Encyclopedia. Web. 24 October, 2023.
UAV-Based Applications for Plant Disease Detection and Monitoring
Edit
This entry is adapted from the peer-reviewed paper 10.3390/rs15174273

Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies.

unmanned aerial vehicle plant disease disease monitoring image processing machine learning

1. Introduction

Plant diseases have multifaceted and far-reaching consequences, impacting agriculture, ecosystems, economies, and human well-being. They can lead to reduced crop yields, lower crop quality, and even complete crop failures, which can disrupt the supply chain, result in increased food prices and potential food shortages, and negatively impact food security and the livelihood of stakeholders engaged in agricultural sectors [1][2]. Globally, the economic impact of crop yield loss due to plant diseases is estimated to be around US$220 billion each year [3]. Annual yield losses due to plant diseases and pests in the top food staple rice, maize, and wheat range from 24.6% to 40.9% for rice, from 19.5% to 41.1% for maize, and from 10.1% to 28.1% for wheat worldwide [4]. Plant diseases can also alter ecosystems by affecting the abundance and distribution of plant species and disrupting the food web and ecosystem dynamics [5][6]. Some plant diseases may cause health issues in humans and livestock. For example, mycotoxins produced by certain fungi can contaminate crops, leading to the ingestion of toxins through food consumption [7]. It is, therefore, essential to adopt good management practices to reduce disease risk and potential epidemic outbreaks in order to minimize their impact and ensure good crop production [8][9].
There have been multiple review articles dealing with the use of UAV for monitoring and assessing biotic plant stresses, including plant diseases (e.g., [10][11][12][13][14][15]). For example, Barbedo [10] discussed UAV imagery-based monitoring of different plant stresses caused by drought, nutrition disorders, and diseases and the detection of pests and weeds using UAVs. 

In all the reviews listed above, an overview of the types of plants and diseases investigated using UAV imagery, the trends of sensor and camera types, along with the related data analysis methods has yet to be provided. Furthermore, as UAV-based plant stress detection is still a subject of ongoing research, a comprehensive overview and interpretation of current research on UAV-based applications for plant disease detection and monitoring is of particular interest. For farmers willing to adopt such approaches, such a comprehensive review can serve as a repository of knowledge, elucidating the evolving landscape of technological advancements and methodologies pertinent to disease management. It also offers a strategic perspective on the potential and limitations of these approaches. For agribusinesses, comprehensive reviews can facilitate informed decision-making regarding investment, implementation, and integration of UAV systems within farm activities. For researchers, in addition to providing potential research avenues, the findings of the review can help create and/or foster collaboration and information exchange, encouraging innovation and cross-sectoral synergy.

2. UAV-Based Applications for Plant Disease Detection and Monitoring

Plants of Interest Found in Articles

The systematic quantitative literature review indicated that current research has dealt with disease symptoms on 35 different plants (Figure 1). Not surprisingly, diseases in cereal crops were most often investigated in the articles, with wheat and maize being the cereal crops that were most investigated (Figure 1). Other plant species most often studied included potato and sugar beet (Figure 1). When breaking down the number of research articles by plant species investigated for the top countries of studies China, USA, Brazil, Malaysia, Germany, and Italy, the analysis showed that in China or the USA, diseases in 10 different plant species were investigated. Diseases on wheat, pine tree, and banana were the most studied in China, whereas in the USA, it was research on maize diseases that dominated (Figure 1a). In this latter country, the number of research articles reporting on UAV-based approaches for disease monitoring was the same for apple, citrus, cotton, tomato, and watermelon (Figure 1a). In Brazil, diseases on five plant species were investigated, with coffee and soybean dominating. For Malaysia, research on UAV-based monitoring of diseases affecting oil palm ranked first among the three plant species of study (rice and eucalyptus were the two other plant species). A distinct trait was found for Germany, where most studies (four out of five) concerned sugar beet (Figure 1a).
Remotesensing 15 04273 g004
Figure 1. The proportion of plant species whose diseases were investigated in the research articles. (a) Countries with more than one study plant; (b) countries with one study plant.

