Comprehensive Analysis on Tomato: Disease Detection, Recommendations, and Advanced Technological Approaches in AI
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Abstract
Tomato plants are vulnerable to a wide range of diseases that affect their growth and crop yield, resulting in significant economic losses for farmers. This paper provides a comprehensive analysis of tomato plant life cycle and environmental conditions required for optimum crop growth. It explores tomato varieties along with their properties, detailed information on diseases across all the growth stages, including causes, symptoms, and control measures. The paper also provides an extensive evaluation of different artificial intelligence methods for tomato plant disease detection and prediction. It is observed that the detection accuracy of AI models ranges from 92% to 99%. Additionally, the paper explores advanced technologies like drones/UAV, IoT, cloud and edge computing, and blockchain for tomato cultivation management. The advantages and challenges w.r.t each technology are analyzed. The insights and solutions discussed in this paper can help farmers and researchers effectively manage tomato plant diseases and better understand supporting technologies that can enhance productivity and enable disease prediction for sustainable tomato cultivation.
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References
Abdulridha, J., Ampatzidis, Y., Qureshi, J., & 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 Sensing, 12(17), Article 2732. https://doi.org/10.3390/rs12172732
Al-Mashhadani, Z., & Chandrasekaran, B. (2021). ROS-based robotic system for tomato disease and ripeness classification using convolutional neural networks. In Proceedings of the IEEE 12th annual information technology, electronics and mobile communication conference (IEMCON) (pp. 420-427). IEEE. https://doi.org/10.1109/IEMCON53756.2021.9623183
Alizadeh-Moghaddam, G., Nasr-Esfahani, M., Nasr-Esfahani, A., & Mohammadbagheri, L. (2024). A suitable alternative to antifungal agents for the control of early blight disease - Alternaria alternata of tomato. Australasian Plant Pathology, 53(2), 129-140. https://doi.org/10.1007/s13313-024-00968-6
Alzahrani, M. (2025). Automated tomato defect detection using CNN feature fusion for enhanced classification. Processes, 13(1), Article 115. https://doi.org/10.3390/pr13010115
Ampatzidis, Y., De Bellis, L., & Luvisi, A. (2017). iPathology: Robotic applications and management of plants and plant diseases. Sustainability, 9(6), Article 1010. https://doi.org/10.3390/su9061010
Asia Farming. (2025). Different types of agriculture subsidy in India: List of farming subsidy schemes by Government of India. https://www.asiafarming.com/different-types-of-agriculture-subsidy-in-india-list-of-farming-subsidy-schemes-by-government-of-india
Aversano, L., Bernardi, M. L., Cimitile, M., Iammarino, M., & Rondinella, S. (2020). Tomato diseases classification based on VGG and transfer learning. In Proceedings of the IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) (pp. 129-133). IEEE. https://doi.org/10.1109/MetroAgriFor50201.2020.9277626
Bala, M. N., Nayak, T. K., Palla, J. S. S. P., Nath, S. G., & Jeswanth, S. (2025). Plant leaf disease detection using MobileNetV2. ITM Web of Conferences, 79, Article 01021. https://doi.org/10.1051/itmconf/20257901021
Bhatia, A., Chug, A., Singh, A. P., Singh, R. P., & Singh, D. (2022). A machine learning-based spray prediction model for tomato powdery mildew disease. Indian Phytopathology, 75, 225-230. https://doi.org/10.1007/s42360-021-00430-3
Blancard, D. (2012). Tomato diseases. Academic Press.
Chakravarthy, A. S., & Raman, S. (2020). Early blight identification in tomato leaves using deep learning. In Proceedings of the international conference on contemporary computing and applications (IC3A) (pp. 154-158). IEEE. https://doi.org/10.1109/IC3A48958.2020.233288
Chavez-Martinez, O., Monjardin-Armenta, S. A., Rangel-Peraza, J. G., Mora-Felix, Z. D., & Sanhouse-García, A. J. (2025). Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoring. Data in Brief, 58, Article 111256. https://doi.org/10.1016/j.dib.2024.111256
Chin, R., Catal, C., & Kassahun, A. (2023). Plant disease detection using drones in precision agriculture. Precision Agriculture, 24(5), 1663-1682.
