{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T23:03:08Z","timestamp":1778540588340,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2021,9,10]]},"abstract":"<jats:p>Detection and diagnosis of the plant diseases in the early stage significantly minimize yield losses. Image-based automated plant diseases identification (APDI) tools have started to been widely used in pest managements strategies. The current APDI systems rely on images captured in laboratory conditions, which hardens the usage of the APDI systems by smallholder farmers. In this study, we investigate whether the smallholder farmers can exploit APDI systems using their basic and cheap unmanned autonomous vehicles (UAVs) with standard cameras. To create the tomato images like the one taken by UAVs, we build a new dataset from a public dataset by using image processing tools. The dataset includes tomato leaf photographs separated into 10 classes (diseases or healthy). To detect the diseases, we develop a new hybrid detection model, called SpikingTomaNet, which merges a novel deep convolutional neural network model with spiking neural network (SNN) model. This hybrid model provides both better accuracy rates for the plant diseases identification and more energy efficiency for the battery-constrained UAVs due to the SNN\u2019s event-driven architecture. In this hybrid model, the features extracted from the CNN model are used as the input layer for SNNs. To assess our approach\u2019s performance, firstly, we compare the proposed CNN model inside the developed hybrid model with well-known AlexNet, VggNet-5 and LeNet models. Secondly, we compare the developed hybrid model with three hybrid models composed of combinations of the well-known models and SNN model. To train and test the proposed neural network, 32022 images in the dataset are exploited. The results show that the SNN method significantly increases the success, especially in the augmented dataset. The experiment result shows that while the proposed hybrid model provides 97.78% accuracy on original images, its success on the created datasets is between 59.97%\u201382.98%. In addition, the results shows that the proposed hybrid model provides better overall accuracy in the classification of the diseases in comparison to the well-known models and LeNet and their combination with SNN.<\/jats:p>","DOI":"10.3233\/aic-210009","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T10:52:10Z","timestamp":1627642330000},"page":"147-162","source":"Crossref","is-referenced-by-count":6,"title":["Drone-assisted automated plant diseases identification using spiking deep conventional neural learning"],"prefix":"10.1177","volume":"34","author":[{"given":"Kubilay","family":"Demir","sequence":"first","affiliation":[{"name":"Electrical-Electronics Engineering Department, Bitlis Eren University, Bitlis, Turkey. E-mail:\u00a0kdemir@beu.edu.tr"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vedat","family":"T\u00fcmen","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Bitlis Eren University, Bitlis, Turkey. E-mail:\u00a0vtumen@beu.edu.tr"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-210009_ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101182"},{"key":"10.3233\/AIC-210009_ref4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2019.02.016","article-title":"Classification of myocardial infarction with multi-lead ECG signals and deep CNN","author":"Baloglu","year":"2019","journal-title":"Pattern Recognition Letters"},{"issue":"144","key":"10.3233\/AIC-210009_ref5","doi-asserted-by":"publisher","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":"1","author":"Barbedo","year":"2016","journal-title":"Biosystems engineering."},{"key":"10.3233\/AIC-210009_ref6","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.biosystemseng.2019.02.002","article-title":"Plant disease identification from individual lesions and spots using deep learning","volume":"180","author":"Barbedo","year":"2019","journal-title":"Biosystems Engineering."},{"key":"10.3233\/AIC-210009_ref7","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11263-014-0788-3","article-title":"Spiking deep convolutional neural networks for energy-efficient object recognition","volume":"113","author":"Cao","year":"2015","journal-title":"Int J Comput Vis"},{"key":"10.3233\/AIC-210009_ref8","doi-asserted-by":"crossref","unstructured":"Q.H.\u00a0Cap, H.\u00a0Uga, S.\u00a0Kagiwada and H.\u00a0Iyatomi, Leafgan: An effective data augmentation method for practical plant disease diagnosis, IEEE Transactions on Automation Science and Engineering (2020).","DOI":"10.1109\/TASE.2020.3041499"},{"key":"10.3233\/AIC-210009_ref10","doi-asserted-by":"crossref","unstructured":"H.\u00a0Durmus, E.O.