Diseases and Groups of Pathogens Investigated

The list of plant diseases whose symptoms and/or severity were assessed using UAV-based imagery is presented in Table 1. Overall, the symptoms and/or severity of more than 80 plant diseases have been monitored using UAV-based approaches. Depending on the plant and the disease, the studies involved disease symptoms visible on either leaf, stem, or fruit, with most of the studies focusing on leaf diseases. In wheat, six main diseases were investigated, including leaf rust (caused by Puccinia triticina) [16], yellow rust (caused by P. striiformis f. sp. tritici) [16][17][18][19][20][21][22][23][24][25], powdery mildew (caused by Blumeria graminim f. sp. tritici) [26], tan spot (caused by Pyrenophora tritici-repentis) [27], Septoria leaf blotch (caused by Zymoseptoria tritici) [27], and Fusarium head blight (caused by a complex of Fusarium graminearum Schwabe and F. culmorum) [28][29] (Table 1). The first four diseases typically attack wheat leaves, whereas yellow rust can cause damage to the leaves and stems, whereas symptoms of Fusarium head blight are visible on infected spikelets. For potatoes, symptoms of five diseases have been investigated using UAV-based approaches (Table 1). These diseases include potato early blight (caused by Alternaria solani Sorauer) [30], late blight (caused by Phytophthora infestans (Mont.) De Bary) [31][32][33][34], the Y virus (caused by the potato virus Y) [35], soft rot (caused by Erwinia bacteria) [30], and vascular wilt (caused by Pseudomonas solanacearum) [36].
Table 1. List of plant diseases whose symptoms and/or severity were investigated.
Plant Disease Related Reviewed Study
Apple tree Cedar rust [37][38]
Scab [37]
Fire blight [39]
Areca palm Yellow leaf disease [40]
Banana Yellow sigatoka [41]
Xanthomonas wilt of banana [42]
Banana bunchy top virus [42][43]
Fusarium wilt [44][45][46]
Bermudagrass Spring dead spot [47]
Citrus Citrus canker [48]
Citrus huanglongbing disease [49][50][51][52]
Phytophthora foot rot [52]
Citrus gummosis disease [53]
Coffee Coffee leaf rust [54][55]
Cotton Cotton root rot [56][57]
Eucalyptus Various leaf diseases [58]
Grapevine Grapevine leaf stripe [59][60][61][62]
Flavescence dorée phytoplasma [63]
Black rot [38][62]
Isariopsis leaf spot [61][62]
Kiwifruit Kiwifruit decline [64]
Lettuce Soft rot [65]
Maize Northern leaf blight [66][67][68]
Southern leaf blight [69]
Maize streak virus disease [70][71]
Tar spot [72]
Norway spruce Needle bladder rust [73]
Oil palm Basal stem rot [74][75][76]
Oilseed rape Sclerotinia [77]
Okra Cercospora leaf spot [78]
Olive tree Verticillium wilt [79]
Xylella fastidiosa [80][81]
Peacock spot [82]
Onion Anthracnose-twister [83]
Stemphylium leaf blight [84]
Opium poppy Downy mildew [85]
Paperbark tree Myrtle rust [86]
Peach tree Fire blight [87]
Peanut Bacterial wilt [88]
Pine tree Pine wilt disease [89][90][91][92][93]
Red band needle blight [94]
Potato Potato late blight [31][32][33][34]
Potato early blight [30]
Potato Y virus [35]
Vascular wilt [36]
Soft rot [35]
Radish Fusarium wilt [95][96]
Rice Sheath blight [97]
Bacterial leaf blight [98]
Bacterial panicle blight [98]
Soybean Target spot [99][100]
Powdery mildew [99][100]
Squash Powdery mildew [101]
Sugar beet Cercospora leaf spot [102][103][104][105][106][107]
Anthracnose [103][104]
Alternaria leaf spot [103][104]
Beet cyst nematode [108]
Sugarcane White leaf phytoplasma [109]
Switchgrass Rust disease [110]
Tea Anthracnose [111]
Tomato Bacterial spot [112][113][114]
Early blight [112]
Late blight [112]
Septoria leaf spot [112]
Tomato mosaic virus [112]
Leaf mold [112]
Target leaf spot [112][113][114]
Tomato yellow leaf curl virus [112][114]
Watermelon Gummy stem blight [115]
Anthracnose [115]
Fusarium wilt [115]
Phytophthora fruit rot [115]
Alternaria leaf spot [115]
Cucurbit leaf crumple [115]
Downy mildew [116]
Wheat Yellow rust [16][17][18][19][20][21][22][23][24][25]
Leaf rust [16]
Septoria leaf spot [27]
Powdery mildew [26]
Tan spot [27]
Fusarium head blight [28][29]

Sensors Used for the Detection and Monitoring of Plant Diseases

Various types of sensors mounted on UAVs have been used to collect high spatial and spectral resolution data for plant disease detection and monitoring (Figure 2). The most used sensors were multispectral, RGB, hyperspectral, and digital cameras. Wheat was the plant whose diseases were investigated using different sensor types (individually or in combination) (Figure 2). Thus, symptoms of yellow rust on wheat leaves have been investigated using data from multispectral sensors [17][19][25], RGB cameras [16][22][24], hyperspectral sensors [18][20][21], and RGB + multispectral sensors [23]. Symptoms of Fusarium head blight were identified using data captured by hyperspectral sensors [29] and thermal infrared + RGB sensors [28], whereas symptoms of Septoria leaf blotch and tan spot were detected using RGB + multispectral sensors [27] (Figure 2). Images acquired using multispectral and RGB sensors were more often used to derive vegetation indices (VIs), which allowed for the detection of changes in vegetation health indicative of disease (e.g., discoloration, wilting, spots). Owing to their capability to capture images in different narrow spectral bands, hyperspectral sensors were used to detect more subtle changes in vegetation health that may not be visible with other sensors. Such data were used to create spectral signatures characteristic of a given disease.
Remotesensing 15 04273 g005
Figure 2. The distribution of sensor types and plants whose diseases were investigated. The segments in each ring are proportionate to the number of related research articles reviewed in the systematic quantitative literature review. RGB, NIR, and LiDAR stand for red-green-blue, near-infrared, and light detection and ranging, respectively.

Methods Used for Image Processing and Data Analysis

By capturing high spatial and spectral resolution images, sensors, and cameras embarked on UAVs provide valuable data that can be leveraged to analyze and detect plant disease symptoms. Results of the SQLR showed that various techniques, including visual analysis, computer vision, and VI-based analysis, have been used to process and analyze UAV-based imagery data for plant disease detection. Among these techniques, computer vision was the most used technique. In computer vision, the algorithms used for image classification and object recognition were machine learning (ML) algorithms that enabled the extraction of meaningful information from the images by automatically identifying and classifying visual patterns associated with disease symptoms. Generally, after image pre-processing, feature extraction techniques were employed to identify the relevant visual characteristics associated with disease symptoms. Then, the extracted features were classified into different categories (e.g., healthy, diseased, etc.). Next, ML algorithms were trained on labeled datasets where regions of interest have been annotated manually as healthy or diseased by human experts. Through the training process, the algorithms learned to recognize and distinguish between healthy and diseased plant organs. Depending on the extracted features, the classification analysis was either color, texture, shape, or spectral-based. Color-based analysis examines variations in coloration of the plant organ of interest (i.e., leaf) that may indicate the presence of disease.