Choi, E. Y., Choi, K.-Y., & Lee, Y.-B. (2013). Non-drainage irrigation scheduling in coir substrate hydroponic system for tomato cultivation by a frequency domain reflectometry sensor. European Journal of Horticultural Science, 78(3), 132-143.
Chou, H.-P., Huang, Y.-C., Lin, Y.-H., & Deng, W.-L. (2022). Selection, formulation, and field evaluation of Bacillus amyloliquefaciens PMB01 for its application to manage tomato bacterial wilt disease. Agriculture, 12(10), Article 1714. https://doi.org/10.3390/agriculture12101714
Chung, I., Gupta, A., & Ogunfunmi, T. (2022). Remote crop disease detection using deep learning with IoT. In Proceedings of the IEEE global humanitarian technology conference (GHTC) (pp. 353-360). IEEE. https://doi.org/10.1109/GHTC55712.2022.9910991
Cordon, G., Andrade, C., Barbara, L., & Romero, A.M. (2022). Early detection of tomato bacterial canker by reflectance indices. Information Processing in Agriculture, 9(2), 184-194. https://doi.org/10.1016/j.inpa.2021.06.004
Darmawan, R. R., Rozin, F., Evani, C., Idris, I., & Sumardi, D. (2021). IoT and machine learning system for early/late blight disease severity level identification on tomato plants. In Proceedings of the 13th International conference on information and communication technology and system (ICTS) (pp. 288-293). IEEE. https://doi.org/10.1109/ICTS52701.2021.9608788
Devaraj, H., Sohail, S., Ooi, M., Li, B., Hudson, N., Baughman, M., Chard, K., Chard, R., Casella, E., Foster, I., & Rana, O. (2024). RuralAI in tomato farming: Integrated sensor system, distributed computing and hierarchical federated learning for crop health monitoring. IEEE Sensors Letters, 8(5), Article 5501604. https://doi.org/ 10.1109/LSENS.2024.3384935
Devi, N. O., Devi, R. K. T., Debbarma, M., Hajong, M., & Thokchom, S. (2022). Effect of endophytic Bacillus and arbuscular mycorrhiza fungi (AMF) against Fusarium wilt of tomato caused by Fusarium oxysporum f. sp. lycopersici. Egyptian Journal of Biological Pest Control, 32, Article 1. https://doi.org/10.1186/s41938-021-00499-y
Dhaliwal, M. S., Jindal, S. K., Sharma, A., & Prasanna, H. C. (2020). Tomato yellow leaf curl virus disease of tomato and its management through resistance breeding: A review. Journal of Horticultural Science and Biotechnology, 95(4), 425-444. https://doi.org/10.1080/14620316.2019.1691060
Drishti IAS. (2023, July 1). Tomatoes price volatility. https://www.drishtiias.com/daily-updates/daily-news-analysis/tomatoes-price-volatility
Egi, Y., Hajyzadeh, M., & Eyceyurt, E. (2022). Drone-computer communication based tomato generative organ counting model using YOLOv5 and DeepSORT. Agriculture, 12(9), Article 1290. https://doi.org/10.3390/agriculture12091290
Elsharkawy, M. M., El-Okkiah, S., Elsadany, A. Y., Bedier, M. Y., Omara, R. I., Behiry, S. I., Hassan, S., & Abdelkhalek, A. (2022). Systemic resistance induction of tomato plants against tomato mosaic virus by microalgae. Egyptian Journal of Biological Pest Control, 32(1), Article 37. https://doi.org/10.1186/s41938-022-00538-2
Gangadhar, S., & Sowmya. (2020). Real time environmental time series data analysis using Influx DB. International Journal of Advanced Scientific Innovation, 1(1), 1-5. https://doi.org/10.5281/zenodo.4641703
Giri, V. P., Pandey, S., Srivastava, S., Shukla, P., Kumar, N., Kumari, M., Katiyar, R., Singh, S., & Mishra, A. (2023). Chitosan fabricated biogenic silver nanoparticles (Ch@BSNP) protectively modulate the defense mechanism of tomato during bacterial leaf spot (BLS) disease. Plant Physiology and Biochemistry, 197, Article 107637. https://doi.org/10.1016/j.plaphy.2023.03.014
Girish, L., Gangadhar, S., Bharath, T. R., Balaji, K. S., & Abhishek, K. T. (2018). Crop yield and rainfall prediction in Tumakuru district using machine learning. International Journal for Research in Engineering Application and Management, 4(Special issue NCTFRD-2018), 61-65.
