{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T13:38:59Z","timestamp":1760621939049,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wabash Heartland Innovation Network (WHIN)","award":["18024589","1012501"],"award-info":[{"award-number":["18024589","1012501"]}]},{"name":"USDA National Institute of Food and Agriculture (NIFA) Hatch project","award":["18024589","1012501"],"award-info":[{"award-number":["18024589","1012501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management systems. However, the use of deep learning and unmanned aerial system (UAS) imagery to track the spread of diseases, identify diseased regions within cornfields, and notify users with actionable information remains a research gap. Therefore, in this study, high-resolution, UAS-acquired, real-time kinematic (RTK) geotagged, RGB imagery at an altitude of 12 m above ground level (AGL) was used to develop the Geo Disease Location System (GeoDLS), a deep learning-based system for tracking diseased regions in corn fields. UAS images (resolution 8192 \u00d7 5460 pixels) were acquired in cornfields located at Purdue University\u2019s Agronomy Center for Research and Education (ACRE), using a DJI Matrice 300 RTK UAS mounted with a 45-megapixel DJI Zenmuse P1 camera during corn stages V14 to R4. A dataset of 5076 images was created by splitting the UAS-acquired images using tile and simple linear iterative clustering (SLIC) segmentation. For tile segmentation, the images were split into tiles of sizes 250 \u00d7 250 pixels, 500 \u00d7 500 pixels, and 1000 \u00d7 1000 pixels, resulting in 1804, 1112, and 570 image tiles, respectively. For SLIC segmentation, 865 and 725 superpixel images were obtained using compactness (m) values of 5 and 10, respectively. Five deep neural network architectures, VGG16, ResNet50, InceptionV3, DenseNet169, and Xception, were trained to identify diseased, healthy, and background regions in corn fields. DenseNet169 identified diseased, healthy, and background regions with the highest testing accuracy of 100.00% when trained on images of tile size 1000 \u00d7 1000 pixels. Using a sliding window approach, the trained DenseNet169 model was then used to calculate the percentage of diseased regions present within each UAS image. Finally, the RTK geolocation information for each image was used to update users with the location of diseased regions with an accuracy of within 2 cm through a web application, a smartphone application, and email notifications. The GeoDLS could be a potential tool for an automated disease management system to track the spread of crop diseases, identify diseased regions, and provide actionable information to the users.<\/jats:p>","DOI":"10.3390\/rs14174140","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"4140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7431-0808","authenticated-orcid":false,"given":"Aanis","family":"Ahmad","sequence":"first","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0442-5474","authenticated-orcid":false,"given":"Varun","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8882-0510","authenticated-orcid":false,"given":"Dharmendra","family":"Saraswat","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aly","family":"El Gamal","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gurmukh S.","family":"Johal","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114514","DOI":"10.1016\/j.eswa.2020.114514","article-title":"Identification of Rice Plant Diseases Using Lightweight Attention Networks","volume":"169","author":"Chen","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tudi, M., Ruan, H.D., Wang, L., Lyu, J., Sadler, R., Connell, D., Chu, C., and Phung, D.T. (2021). Agriculture Development, Pesticide Application and Its Impact on the Environment. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18031112"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/07352681003617285","article-title":"Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging","volume":"29","author":"Bock","year":"2010","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106081","DOI":"10.1016\/j.compag.2021.106081","article-title":"Performance of Deep Learning Models for Classifying and Detecting Common Weeds in Corn and Soybean Production Systems","volume":"184","author":"Ahmad","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Saraswat, D., El Gamal, A., and Johal, G.S. (2021, January 12\u201316). Comparison of Deep Learning Models for Corn Disease Identification, Tracking, and Severity Estimation Using Images Acquired from Uav-Mounted and Handheld Sensors. Proceedings of the 2021 Annual International Meeting ASABE Virtual and On Demand, virtual.","DOI":"10.13031\/aim.202100566"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, A.X., Tran, C., Desai, N., Lobell, D., and Ermon, S. (2018, January 20\u201322). Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS 2018, San Jose, CA, USA.","DOI":"10.1145\/3209811.3212707"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104906","DOI":"10.1016\/j.compag.2019.104906","article-title":"Crop Pest Classification Based on Deep Convolutional Neural Network and Transfer Learning","volume":"164","author":"Thenmozhi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/LGRS.2019.2954735","article-title":"A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests","volume":"17","author":"Tetila","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kitano, B.T., Mendes, C.C.T., Geus, A.R., Oliveira, H.C., and Souza, J.R. (2019). Corn Plant Counting Using Deep Learning and UAV Images. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2019.2930549"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xie, Q., Wang, J., Lopez-Sanchez, J.M., Peng, X., Liao, C., Shang, J., Zhu, J., Fu, H., and Ballester-Berman, J.D. (2021). Crop Height Estimation of Corn from Multi-Year Radarsat-2 Polarimetric Observables Using Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13030392"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Etienne, A., Ahmad, A., Aggarwal, V., and Saraswat, D. (2021). Deep Learning-Based Object Detection System for Identifying Weeds Using Uas Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13245182"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jahan, N., Zhang, Z., Liu, Z., Friskop, A., Flores, P., Mathew, J., and Das, A.K. (2021, January 12\u201316). Using Images from a Handheld Camera to Detect Wheat Bacterial Leaf Streak Disease Severities. Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021, online.","DOI":"10.13031\/aim.202100112"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wiesner-Hanks, T., Stewart, E.L., Kaczmar, N., Dechant, C., Wu, H., Nelson, R.J., Lipson, H., and Gore, M.A. (2018). Image Set for Deep Learning: Field Images of Maize Annotated with Disease Symptoms. BMC Res. Notes, 11.","DOI":"10.1186\/s13104-018-3548-6"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s11119-018-9601-6","article-title":"Design and Field Evaluation of a Ground Robot for High-Throughput Phenotyping of Energy Sorghum","volume":"20","author":"Young","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.biosystemseng.2018.05.013","article-title":"Factors Influencing the Use of Deep Learning for Plant Disease Recognition","volume":"172","author":"Barbedo","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6334","DOI":"10.1038\/s41598-022-10140-z","article-title":"Deep Learning-Based Approach for Identification of Diseases of Maize Crop","volume":"12","author":"Haque","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s40030-019-00390-y","article-title":"Identification of Soybean Leaf Diseases via Deep Learning","volume":"100","author":"Wu","year":"2019","journal-title":"J. Inst. Eng. Ser. A"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106042","DOI":"10.1016\/j.compag.2021.106042","article-title":"A Deep Learning Approach for RGB Image-Based Powdery Mildew Disease Detection on Strawberry Leaves","volume":"183","author":"Shin","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.3389\/fpls.2020.01082","article-title":"Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks","volume":"11","author":"Liu","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_20","first-page":"100407","article-title":"Development of Efficient CNN Model for Tomato Crop Disease Identification","volume":"28","author":"Agarwal","year":"2020","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","article-title":"A Recognition Method for Cucumber Diseases Using Leaf Symptom Images Based on Deep Convolutional Neural Network","volume":"154","author":"Ma","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Neupane, K., and Baysal-Gurel, F. (2021). Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13193841"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.biosystemseng.2016.01.017","article-title":"A Review on the Main Challenges in Automatic Plant Disease Identification Based on Visible Range Images","volume":"144","author":"Barbedo","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100083","DOI":"10.1016\/j.atech.2022.100083","article-title":"A Survey on Using Deep Learning Techniques for Plant Disease Diagnosis and Recommendations for Development of Appropriate Tools","volume":"3","author":"Ahmad","year":"2022","journal-title":"Smart Agric. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1038\/s41598-017-04501-2","article-title":"Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-Learning Classifiers","volume":"7","author":"Zhu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., and Kwasniewski, M.T. (2021). Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21030742"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Ampatzidis, Y., Qureshi, J., and Roberts, P. (2020). 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., 12.","DOI":"10.3390\/rs12172732"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.biosystemseng.2020.07.001","article-title":"Detecting Powdery Mildew Disease in Squash at Different Stages Using UAV-Based Hyperspectral Imaging and Artificial Intelligence","volume":"197","author":"Abdulridha","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s11119-019-09703-4","article-title":"Detection of Target Spot and Bacterial Spot Diseases in Tomato Using UAV-Based and Benchtop-Based Hyperspectral Imaging Techniques","volume":"21","author":"Abdulridha","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine Disease Detection in UAV Multispectral Images Using Optimized Image Registration and Deep Learning Segmentation Approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., and Jin, Y. (2020). Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing. Remote Sens., 12.","DOI":"10.3390\/rs12060938"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.trac.2019.05.022","article-title":"Advanced Spectroscopic Techniques for Plant Disease Diagnostics. A Review","volume":"118","author":"Farber","year":"2019","journal-title":"TrAC\u2014Trends Anal. Chem."},{"key":"ref_33","first-page":"27","article-title":"Recent Advances in Image Processing Techniques for Automated Leaf Pest and Disease Recognition\u2013A Review","volume":"8","author":"Ngugi","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2135\/tppj2019.03.0006","article-title":"Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery","volume":"2","author":"Wu","year":"2019","journal-title":"Plant Phenome J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Stewart, E.L., Wiesner-Hanks, T., Kaczmar, N., DeChant, C., Wu, H., Lipson, H., Nelson, R.J., and Gore, M.A. (2019). Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11192209"},{"key":"ref_36","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and S\u00fcsstrunk, S. (2010). SLIC Superpixels. Tech. Rep. EPFL."