3. Promising Means for Improving Plant Disease Management

Eleven years on from the work of Mahlein et al. [117], which critically reviewed the use of non-invasive sensors for the detection, identification, and quantification of plant diseases, there has been noticeable progress in the field of plant disease detection and monitoring using remote sensing derived information. In recent years, UAV-based imagery has become the new norm for plot and field-level studies. UAV-based approaches for plant disease detection and identification have several advantages over traditional methods as sensors mounted on UAVs provide high-resolution and spectral images that can be used to identify small-scale changes in crop health. UAVs also provide a fast and effective solution for capturing images over larger farmland areas, which can be challenging when using ground-based methods, though the use of UAVs in larger areas can be limited by the payload capacity and battery resources [10]. Other advantages of UAV-based approaches for plant disease monitoring include the reduced reliance on manual inspection and scouting, thereby saving time and resources. While initial investments in UAV technology might be significant, they can lead to long-term cost savings. As such, UAVs offer a promising approach for improved plant disease management.
While UAV-based approaches for plant disease monitoring offer several advantages, it is important to acknowledge their limitations [10][11][12]. Challenges related to background interference, weather conditions, sensor constraints, resource limitations (e.g., peripherals, sensors) and disparities between ML-based model training and validation stages, variations in disease symptoms over time and in space have been addressed in [10][11][12]. These challenges will not be discussed extensively here. Adverse weather, such as strong winds, rain, or low light conditions during UAVs flights, can hinder image acquisition and potentially impact the accuracy of disease detection. Another limitation is related to the image annotation consistency. Because the accuracy of disease detection relies on the expertise and experience of the human annotators who label the training datasets, variations in annotations among different operators can introduce inconsistencies and affect the generalization capabilities of the classification models. To overcome limitations associated with weather conditions, careful consideration and planning are required to avoid unfavorable weather conditions as much as possible and ensure a representative sampling of the field. Another potential solution would be to develop autonomous UAV systems that can operate in complex environments (e.g., under reduced light conditions) and adapt to changing conditions to improve flight operations. To address annotation consistency, regular training and calibration sessions are possible solutions to help overcome such a challenge.

4. The Way Forward

Research on using UAV-based approaches to detect and monitor plant stress caused by diseases is still underway, and there are ample opportunities to develop innovative solutions and improve the effectiveness and efficiency of these approaches. Current image analysis techniques for plant disease detection can be time-consuming, labor-intensive, and computationally demanding, particularly when it comes to using sophisticated CNN-based approaches, that require graphical processing units to train models. Balancing the trade-offs between resource requirements, model complexity, performance, and interpretability, and transfer learning opportunities has guided the choice of the most suitable ML technique for analyzing UAV imagery data. Future research can focus on improving the efficiency of ML-based approaches through the development of more advanced ML algorithms that can analyze images quickly and accurately. This will allow for the development of methods for real-time data analysis and decision-making tools that can be integrated with UAV systems. In this line, future research can investigate the use of reinforcement learning algorithms for plant disease management, which will involve training the models to learn from past actions and make decisions that optimize long-term plant health and minimize disease outbreaks.

There have been encouraging outcomes in integrating multiple sensors to provide more detailed and accurate data for plant disease detection, as highlighted by the number of related research articles, though this remains limited to a few numbers of plant species and diseases (Table 1). Future research can explore extending such approaches to economically important plant diseases of major food crops, such as rice, wheat, maize, cassava, plantains, potatoes, sorghum, soybeans, sweet potatoes, and yams, around the world. Research can also focus on integrating UAV data from multiple sensors or with satellite imagery (i.e., data fusion) for plant disease detection, as it has been explored for crop yield forecasting [118] and crop monitoring [119]. Such UAV and satellite data fusion will allow for a better understanding of crop health patterns and trends over large areas [120].