Gleason, M. L., & Edmunds, B. A. (2005). Tomato diseases and disorders. Iowa State University Extension.
Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation management system using machine learning and open source technologies. Computers and Electronics in Agriculture, 155, 41-49. https://doi.org/10.1016/j.compag.2018.09.040
Grecuccio, J., Giusto, E., Fiori, F., & Rebaudengo, M. (2020). Combining blockchain and IoT: Food-chain traceability and beyond. Energies, 13(15), Article 3820. https://doi.org/10.3390/en13153820
Gu, M., Li, K.-C., Li, Z., Han, Q., & Fan, W. (2020). Recognition of crop diseases based on depthwise separable convolution in edge computing. Sensors, 20(15), Article 4091. https://doi.org/10.3390/s20154091
Gupta, S. K., Sharma, M., & Mukherjee, S. (2022). Buckeye rot of tomato in India: Present status, challenges, and future research perspectives. Plant Disease, 106(4), 1085-1095. https://doi.org/10.1094/PDIS-04-21-0861-FE
Hasan, M., Tanawala, B., & Patel, K. J. (2019). Deep learning precision farming: Tomato leaf disease detection by transfer learning. In Proceedings of the 2nd international conference on advanced computing and software engineering (ICACSE 2019) (pp. 171-175). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3349597
Hassan, S. M., & Maji, A. K. (2022). Plant disease identification using a novel convolutional neural network. IEEE Access, 10, 5390-5401. https://doi.org/10.1109/ACCESS.2022.3141371
Hayajneh, A. M., Aldalahmeh, S. A., Alasali, F., Al-Obeidollah, H., Zaidi, S. A., & McLernon, D. (2024). Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming. IET Smart Cities, 6(1), 10-26. https://doi.org/10.1049/smc2.12072
Hemming, S., de Zwart, F., Elings, A., Petropoulou, A., & Righini, I. (2020). Cherry tomato production in intelligent greenhouses—sensors and AI for control of climate, irrigation, crop yield, and quality. Sensors, 20(22), Article 6430. https://doi.org/10.3390/s20226430
Islam, M. T. (2020). Plant disease detection using CNN model image processing. International Journal of Engineering Research and Technology, 9(10), 291-297.
Islam, M. S., Sultana, S., Farid, F. A., Islam, M. N., Rashid, M., Bari, B. S., Hashim, N., & Husen, M. N. (2022). Multimodal hybrid deep learning approach to detect tomato leaf disease using attention-based dilated convolution feature extractor with logistic regression classification. Sensors, 22, Article 6079. https://doi.org/10.3390/s22166079
Kibriya, H., Rafique, R., Ahmad, W., & Adnan, S. M. (2021). Tomato leaf disease detection using convolution neural network. In Proceedings of the 18th international Bhurban conference on applied sciences and technology (IBCAST) (pp. 346-351). IEEE. https://doi.org/10.1109/IBCAST51254.2021.9393311
Kouadio, L., El Jarroudi, M., Belabess, Z., Laasli, S.-E., Roni, M. Z. K., Amine, I. D. I., Mokhtari, N., Mokrini, F., Junk, J., & Lahlali, R. (2023). A review on UAV-based applications for plant disease detection and monitoring. Remote Sensing, 15, Article 4273. https://doi.org/10.3390/rs15174273
Luna-Benoso, B., Martínez-Perales, J. C., Cortés-Galicia, J., Flores-Carapia, R., & Silva-García, V. M. (2021). Detection of diseases in tomato leaves by color analysis. Electronics, 10(9), Article 1055. https://doi.org/10.3390/electronics10091055
Mani, S. D., Pandey, S., Govindan, M., Muthamilarasan, M., & Nagarathnam, R. (2021). Transcriptome dynamics underlying elicitor-induced defense responses against Septoria leaf spot disease of tomato (Solanum lycopersicum L.). Physiology and Molecular Biology of Plants, 27, 873-888. https://doi.org/10.1007/s12298-021-00970-y
MarketResearch.biz. (2025, Jan 5). Tomatoes market. https://marketresearch.biz/report/ tomatoes-market/
Marzougui, F., Elleuch, M., & Kherallah, M. (2025). Blockchain and IoT in smart agriculture: Analysis, opportunities, challenges, and future research directions. Journal of Information Assurance and Security, 19(3), 104-119. https://doi.org/10.2478/ias-2024-0008
Mazumdar, P., Singh, P., Kethiravan, D., Ramathani, I., & Ramakrishnan, N. (2021). Late blight in tomato: Insights into the pathogenesis of the aggressive pathogen Phytophthora infestans and future research priorities. Planta, 253, Article 119. https://doi.org/10.1007/s00425-021-03636-x