\u00a0Gunes and M.\u00a0Kirci, Disease detection on the leaves of the tomato plants by using deep learning, in: 2017 6th International Conference on Agro-Geoinformatics, IEEE, 2017, pp.\u00a01\u20135.","DOI":"10.1109\/Agro-Geoinformatics.2017.8047016"},{"key":"10.3233\/AIC-210009_ref11","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/AIC-210009_ref15","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compag.2019.01.041","article-title":"Analysis of transfer learning for deep neural network based plant classification models","volume":"158","author":"Kaya","year":"2019","journal-title":"Computers and electronics in agriculture"},{"key":"10.3233\/AIC-210009_ref16","unstructured":"A.\u00a0Krizhevsky, I.\u00a0Sutskever and G.H.E.\u00a0Hinton, ImageNet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012."},{"key":"10.3233\/AIC-210009_ref17","first-page":"196","article-title":"Gradient-based learning applied to document recognition","author":"leChun","year":"1990","journal-title":"Proc. Advances in Neural Information Processing Systems"},{"key":"10.3233\/AIC-210009_ref18","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"10.3233\/AIC-210009_ref19","doi-asserted-by":"crossref","unstructured":"K.G.\u00a0Liakos et al., Machine learning in agriculture: A review, Sensors 18(8) (2018), 2674.","DOI":"10.3390\/s18082674"},{"key":"10.3233\/AIC-210009_ref20","doi-asserted-by":"crossref","unstructured":"B.\u00a0Liu, Y.\u00a0Zhang, D.\u00a0He and Y.\u00a0Li, Identification of apple leaf diseases based on deep convolutional neural networks, Symmetry 10(1) (2018), 11.","DOI":"10.3390\/sym10010011"},{"key":"10.3233\/AIC-210009_ref21","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neunet.2019.09.004","article-title":"Spiking neural networks and online learning: An overview and perspectives","volume":"121","author":"Lobo","year":"2020","journal-title":"Neural Networks"},{"key":"10.3233\/AIC-210009_ref22","doi-asserted-by":"crossref","unstructured":"S.P.\u00a0Mohanty, D.P.\u00a0Hughes and M.\u00a0Salathe, Using deep learning for image-based plant disease detection, Frontiers in plant science 22(7) (2016), 1419.","DOI":"10.3389\/fpls.2016.01419"},{"issue":"10","key":"10.3233\/AIC-210009_ref24","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.tplants.2018.07.004","article-title":"Deep learning for plant stress phenotyping: Trends and future perspectives","volume":"23","author":"Singh","year":"2018","journal-title":"Trends in plant science"},{"key":"10.3233\/AIC-210009_ref25","doi-asserted-by":"crossref","unstructured":"S.\u00a0Sladojevic, M.\u00a0Arsenovic, A.\u00a0Anderla, D.\u00a0Culibrk and D.\u00a0Stefanovic, Deep neural networks based recognition of plant diseases by leaf image classification, Computational intelligence and neuroscience 2016 (2016).","DOI":"10.1155\/2016\/3289801"},{"key":"10.3233\/AIC-210009_ref26","doi-asserted-by":"crossref","unstructured":"C.\u00a0Song, W.\u00a0Xu, Z.\u00a0Wang, S.\u00a0Yu, P.\u00a0Zeng and Z.\u00a0Ju, Analysis on the impact of data augmentation on target recognition for UAV-based transmission line inspection, Complexity (2020).","DOI":"10.1155\/2020\/3107450"},{"key":"10.3233\/AIC-210009_ref28","doi-asserted-by":"crossref","unstructured":"T.\u00a0Tang, Z.\u00a0Deng, S.\u00a0Zhou, L.\u00a0Lei and H.\u00a0Zou, Fast vehicle detection in UAV images, in: 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), IEEE, 2017, pp.\u00a01\u20135.","DOI":"10.1109\/RSIP.2017.7958795"},{"key":"10.3233\/AIC-210009_ref29","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neunet.2018.12.002","article-title":"Deep learning in spiking neural networks","volume":"111","author":"Tavanaei","year":"2019","journal-title":"Neural Networks"},{"key":"10.3233\/AIC-210009_ref31","first-page":"1","article-title":"Detection of weather images by using spiking neural networks of deep learning models","author":"Toga\u00e7ar","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/AIC-210009_ref32","first-page":"1646","article-title":"CSNN: An augmented spiking based framework with perceptron-inception","author":"Xu","year":"2018","journal-title":"IJCAI Int. Jt. Conf. Artif. Intell."}],"container-title":["AI Communications"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIC-210009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:27:59Z","timestamp":1777400879000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/AIC-210009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":23,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/aic-210009","relation":{},"ISSN":["1875-8452","0921-7126"],"issn-type":[{"value":"1875-8452","type":"electronic"},{"value":"0921-7126","type":"print"}],"subject":[],"published":{"date-parts":[[2021,9,10]]}}}