References

  1. Ristaino, J.B.; Anderson, P.K.; Bebber, D.P.; Brauman, K.A.; Cunniffe, N.J.; Fedoroff, N.V.; Finegold, C.; Garrett, K.A.; Gilligan, C.A.; Jones, C.M.; et al. The persistent threat of emerging plant disease pandemics to global food security. Proc. Natl. Acad. Sci. USA 2021, 118, e2022239118.
  2. Chaloner, T.M.; Gurr, S.J.; Bebber, D.P. Plant pathogen infection risk tracks global crop yields under climate change. Nat. Clim. Chang. 2021, 11, 710–715.
  3. FAO. New Standards to Curb the Global Spread of Plant Pests and Diseases; The Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2019; Available online: https://www.fao.org/news/story/en/item/1187738/icode/ (accessed on 16 January 2023).
  4. Savary, S.; Willocquet, L.; Pethybridge, S.J.; Esker, P.; McRoberts, N.; Nelson, A. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 2019, 3, 430–439.
  5. Chakraborty, S.; Newton, A.C. Climate change, plant diseases and food security: An overview. Plant Pathol. 2011, 60, 2–14.
  6. Gilbert, G.S. Evolutionary ecology of plant diseases in natural ecosystems. Annu. Rev. Phytopathol. 2002, 40, 13–43.
  7. Bennett, J.W.; Klich, M. Mycotoxins. In Encyclopedia of Microbiology, 3rd ed.; Schaechter, M., Ed.; Academic Press: Oxford, UK, 2009; pp. 559–565.
  8. Cao, S.; Luo, H.; Jin, M.A.; Jin, S.; Duan, X.; Zhou, Y.; Chen, W.; Liu, T.; Jia, Q.; Zhang, B.; et al. Intercropping influenced the occurrence of stripe rust and powdery mildew in wheat. Crop Prot. 2015, 70, 40–46.
  9. Verreet, J.A.; Klink, H.; Hoffmann, G.M. Regional monitoring for disease prediction and optimization of plant protection measures: The IPM wheat model. Plant Dis. 2000, 84, 816–826.
  10. Barbedo, J.G.A. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones 2019, 3, 40.
  11. Neupane, K.; Baysal-Gurel, F. Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sens. 2021, 13, 3841.
  12. Barbedo, J.G.A. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 2016, 144, 52–60.
  13. Bouguettaya, A.; Zarzour, H.; Kechida, A.; Taberkit, A.M. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. Cluster Comp. 2022, 26, 1297–1317.
  14. Shahi, T.B.; Xu, C.-Y.; Neupane, A.; Guo, W. Recent advances in crop disease detection using UAV and deep learning techniques. Remote Sens. 2023, 15, 2450.
  15. Kuswidiyanto, L.W.; Noh, H.H.; Han, X.Z. Plant disease diagnosis using deep learning based on aerial hyperspectral images: A review. Remote Sens. 2022, 14, 6031.
  16. Heidarian Dehkordi, R.; El Jarroudi, M.; Kouadio, L.; Meersmans, J.; Beyer, M. Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based Red-Green-Blue imagery. Remote Sens. 2020, 12, 3696.
  17. Su, J.Y.; Liu, C.J.; Coombes, M.; Hu, X.P.; Wang, C.H.; Xu, X.M.; Li, Q.D.; Guo, L.; Chen, W.H. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Comput. Electron. Agric. 2018, 155, 157–166.
  18. Bohnenkamp, D.; Behmann, J.; Mahlein, A.K. In-field detection of yellow rust in wheat on the ground canopy and UAV scale. Remote Sens. 2019, 11, 2495.
  19. Su, J.Y.; Liu, C.J.; Hu, X.P.; Xu, X.M.; Guo, L.; Chen, W.H. Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Comput. Electron. Agric. 2019, 167, 105035.
  20. Zhang, X.; Han, L.; Dong, Y.; Shi, Y.; Huang, W.; Han, L.; González-Moreno, P.; Ma, H.; Ye, H.; Sobeih, T. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens. 2019, 11, 1554.
  21. Guo, A.T.; Huang, W.J.; Dong, Y.Y.; Ye, H.C.; Ma, H.Q.; Liu, B.; Wu, W.B.; Ren, Y.; Ruan, C.; Geng, Y. Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sens. 2021, 13, 123.
  22. Pan, Q.; Gao, M.; Wu, P.; Yan, J.; Li, S. A deep-learning-based approach for wheat yellow rust disease recognition from unmanned aerial vehicle images. Sensors 2021, 21, 6540.
  23. Su, J.Y.; Yi, D.W.; Su, B.F.; Mi, Z.W.; Liu, C.J.; Hu, X.P.; Xu, X.M.; Guo, L.; Chen, W.H. Aerial visual perception in smart farming: Field study of wheat yellow rust monitoring. IEEE Trans. Indus. Inform. 2021, 17, 2242–2249.
  24. Deng, J.; Zhou, H.R.; Lv, X.; Yang, L.J.; Shang, J.L.; Sun, Q.Y.; Zheng, X.; Zhou, C.Y.; Zhao, B.Q.; Wu, J.C.; et al. Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs. Comput. Electron. Agric. 2022, 200, 107211.
  25. Zhang, T.; Yang, Z.; Xu, Z.; Li, J. Wheat yellow rust severity detection by efficient DF-Unet and UAV multispectral imagery. IEEE Sens. J. 2022, 22, 9057–9068.
  26. Liu, W.; Cao, X.R.; Fan, J.R.; Wang, Z.H.; Yan, Z.Y.; Luo, Y.; West, J.S.; Xu, X.M.; Zhou, Y.L. Detecting wheat powdery mildew and predicting grain yield using unmanned aerial photography. Plant Dis. 2018, 102, 1981–1988.
  27. Vagelas, I.; Cavalaris, C.; Karapetsi, L.; Koukidis, C.; Servis, D.; Madesis, P. Protective effects of Systiva® seed treatment fungicide for the control of winter wheat foliar diseases caused at early stages due to climate change. Agronomy 2022, 12, 2000.