Medici, M., Pedersen, S. M., Carli, G., & Tagliaventi, M. R. (2020). Environmental benefits of precision agriculture adoption. Economia Agro-Alimentare, 21(3), 637-656. https://doi.org/10.3280/ECAG2019-003004
Mezenner, A., Lakehal, M. R., Arab, N., Nemmour, H., & Chibani, Y. (2025). Precision agriculture: Tomato disease classification via compact convolutional vision transformer. Algerian Journal of Signals and Systems, 10(2), 87-90. https://doi.org/10.51485/ajss.v10i2.266
Ministry of Agriculture and Farmers Welfare, Government of India. (2024). Second advance estimates of 2023-24 of area and production of horticultural crops. https://pib.gov.in/Pressreleaseshare.aspx?PRID=2022761
Narimani, M., Pourreza, A., Moghimi, A., Mesgaran, M., Farajpoor, P., & Jafarbiglu, H. (2025). Drone-based multispectral imaging and deep learning for timely detection of branched broomrape in tomato farms. https://doi.org/10.48550/arXiv.2509.09972
National Horticulture Board. (2025a, Jan 20). Tomato diseases. https://www.nhb.gov.in/ pdf/vegetable/tomato/tom002.pdf
National Horticulture Board. (2025b, Feb 1). Tomato: Post harvest management. National Horticulture Board. https://www.nhb.gov.in/pdf/vegetable/tomato/tom013.pdf
Nyakuri, J. P., Nkundineza, C., Gatera, O., Nkurikiyeyezu, K. & Mwitende, G. (2025). AI and IoT-powered edge device optimized for crop pest and disease detection. Scientific Reports, 15, Article 22905. https://doi.org/10.1038/s41598-025-06452-5
Nyasulu, C., Diattara, A., Traore, A., Ba, C., Diedhiou, P. M., Sy, Y., Raki, H., & Peluffo-Ordóñez, D. H. (2023). A comparative study of machine learning-based classification of tomato fungal diseases: Application of GLCM texture features. Heliyon, 9(11), Article e21697. https://doi.org/10.1016/j.heliyon.2023.e21697
Ouhami, M., Es-Saady, Y., Hajji, M. E., Hafiane, A., Canals, R., & Yassa, M. E. (2020). Deep transfer learning models for tomato disease detection. In A. El Moataz, D. Mammass, A. Mansouri, & F. Nouboud (Eds.). Image and signal processing (pp. 65-67). Springer. https://doi.org/10.1007/978-3-030-51935-3_7
Pramanik, D., Shelake, R. M., Park, J., Kim, M. J., Hwang, I., Park, Y., & Kim, J.-Y. (2021). CRISPR/Cas9-mediated generation of pathogen-resistant tomato against Tomato Yellow Leaf Curl Virus and powdery mildew. International Journal of Molecular Sciences, 22(4), Article 1878. https://doi.org/10.3390/ijms22041878
Press Information Bureau. (2024). Final estimates of 2022-23 and first advance estimates of 2023-24 of area and production of horticultural crops. https://www.pib.gov.in/Press ReleaseIframePage.aspx?PRID=2012191
Qiao, K., Liu, Q., Huang, Y., Xia, Y., & Zhang, S. (2020). Management of bacterial spot of tomato caused by copper-resistant Xanthomonas perforans using a small molecule compound carvacrol. Crop Protection, 132, Article 105114. https://doi.org/10.1016/j.cropro.2020.105114
Qi, S., Shen, Y., Wang, X., Zhang, S., Li, Y., Islam, M. M., Wang, J., Zhao, P., Zhan, X., Zhang, F., & Liang, Y. (2022). A new NLR gene for resistance to tomato spotted wilt virus in tomato (Solanum lycopersicum). Theoretical and Applied Genetics, 135(5), 1493-1509. https://doi.org/10.1007/s00122-022-04049-4
Rahman, S. U., Alam, F., Ahmad, N., & Arshad, S. (2023). Image processing based system for the detection, identification and treatment of tomato leaf diseases. Multimedia Tools and Applications, 82, 9431-9445. https://doi.org/10.1007/s11042-022-13715-0
Ramírez, V., Martinez, J., Bustillos-Cristales, M. D. R., Cataneda-Antonio, D., Munive, J. A., & Baez, A. (2022). Bacillus cereus MH778713 elicits tomato plant protection against Fusarium oxysporum. Journal of Applied Microbiology, 132(1), 470-482. https://doi.org/10.1111/jam.15179
Rani, S. S., Kumar, C. H. M. S., Felicita, S. A., Ganesh, S. S., Choubey, A., & Anitha, R. (2024). Development and evaluation of a distinctive cloud-based artificial intelligence system using deep learning techniques (AISDLT) for accurate detection of tomato plant leaf diseases. International Journal of Intelligent Systems and Applications in Engineering, 12(12S), 538-552.