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101465","DOI":"10.1016\/j.ecoinf.2021.101465","article-title":"Machine Learning and SLIC for Tree Canopies Segmentation in Urban Areas","volume":"66","author":"Martins","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/LGRS.2019.2932385","article-title":"Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks","volume":"17","author":"Tetila","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Trindade, L.D.G., Basso, F.P., de Macedo Rodrigues, E., Bernardino, M., Welfer, D., and M\u00fcller, D. (2021). Analysis of the Superpixel Slic Algorithm for Increasing Data for Disease Detection Using Deep Learning. Electr. Distrib., 488\u2013497.","DOI":"10.1007\/978-3-030-71187-0_45"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"194","DOI":"10.18517\/ijaseit.9.1.5322","article-title":"An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels","volume":"9","author":"Kemper","year":"2019","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1007\/s00521-017-3067-8","article-title":"Plant Disease Leaf Image Segmentation Based on Superpixel Clustering and EM Algorithm","volume":"31","author":"Zhang","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sugiura, R., Tsuda, S., Tsuji, H., and Murakami, N. (2018, January 31). Virus-Infected Plant Detection in Potato Seed Production Field by UAV Imagery. Proceedings of the 2018 ASABE Annual International Meeting, Detroit, MI, USA.","DOI":"10.13031\/aim.201800594"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pan, Q., Gao, M., Wu, P., Yan, J., and Li, S. (2021). A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images. Sensors, 21.","DOI":"10.3390\/s21196540"},{"key":"ref_45","first-page":"38","article-title":"Use of UAV Images to Assess Narrow Brown Leaf Spot Severity in Rice","volume":"2","author":"Cai","year":"2019","journal-title":"Int. J. Precis. Agric. Aviat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"9515","DOI":"10.1007\/s10586-018-2482-7","article-title":"The Recognition of Rice Images by UAV Based on Capsule Network","volume":"22","author":"Li","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonz\u00e1lez-Moreno, P., Ma, H., Ye, H., and Sobeih, T. (2019). A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images. Remote Sens., 11.","DOI":"10.3390\/rs11131554"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/03772063.2019.1583610","article-title":"Sliding Window Based Support Vector Machine System for Classification of Breast Cancer Using Histopathological Microscopic Images","volume":"68","author":"Alqudah","year":"2022","journal-title":"IETE J. Res."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., and Zuair, M. (2017). Deep Learning Approach for Car Detection in UAV Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040312"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/JSTARS.2020.2983788","article-title":"DeepWindow: Sliding Window Based on Deep Learning for Road Extraction from Remote Sensing Images","volume":"13","author":"Lian","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/978-981-15-9651-3_70","article-title":"Lane Detection Using Sliding Window for Intelligent Ground Vehicle Challenge","volume":"59","author":"Samantaray","year":"2021","journal-title":"Lect. Notes Data Eng. Commun. Technol."},{"key":"ref_52","unstructured":"DJI Official (2022, August 17). D-RTK 2\u2014Product Information. Available online: https:\/\/www.dji.com\/d-rtk-2\/info#specs."},{"key":"ref_53","first-page":"7","article-title":"Positioning Accuracy Assessment of a Commercial RTK UAS","volume":"11414","author":"Zhao","year":"2020","journal-title":"Auton. Air Ground Sens. Syst. Agric. Optim. Phenotyping V"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"919","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-919-2020","article-title":"Modelling of Glacier and Ice Sheet Micro-Topography Based on Unmanned Aerial Vehicle Data, Antarctica","volume":"43","author":"Yuan","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_55","unstructured":"Wise, K. (2022, August 17). Northern Corn Leaf Blight. Available online: http:\/\/www.extension.purdue.edu\/extmedia\/BP\/BP-84-W.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bah, M.D., Hafiane, A., and Canals, R. (December, January 28). Weeds Detection in UAV Imagery Using SLIC and the Hough Transform. Proceedings of the 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, QC, Canada.","DOI":"10.1109\/IPTA.2017.8310102"},{"key":"ref_57","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_58","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (1996, January 18\u201320). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA."},{"key":"ref_59","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (1997, January 17\u201319). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2018, January 18\u201323). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2016, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1094\/PDIS-03-15-0319-RE","article-title":"Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity","volume":"99","author":"Pethybridge","year":"2015","journal-title":"Plant Dis."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Valdoria, J.C., Caballeo, A.R., Fernandez, B.I.D., and Condino, J.M.M. (2019, January 24\u201325). IDahon: An Android Based Terrestrial Plant Disease Detection Mobile Application through Digital Image Processing Using Deep Learning Neural Network Algorithm. Proceedings of the 2019 4th International Conference on Information Technology (InCIT), Bangkok, Thailand.","DOI":"10.1109\/INCIT.2019.8912053"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Andrianto, H., Faizal, A., and Armandika, F. (2020, January 19\u201323). Smartphone Application for Deep Learning-Based Rice Plant Disease Detection. Proceedings of the 2020 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung-Padang, Indonesia.","DOI":"10.1109\/ICITSI50517.2020.9264942"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:05Z","timestamp":1760141645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":64,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174140"],"URL":"https:\/\/doi.org\/10.3390\/rs14174140","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,23]]}}}