  28. Francesconi, S.; Harfouche, A.; Maesano, M.; Balestra, G.M. UAV-based thermal, RGB imaging and gene expression analysis allowed detection of Fusarium head blight and gave new insights into the physiological responses to the disease in durum wheat. Front. Plant Sci. 2021, 12, 628575.
  29. Zhang, H.S.; Huang, L.S.; Huang, W.J.; Dong, Y.Y.; Weng, S.Z.; Zhao, J.L.; Ma, H.Q.; Liu, L.Y. Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion. Front. Plant Sci. 2022, 13, 4427.
  30. Van De Vijver, R.; Mertens, K.; Heungens, K.; Nuyttens, D.; Wieme, J.; Maes, W.H.; Van Beek, J.; Somers, B.; Saeys, W. Ultra-high-resolution UAV-based detection of Alternaria solani infections in potato fields. Remote Sens. 2022, 14, 6232.
  31. Sugiura, R.; Tsuda, S.; Tamiya, S.; Itoh, A.; Nishiwaki, K.; Murakami, N.; Shibuya, Y.; Hirafuji, M.; Nuske, S. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst. Eng. 2016, 148, 1–10.
  32. Duarte-Carvajalino, J.M.; Alzate, D.F.; Ramirez, A.A.; Santa-Sepulveda, J.D.; Fajardo-Rojas, A.E.; Soto-Suárez, M. Evaluating late blight severity in potato crops using unmanned aerial vehicles and machine learning algorithms. Remote Sens. 2018, 10, 1513.
  33. Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.F.; Suomalainen, J.; Kooistra, L. Feasibility of unmanned aerial vehicle optical imagery for early detection and severity assessment of late blight in Potato. Remote Sens. 2019, 11, 224.
  34. Shi, Y.; Han, L.X.; Kleerekoper, A.; Chang, S.; Hu, T.L. Novel CropdocNet model for automated potato late blight disease detection from unmanned aerial vehicle-based hyperspectral imagery. Remote Sens. 2022, 14, 396.
  35. Siebring, J.; Valente, J.; Franceschini, M.H.D.; Kamp, J.; Kooistra, L. Object-based image analysis applied to low altitude aerial imagery for potato plant trait retrieval and pathogen detection. Sensors 2019, 19, 5477.
  36. León-Rueda, W.A.; León, C.; Caro, S.G.; Ramírez-Gil, J.G. Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools. Trop. Plant Pathol. 2022, 47, 152–167.
  37. Prasad, A.; Mehta, N.; Horak, M.; Bae, W.D. A two-step machine learning approach for crop disease detection using GAN and UAV technology. Remote Sens. 2022, 14, 4765.
  38. Yağ, İ.; Altan, A. Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology 2022, 11, 1732.
  39. Xiao, D.Q.; Pan, Y.Q.; Feng, J.Z.; Yin, J.J.; Liu, Y.F.; He, L. Remote sensing detection algorithm for apple fire blight based on UAV multispectral image. Comput. Electron. Agric. 2022, 199, 107137.
  40. Lei, S.H.; Luo, J.B.; Tao, X.J.; Qiu, Z.X. Remote sensing detecting of yellow leaf disease of arecanut based on UAV multisource sensors. Remote Sens. 2021, 13, 4562.
  41. Calou, V.B.C.; Teixeira, A.d.S.; Moreira, L.C.J.; Lima, C.S.; de Oliveira, J.B.; de Oliveira, M.R.R. The use of UAVs in monitoring yellow sigatoka in banana. Biosyst. Eng. 2020, 193, 115–125.
  42. Gomez Selvaraj, M.; Vergara, A.; Montenegro, F.; Alonso Ruiz, H.; Safari, N.; Raymaekers, D.; Ocimati, W.; Ntamwira, J.; Tits, L.; Omondi, A.B.; et al. Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS J. Photogramm. Remote Sens. 2020, 169, 110–124.
  43. Alabi, T.R.; Adewopo, J.; Duke, O.P.; Kumar, P.L. Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease surveillance. Remote Sens. 2022, 14, 5206.
  44. Ye, H.C.; Huang, W.J.; Huang, S.Y.; Cui, B.; Dong, Y.Y.; Guo, A.T.; Ren, Y.; Jin, Y. Recognition of banana fusarium wilt based on UAV remote sensing. Remote Sens. 2020, 12, 938.
  45. Ye, H.C.; Huang, W.J.; Huang, S.Y.; Cui, B.; Dong, Y.Y.; Guo, A.T.; Ren, Y.; Jin, Y. Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery. Int. J. Agric. Biol. Eng. 2020, 13, 136–142.
  46. Zhang, S.M.; Li, X.H.; Ba, Y.X.; Lyu, X.G.; Zhang, M.Q.; Li, M.Z. Banana fusarium wilt disease detection by supervised and unsupervised methods from UAV-based multispectral imagery. Remote Sens. 2022, 14, 1231.
  47. Booth, J.C.; Sullivan, D.; Askew, S.A.; Kochersberger, K.; McCall, D.S. Investigating targeted spring dead spot management via aerial mapping and precision-guided fungicide applications. Crop Sci. 2021, 61, 3134–3144.
  48. Abdulridha, J.; Batuman, O.; Ampatzidis, Y. UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens. 2019, 11, 1373.
  49. DadrasJavan, F.; Samadzadegan, F.; Seyed Pourazar, S.H.; Fazeli, H. UAV-based multispectral imagery for fast citrus greening detection. J. Plant Dis. Prot. 2019, 126, 307–318.
  50. Pourazar, H.; Samadzadegan, F.; Javan, F.D. Aerial multispectral imagery for plant disease detection: Radiometric calibration necessity assessment. Eur. J. Remote Sens. 2019, 52, 17–31.
  51. Deng, X.L.; Zhu, Z.H.; Yang, J.C.; Zheng, Z.; Huang, Z.X.; Yin, X.B.; Wei, S.J.; Lan, Y.B. Detection of Citrus Huanglongbing based on multi-input neural network model of UAV hyperspectral remote sensing. Remote Sens. 2020, 12, 2678.