Rezvani, S. M., Abyaneh, H. Z., Shamshiri, R. R., Balasundram, S. K., Dworak, V., Goodarzi, M., Sultan, M., & Mahns, B. (2020). IoT-based sensor data fusion for determining optimality degrees of microclimate parameters in commercial greenhouse production of tomato. Sensors, 20(22), Article 6474. https://doi.org/10.3390/s20226474
Rudramurthy, V., Bharath, T. R., & Gangadhar, S. (2018). Applying cloud computing and wireless sensor technology for making smart agriculture. International Journal for Research in Engineering Application and Management, 4(special issue NCTFRD-2018), 38-42.
Sagheer, A., Mohammed, M., Riad, K., & Alhajhoj, M. (2020). A cloud-based IoT platform for precision control of soilless greenhouse cultivation. Sensors, 21(1), Article 223. https://doi.org/10.3390/s21010223
Sangeetha, M., Thejaswini, G., Shoba, A., Gaikwad, S. S., Amretasre, R. T., & Nivedita, S. (2021). Design and development of a crop quality monitoring and classification system using IoT and blockchain. Journal of Physics: Conference Series, 1964, Article 062011. https://doi.org/10.1088/1742-6596/1964/6/062011
Sarvaiya, S., Dhole, P., & Nandwanshi, I. (2025). Crop disease classification for edge devices: A quantized MobileNetV2 approach. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 13(10), 39-45. https://doi.org/10.17148/IJIREEICE.2025.131006
Saxena, R., Chauhan, S., Ghosh, D. K., Kumar, R., Kumar, D., & Das, A. (2023). Trends and trajectory of Indian agriculture. In P. K. Ghosh, A. Das, R. Saxena, K. Banerjee, G. Kar, & D. Vijay (Eds.). Trajectory of 75 years of Indian agriculture after independence (pp. 3-22). Springer. https://doi.org/10.1007/978-981-19-7997-2_1
Shahi, T. B., Xu, C.-Y., Neupane, A., & Guo, W. (2023). Recent advances in crop disease detection using UAV and deep learning techniques. Remote Sensing, 15(9), Article 2450. https://doi.org/10.3390/rs15092450
Sharma, J., Al-Huqail, A. A., Almogren, A., Doshi, H., Jayaprakash, B., Bharathi, B., Rehman, A. U., & Hussen, S. (2025). Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures. Scientific Reports, 15, Article 13904. https://doi.org/10.1038/s41598-025-98015-x
Sharma, K. K., Kumar, P., Kaushik, S., & Chaudhary, S. (2023). Enhancing soil health and productivity through conservation agriculture. Vigyan Varta, 4(11), 231-235.
Sharma, P., Thakur, S., & Negi, R. (2019). Recent advances in breeding of tomato- A review. International Journal of Current Microbiology and Applied Sciences, 8(3), 1275-1283.