  52. Garza, B.N.; Ancona, V.; Enciso, J.; Perotto-Baldivieso, H.L.; Kunta, M.; Simpson, C. Quantifying citrus tree health using true color UAV images. Remote Sens. 2020, 12, 170.
  53. Moriya, É.A.S.; Imai, N.N.; Tommaselli, A.M.G.; Berveglieri, A.; Santos, G.H.; Soares, M.A.; Marino, M.; Reis, T.T. Detection and mapping of trees infected with citrus gummosis using UAV hyperspectral data. Comput. Electron. Agric. 2021, 188, 106298.
  54. Marin, D.B.; Ferraz, G.A.E.S.; Santana, L.S.; Barbosa, B.D.S.; Barata, R.A.P.; Osco, L.P.; Ramos, A.P.M.; Guimarães, P.H.S. Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models. Comput. Electron. Agric. 2021, 190, 106476.
  55. Soares, A.D.S.; Vieira, B.S.; Bezerra, T.A.; Martins, G.D.; Siquieroli, A.C.S. Early detection of coffee leaf rust caused by Hemileia vastatrix using multispectral images. Agronomy 2022, 12, 2911.
  56. Wang, T.Y.; Thomasson, J.A.; Isakeit, T.; Yang, C.H.; Nichols, R.L. A plant-by-plant method to identify and treat cotton root rot based on UAV remote sensing. Remote Sens. 2020, 12, 2453.
  57. Wang, T.Y.; Thomasson, J.A.; Yang, C.H.; Isakeit, T.; Nichols, R.L. Automatic classification of cotton root rot disease based on UAV remote sensing. Remote Sens. 2020, 12, 1310.
  58. Megat Mohamed Nazir, M.N.; Terhem, R.; Norhisham, A.R.; Mohd Razali, S.; Meder, R. Early monitoring of health status of plantation-grown eucalyptus pellita at large spatial scale via visible spectrum imaging of canopy foliage using unmanned aerial vehicles. Forests 2021, 12, 1393.
  59. di Gennaro, S.F.; Battiston, E.; di Marco, S.; Facini, O.; Matese, A.; Nocentini, M.; Palliotti, A.; Mugnai, L. Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathol. Mediterr. 2016, 55, 262–275.
  60. Kerkech, M.; Hafiane, A.; Canals, R. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Comput. Electron. Agric. 2018, 155, 237–243.
  61. Kerkech, M.; Hafiane, A.; Canals, R. VddNet: Vine disease detection network based on multispectral images and depth map. Remote Sens. 2020, 12, 3305.
  62. Dwivedi, R.; Dey, S.; Chakraborty, C.; Tiwari, S. Grape disease detection network based on multi-task learning and attention features. IEEE Sens. J. 2021, 21, 17573–17580.
  63. Albetis, J.; Duthoit, S.; Guttler, F.; Jacquin, A.; Goulard, M.; Poilvé, H.; Féret, J.B.; Dedieu, G. Detection of Flavescence dorée grapevine disease using Unmanned Aerial Vehicle (UAV) multispectral imagery. Remote Sens. 2017, 9, 308.
  64. Savian, F.; Martini, M.; Ermacora, P.; Paulus, S.; Mahlein, A.K. Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing. Remote Sens. 2020, 12, 2194.
  65. Carmo, G.J.D.; Castoldi, R.; Martins, G.D.; Jacinto, A.C.P.; Tebaldi, N.D.; Charlo, H.C.D.; Zampiroli, R. Detection of lesions in lettuce caused by Pectobacterium carotovorum subsp. carotovorum by supervised classification using multispectral images. Can. J. Remote Sens. 2022, 48, 144–157.
  66. Stewart, E.L.; Wiesner-Hanks, T.; Kaczmar, N.; DeChant, C.; Wu, H.; Lipson, H.; Nelson, R.J.; Gore, M.A. Quantitative phenotyping of northern leaf blight in uav images using deep learning. Remote Sens. 2019, 11, 2209.
  67. Wiesner-Hanks, T.; Wu, H.; Stewart, E.L.; DeChant, C.; Kaczmar, N.; Lipson, H.; Gore, M.A.; Nelson, R.J. Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data. Front. Plant Sci. 2019, 10, 1550.
  68. Wu, H.; Wiesner-Hanks, T.; Stewart, E.L.; DeChant, C.; Kaczmar, N.; Gore, M.A.; Nelson, R.J.; Lipson, H. Autonomous detection of plant disease symptoms directly from aerial imagery. Plant Phenome J. 2019, 2, 1–9.
  69. Gao, J.M.; Ding, M.L.; Sun, Q.Y.; Dong, J.Y.; Wang, H.Y.; Ma, Z.H. Classification of southern corn rust severity based on leaf-level hyperspectral data collected under solar illumination. Remote Sens. 2022, 14, 2551.
  70. Chivasa, W.; Mutanga, O.; Biradar, C. UAV-based multispectral phenotyping for disease resistance to accelerate crop improvement under changing climate conditions. Remote Sens. 2020, 12, 2445.
  71. Chivasa, W.; Mutanga, O.; Burgueño, J. UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation. Comput. Electron. Agric. 2021, 184, 106128.
  72. Oh, S.; Lee, D.Y.; Gongora-Canul, C.; Ashapure, A.; Carpenter, J.; Cruz, A.P.; Fernandez-Campos, M.; Lane, B.Z.; Telenko, D.E.P.; Jung, J.; et al. Tar spot disease quantification using unmanned aircraft systems (UAS) data. Remote Sens. 2021, 13, 2567.
  73. Ganthaler, A.; Losso, A.; Mayr, S. Using image analysis for quantitative assessment of needle bladder rust disease of Norway spruce. Plant Pathol. 2018, 67, 1122–1130.
  74. Izzuddin, M.A.; Hamzah, A.; Nisfariza, M.N.; Idris, A.S. Analysis of multispectral imagery from unmanned aerial vehicle (UAV) using object-based image analysis for detection of ganoderma disease in oil palm. J. Oil Palm Res. 2020, 32, 497–508.
  75. Ahmadi, P.; Mansor, S.; Farjad, B.; Ghaderpour, E. Unmanned aerial vehicle (UAV)-based remote sensing for early-stage detection of Ganoderma. Remote Sens. 2022, 14, 1239.
  76. Kurihara, J.; Koo, V.C.; Guey, C.W.; Lee, Y.P.; Abidin, H. Early detection of basal stem rot disease in oil palm tree using unmanned aerial vehicle-based hyperspectral imaging. Remote Sens. 2022, 14, 799.
  77. Cao, F.; Liu, F.; Guo, H.; Kong, W.W.; Zhang, C.; He, Y. Fast detection of Sclerotinia sclerotiorum on oilseed rape leaves using low-altitude remote sensing technology. Sensors 2018, 18, 4464.
  78. Rangarajan, A.K.; Balu, E.J.; Boligala, M.S.; Jagannath, A.; Ranganathan, B.N. A low-cost UAV for detection of Cercospora leaf spot in okra using deep convolutional neural network. Multimed. Tools Appl. 2022, 81, 21565–21589.
  79. Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 2013, 139, 231–245.
  80. Di Nisio, A.; Adamo, F.; Acciani, G.; Attivissimo, F. Fast detection of olive trees affected by Xylella fastidiosa from UAVs using multispectral imaging. Sensors 2020, 20, 4915.
  81. Castrignanò, A.; Belmonte, A.; Antelmi, I.; Quarto, R.; Quarto, F.; Shaddad, S.; Sion, V.; Muolo, M.R.; Ranieri, N.A.; Gadaleta, G.; et al. A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees. Sci. Total Environ. 2021, 752, 141814.
  82. Ksibi, A.; Ayadi, M.; Soufiene, B.O.; Jamjoom, M.M.; Ullah, Z. MobiRes-Net: A hybrid deep learning model for detecting and classifying olive leaf diseases. Appl. Sci. 2022, 12, 10278.
  83. Alberto, R.T.; Rivera, J.C.E.; Biagtan, A.R.; Isip, M.F. Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries. Spat. Inf. Res. 2020, 28, 383–389.
  84. McDonald, M.R.; Tayviah, C.S.; Gossen, B.D. Human vs. Machine, the eyes have it. Assessment of Stemphylium leaf blight on onion using aerial photographs from an NIR camera. Remote Sens. 2022, 14, 293.
  85. Calderón, R.; Montes-Borrego, M.; Landa, B.B.; Navas-Cortés, J.A.; Zarco-Tejada, P.J. Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. Precision Agric. 2014, 15, 639–661.
  86. Sandino, J.; Pegg, G.; Gonzalez, F.; Smith, G. Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence. Sensors 2018, 18, 944.
  87. Bagheri, N. Application of aerial remote sensing technology for detection of fire blight infected pear trees. Comput. Electron. Agric. 2020, 168, 105147.
  88. Chen, T.; Yang, W.; Zhang, H.; Zhu, B.; Zeng, R.; Wang, X.; Wang, S.; Wang, L.; Qi, H.; Lan, Y.; et al. Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data. Comput. Electron. Agric. 2020, 177, 105708.
  89. Li, F.D.; Liu, Z.Y.; Shen, W.X.; Wang, Y.; Wang, Y.L.; Ge, C.K.; Sun, F.G.; Lan, P. A remote sensing and airborne edge-computing based detection system for pine wilt disease. IEEE Access 2021, 9, 66346–66360.
  90. Wu, B.Z.; Liang, A.J.; Zhang, H.F.; Zhu, T.F.; Zou, Z.Y.; Yang, D.M.; Tang, W.Y.; Li, J.; Su, J. Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning. For. Ecol. Manag. 2021, 486, 118986.
  91. Yu, R.; Ren, L.L.; Luo, Y.Q. Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery. For. Ecosyst. 2021, 8, 44.
  92. Liang, D.; Liu, W.; Zhao, L.; Zong, S.; Luo, Y. An improved convolutional neural network for plant disease detection using unmanned aerial vehicle images. Nat. Environ. Pollut. Technol. 2022, 21, 899–908.
  93. Yu, R.; Huo, L.N.; Huang, H.G.; Yuan, Y.; Gao, B.T.; Liu, Y.J.; Yu, L.F.; Li, H.A.; Yang, L.Y.; Ren, L.L.; et al. Early detection of pine wilt disease tree candidates using time-series of spectral signatures. Front. Plant Sci. 2022, 13, 1000093.
  94. Smigaj, M.; Gaulton, R.; Suárez, J.C.; Barr, S.L. Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity. For. Ecol. Manag. 2019, 433, 699–708.
  95. Dang, L.M.; Hassan, S.I.; Suhyeon, I.; Sangaiah, A.K.; Mehmood, I.; Rho, S.; Seo, S.; Moon, H. UAV based wilt detection system via convolutional neural networks. Sustain. Comput.-Infor. 2020, 28, 100250.
  96. Dang, L.M.; Wang, H.; Li, Y.; Min, K.; Kwak, J.T.; Lee, O.N.; Park, H.; Moon, H. Fusarium wilt of radish detection using RGB and near infrared images from unmanned aerial vehicles. Remote Sens. 2020, 12, 2863.
  97. Zhang, D.; Zhou, X.; Zhang, J.; Lan, Y.; Xu, C.; Liang, D. Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS ONE 2018, 13, e0187470.
  98. Kharim, M.N.A.; Wayayok, A.; Fikri Abdullah, A.; Rashid Mohamed Shariff, A.; Mohd Husin, E.; Razif Mahadi, M. Predictive zoning of pest and disease infestations in rice field based on UAV aerial imagery. Egypt. J. Remote Sens. Space Sci. 2022, 25, 831–840.
  99. Tetila, E.C.; Machado, B.B.; Menezes, G.K.; Da Silva Oliveira, A., Jr.; Alvarez, M.; Amorim, W.P.; De Souza Belete, N.A.; Da Silva, G.G.; Pistori, H. Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks. IEEE Geosci. Remote Sens. 2020, 17, 903–907.