Singh, D., Devappa, V., & Yadav, D. K. (2022a). Suppression of tomato bacterial wilt incited by Ralstonia pseudosolanacearum using polyketide antibiotic-producing Bacillus spp. isolated from rhizospheric soil. Agriculture, 12(12), Article 2009. https://doi.org/10.3390/agriculture12122009
Singh, R., Ao, N. T., Kangjam, V., Rajesha, G., & Banik, S. (2022b). Plant growth promoting microbial consortia against late blight disease of tomato under natural epiphytotic conditions. Indian Phytopathology, 75, 527-539. https://doi.org/10.1007/s42360-022-00464-1
Sotelo-Cardona, P., Lin, M.-Y., & Srinivasan, R. (2021). Growing tomato under protected cultivation conditions: Overall effects on productivity, nutritional yield, and pest incidences. Crops, 1(2), 97-110. https://doi.org/10.3390/crops1020010
Soy, A., & Balkrishna, S. M. (2025). Blockchain integration in agriculture for transparent farm-to-fork supply chains: Leveraging IoT and decentralized identity for enhanced traceability and security. SHS web of conferences, 216, Article 01073. https://doi.org/10.1051/shsconf/202521601073
Sudha, V., Aswini, D., & Akiladevi, R. (2023). Blockchain-based tomato supply chain management. In Proceedings of the 2nd international conference on advancements in electrical, electronics, communication, computing and automation (ICAECA) (pp. 1-5). IEEE. https://doi.org/10.1109/ICAECA56562.2023.10201119
Sultana, F., Islam, M. R., Sabbir, M. A., & Hossain, M. M. (2020). Plant growth promotion and suppression of damping off in tomato by plant growth promoting Rhizobacterium Bacillus amyloliquifaciens. Crops, 5(1), 59-68. https://doi.org/10.20448/803.5.1.59.68
Sun, H., Fu, R., Wang, X., Wu, Y., Al-Absi, M. A., Chen, Z., Chen, Q., & Sun, Y. (2025). Efficient deep learning-based tomato leaf disease detection through global and local feature fusion. BMC Plant Biology, 25, Article 311. https://doi.org/10.1186/s12870-025-06247-w
Suneja, B., Negi, A., Kumar, N., & Bhardwaj, R. (2022). Cloud-based tomato plant growth and health monitoring system using IoT. In Proceedings of the 3rd international conference on intelligent engineering and management (ICIEM) (pp. 237-243). IEEE. https://doi.org/10.1109/ICIEM54221.2022.9853170
Tarek, H., Aly, H., Eisa, S., & Abul-Soud, M. (2022). Optimized deep learning algorithms for tomato leaf disease detection with hardware deployment. Electronics, 11(1), Article 140. https://doi.org/10.3390/electronics11010140
Thalluri, L. N., Adapa, S. D., Priyanka, D., N., Sri Nitaya, N., Yasmeen, Sarma, A. V. S. Y. N., & Venkat, S. N. (2021). Drone technology enabled leaf disease detection and analysis system for agriculture applications. In Proceedings of the Second International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1079-1085). IEEE. https://doi.org/10.1109/ICOSEC51865.2021.9591837
Tripathi, P., Kumar, N., Rai, M., & Khan, A. (2022a). Applications of deep learning in agriculture. In M. A. Khan, R. Khan, & P. Praveen (Eds.). Artificial intelligence applications in agriculture and food quality improvement (pp. 17-28). IGI Global. https://doi.org/10.4018/978-1-6684-5141-0.ch002
Tripathi, R., Vishunavat, K., & Tewari, R. (2022b). Morphological and molecular characterization of Clavibacter michiganensis subsp. michiganensis causing bacterial canker in tomatoes. Physiological and Molecular Plant Pathology, 119, Article 101833. https://doi.org/10.1016/j.pmpp.2022.101833
Vallejo-Pérez, M. R., Sosa-Herrera, J. A., Navarro-Contreras, H. R., Alvarez-Preciado, L. G., Rodriguez-Vazquez, A. G., & Lara-Avila, J. P. (2021). Raman spectroscopy and machine learning for early detection of bacterial canker of tomato: The asymptomatic disease condition. Plants, 10(8), Article 1542. https://doi.org/10.3390/plants10081542
Vandana, & Goel, A. K. (2021). Plant leaves diseases recognition using convolutional neural network and five other hybrid models. In Proceedings of the 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-9). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9580025
Vasireddy, S. S. D. M., Yalagala, S., Sikha, J., Vullaganti, R., Repudi, R., Chandra, G. R., & Anand, D. (2024). Irrigation in precision agriculture using blockchain Ethereum based on IoT. In Engineering Proceedings, 66(1), Article 29. https://doi.org/10.3390/engproc2024066029
Vasudeva, R. S., & Raj, J. S. (1948). A leaf-curl disease of tomato. Phytopathology, 38(5), 364-369.
Verma, S., Chug, A., & Singh, A. (2020). Application of convolutional neural networks for evaluation of disease severity in tomato plant. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 273-282. https://doi.org/10.1080/09720529.2020.1721890
Verma, S., Chug, A., & Singh, A. P. (2018). Prediction models for identification and diagnosis of tomato plant diseases. In Proceedings of the international conference on advances in computing, communications and informatics (ICACCI) (pp. 1557-1563). IEEE. https://doi.org/10.1109/ICACCI.2018.8554842
Xia, J., Zhang, W., Zhang, W., Yang, Y., Hu, G., Ge, D., Liu, H., & Cao, H. (2021). A cloud computing-based approach using the visible near-infrared spectrum to classify greenhouse tomato plants under water stress. Computers and Electronics in Agriculture, 181, Article 105966. https://doi.org/10.1016/j.compag.2020.105966
Yallappa, D., Ramasamy, K., Surendrakumar, A., Suthakar, B., Kumar, A. P. M., Kannan, B., & Muthusami, M. K. (2024). Improving agricultural spraying with multi-rotor drones: A technical study on operational parameter optimization. Frontiers in Nutrition, 11, Article 1487074. https://doi.org/10.3389/fnut.2024.1487074
Yoren, A. I., & Suyanto, S. (2021). Tomato plant disease identification through leaf image using convolutional neural network. In Proceedings of the 9th international conference on information and communication technology (ICoICT) (pp. 320-325). IEEE. https://doi.org/10.1109/ICoICT52021.2021.9527425
Zahid, A., Majeed, Y., & Ojo, M. O. (2024). Standalone edge AI-based solution for tomato diseases detection. https://ssrn.com/abstract=4824801
Zhang, H., Zhang, L., Wu, H., Wang, D., Ma, X., Shao, Y., Jiang, M., & Chen, X. (2025). Unmanned-aerial-vehicle-based multispectral monitoring of nitrogen content in canopy leaves of processed tomatoes. Agriculture, 15(3), Article 309. https://doi.org/10.3390/agriculture15030309
Zhang, N., Wu, H., Zhu, H., Deng, Y., & Han, X. (2022). Tomato disease classification and identification method based on multimodal fusion deep learning. Agriculture, 12(12), Article 2014. https://doi.org/10.3390/agriculture12122014
Zhang, Y., & Chen, M. (2023). An IoT-enabled energy-efficient approach for the detection of leaf curl disease in tomato crops. Wireless Networks, 29, 321-329. https://doi.org/10.1007/s11276-022-03071-0
Zhang, Z., Li, J., Zhang, Z., Liu, Y., & Wei, Y. (2021). Tomato endophytic bacteria composition and mechanism of suppressiveness of wilt disease (Fusarium oxysporum). Frontiers in Microbiology, 12, Article 731764. https://doi.org/10.3389/fmicb.2021.731764
Zhong, X., Li, H., Cai, Y., Deng, Y., Xu, H., Tian, J., Liu, S., Hou, M., Weng, H., Wang, L., Ruan, M., Zhong, F., Zhu, C., & Xu, L. (2025). Early detection of tomato gray mold based on multispectral imaging and machine learning. Horticulturae, 11(9), Article 1073. https://doi.org/10.3390/horticulturae11091073
Zhou, L., Song, C., Li, Z., & Kuipers, O. P. (2021). Antimicrobial activity screening of rhizosphere soil bacteria from tomato and genome-based analysis of their antimicrobial biosynthetic potential. BMC Genomics, 22(1), Article 29. https://doi.org/10.1186/s12864-020-07346-8
Zhou, Q., Ma, L. Cao, L., & Yu, H. (2022). Identification of tomato leaf diseases based on improved lightweight convolutional neural networks MobileNetV3. Smart Agriculture, 4(1), 47-56. https://doi.org/10.12133/j.smartag.SA202202003