  100. Castelao Tetila, E.; Brandoli Machado, B.; Belete, N.A.D.S.; Guimaraes, D.A.; Pistori, H. Identification of soybean foliar diseases using unmanned aerial vehicle images. IEEE Geosci. Remote Sens. 2017, 14, 2190–2194.
  101. Babu, R.G.; Chellaswamy, C. Different stages of disease detection in squash plant based on machine learning. J. Biosci. 2022, 47, 9.
  102. Jay, S.; Comar, A.; Benicio, R.; Beauvois, J.; Dutartre, D.; Daubige, G.; Li, W.; Labrosse, J.; Thomas, S.; Henry, N.; et al. Scoring Cercospora leaf spot on sugar beet: Comparison of UGV and UAV phenotyping systems. Plant Phenomics 2020, 2020, 9452123.
  103. Rao Pittu, V.S. Image processing system integrated multicopter for diseased area and disease recognition in agricultural farms. Int. J. Control Autom. 2020, 13, 219–230.
  104. Rao Pittu, V.S.; Gorantla, S.R. Diseased area recognition and pesticide spraying in farming lands by multicopters and image processing system. J. Eur. Syst. Autom. 2020, 53, 123–130.
  105. Görlich, F.; Marks, E.; Mahlein, A.K.; König, K.; Lottes, P.; Stachniss, C. UAV-based classification of Cercospora leaf spot using RGB images. Drones 2021, 5, 34.
  106. Gunder, M.; Yamati, F.R.I.; Kierdorf, J.; Roscher, R.; Mahlein, A.K.; Bauckhage, C. Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision. Gigascience 2022, 11, giac054.
  107. Ispizua Yamati, F.R.; Barreto, A.; Günder, M.; Bauckhage, C.; Mahlein, A.K. Sensing the occurrence and dynamics of Cercospora leaf spot disease using UAV-supported image data and deep learning. Zuckerindustrie 2022, 147, 79–86.
  108. Joalland, S.; Screpanti, C.; Varella, H.V.; Reuther, M.; Schwind, M.; Lang, C.; Walter, A.; Liebisch, F. Aerial and ground based sensing of tolerance to beet cyst nematode in sugar beet. Remote Sens. 2018, 10, 787.
  109. Narmilan, A.; Gonzalez, F.; Salgadoe, A.S.A.; Powell, K. Detection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images. Drones 2022, 6, 230.
  110. Xu, Y.P.; Shrestha, V.; Piasecki, C.; Wolfe, B.; Hamilton, L.; Millwood, R.J.; Mazarei, M.; Stewart, C.N. Sustainability trait modeling of field-grown switchgrass (Panicum virgatum) using UAV-based imagery. Plants 2021, 10, 2726.
  111. Zhao, X.H.; Zhang, J.C.; Tang, A.L.; Yu, Y.F.; Yan, L.J.; Chen, D.M.; Yuan, L. The stress detection and segmentation strategy in tea plant at canopy level. Front. Plant Sci. 2022, 13, 9054.
  112. Yamamoto, K.; Togami, T.; Yamaguchi, N. Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors 2017, 17, 2557.
  113. Abdulridha, J.; Ampatzidis, Y.; Kakarla, S.C.; Roberts, P. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precis. Agric. 2020, 21, 955–978.
  114. Abdulridha, J.; Ampatzidis, Y.; Qureshi, J.; Roberts, P. Laboratory and UAV-based identification and classification of tomato yellow leaf curl, bacterial spot, and target spot diseases in tomato utilizing hyperspectral imaging and machine learning. Remote Sens. 2020, 12, 2732.
  115. Kalischuk, M.; Paret, M.L.; Freeman, J.H.; Raj, D.; Da Silva, S.; Eubanks, S.; Wiggins, D.J.; Lollar, M.; Marois, J.J.; Mellinger, H.C.; et al. An improved crop scouting technique incorporating unmanned aerial vehicle-assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon. Plant Dis. 2019, 103, 1642–1650.
  116. Abdulridha, J.; Ampatzidis, Y.; Qureshi, J.; Roberts, P. Identification and classification of downy mildew severity stages in watermelon utilizing aerial and ground remote sensing and machine learning. Front. Plant Sci. 2022, 13, 791018.
  117. Mahlein, A.-K.; Oerke, E.-C.; Steiner, U.; Dehne, H.-W. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 2012, 133, 197–209.
  118. Maimaitijiang, M.; Sagan, V.; Sidike, P.; Daloye, A.M.; Erkbol, H.; Fritschi, F.B. Crop monitoring using satellite/UAV data fusion and machine learning. Remote Sens. 2020, 12, 1357.
  119. Alvarez-Vanhard, E.; Corpetti, T.; Houet, T. UAV & satellite synergies for optical remote sensing applications: A literature review. Sci. Remote Sens. 2021, 3, 100019.
  120. Cardoso, R.M.; Pereira, T.S.; Facure, M.H.M.; dos Santos, D.M.; Mercante, L.A.; Mattoso, L.H.C.; Correa, D.S. Current progress in plant pathogen detection enabled by nanomaterials-based (bio)sensors. Sens. Actuators Rep. 2022, 4, 100068.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Agronomy
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Louis Kouadio
,
Moussa El Jarroudi
,
Zineb Belabess
,
Salah-Eddine Laasli
,
Md Zohurul Kadir Roni
,
Ibn Dahou Idrissi Amine
,
Nourreddine Mokhtari
,
Fouad Mokrini
,
Jürgen Junk
,
Rachid Lahlali
View Times: 309
Revisions: 4 times (View History)
Update Date: 27 Oct 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback