Abdullah, N. E., Rahim, A. A., Hashim, H., & Kamal, M. M. (2007). Classification of rubber tree leaf diseases using multilayer perceptron neural network. 2007 5th Student Conference on Research and Development, SCORED, (December), 11–16. https://doi.org/10.1109/SCORED.2007.4451369 

Afifi, A., Alhumam, A., & Abdelwahab, A. (2021). Convolutional neural network for automatic identification of plant diseases with limited data. Plants, 10(28), 1–15. 

Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta, S. (2020). ToLed:DTomato leaf disease disease detection detection using convolution neural network. Procedia Computer Science, 167(2019), 293–301. https://doi.org/10.1016/j.procs.2020.03.225 

Agrawal, N., Urma, S., Padhan, S., & Barik, R. C. (2018). Indian agro based pest region detection by clustering and pseudo- color image processing. International Journal of Engineering Research & Technology, 7(03), 380–386. 

Ahmad, I., Hamid, M., Yousaf, S., Shah, S. T., & Ahmad, M. O. (2020). Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity, 1–6. 

Al-bayati, J. S. H., & Üstündag, B. B. (2020). Evolutionary feature optimization for plant leaf disease detection by deep neural networks. International Journal of Computational Intelligence Systems, 13(1), 12–23. 

Al-windi, B. K. M. A., Abbas, A. H., & Mahmood, M. S. (2023). Wheat stem rust leaf disease detection using image processing. Materials Today: Proceedings, 80(3). https://doi.org/10.1016/j.matpr.2021.07.251 

Alagarsamy, S. B., & Kondappan, S. (2020). Ear recognition system using adaptive approach Runge–Kutta (AARK) threshold segmentation with ANFIS classification. Neural Computing and Applications, 32(15), 10995–11006. https://doi.org/10.1007/s00521-018-3805-6 

Alessandrini, M., Fuentes, R. C., & Falaschetti, L. (2021). A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning. Data in Brief, 35, 106809. https://doi.org/10.1016/j.dib.2021.106809 

Ali, H., Lali, M. I., Nawaz, M. Z., Sharif, M., & Saleem, B. A. (2017). Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104. https://doi.org/10.1016/j.compag.2017.04.008 

Alshammari, H. H., & Alkhiri, H. (2023). Optimized recurrent neural network mechanism for olive leaf disease diagnosis based on wavelet transform. Alexandria Engineering Journal, 78(June), 149–161. https://doi.org/10.1016/j.aej.2023.07.037 

Anantha Padmanabhan, S., & Krishna Kumar, N. J. (2019). An effective pose invariant face recognition system with the aid of ABC optimized ANFIS. Cluster Computing, 22, 177–187. https://doi.org/10.1007/s10586-018-2032-3 

Ansong, M., Acheampong, E., Echeruo, J. B., Afful, S. N., & Ahimah, M. (2021). Direct financial cost of weed control in smallholder rubber plantations. Open Agriculture, 6, 346–355. 

Argenti, F., Alparone, L., & Benelli, G. (1990). Fast algorithms for texture analysis using co-occurrence matrices. IEE Proceedings F - Radar and Signal Processing, 137(6), 443–448. https://doi.org/10.1049/ip-f-2.1990.0064 

Arivazhagan, S., Shebiah, R. N., Ananthi, S., & Vishnu Varthini, S. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211–217. 

Arshad, F., Mateen, M., Hayat, S., Wardah, M., Al-huda, Z., Hyeon, Y., & Al-antari, M. A. (2023). PLDPNet : End-to-end hybrid deep learning framework for potato leaf disease prediction. Alexandria Engineering Journal, 78(February), 406–418. https://doi.org/10.1016/j.aej.2023.07.076 

Arsi, A., SHK, S., Hamidson, H., Gunawan, B., Pujiastuti, Y., Pratama, R., & Maulidin, M. (2022). Teknik Budidaya Petani Tanaman Karet (Hevea brasiliensis) terhadap Hama dan Penyakit di Kecamatan Tanjung Batu, Kabupaten Ogan Ilir. In S. H. Et.al. (Ed.), Seminar Nasional Lahan Suboptimal ke-10 “Ravitalisasi SUmber Pangan Nabati dan Hewani Pascapandemi dalam Mendukung Pertanian Lahan Suboptimal secara Berkelanjutan” (pp. 898–909). Palembang. 

Asfarian, A., Herdiyeni, Y., Rauf, A., & Mutaqin, K. H. (2013). Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: “Recent Challenges in Computer, Control and Informatics”, IC3INA 2013, 77–81. https://doi.org/10.1109/IC3INA.2013.6819152 

Ashraf, M., Abrar, M., Qadeer, N., Alshdadi, A. A., Sabbah, T., & Khan, M. A. (2023). A convolutional neural network model for wheat crop disease prediction. Computers, Materials & Continua, 75(2), 3867–3882. https://doi.org/10.32604/cmc.2023.035498 

Ashwinkumar, S., Rajagopal, S., Manimaran, V., & Jegajothi, B. (2022). Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Materials Today: Proceedings, 51(1), 480–487. https://doi.org/10.1016/j.matpr.2021.05.584 

Asraf, H. M., Nooritawati, M. T., & Rizam, M. S. B. S. (2012). A comparative study in kernel-based support vector machine of oil palm leaves nutrient disease. Procedia Engineering, 41(Iris), 1353–1359. https://doi.org/10.1016/j.proeng.2012.07.321 

Awate, A., Deshmankar, D., Amrutkar, G., Bagul, U., & Sonavane, S. (2015). Fruit disease detection using color, texture analysis and ANN. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 970–975. https://doi.org/10.1109/ICGCIoT.2015.7380603 

Badan Pusat Statistik. (2023). Hasil Pencacahan Lengkap Sensus Pertanian 2023 - Tahap I Provinsi Kalimantan Selatan. Provinsi Kalimantan Selatan. 

Badan Pusat Statistik Kabupaten Tabalong. (2016a). Monografi Kabupaten Tabalong 2016. Tanjung: BPS Kabupaten Tabalong. 

Badan Pusat Statistik Kabupaten Tabalong. (2016b). Produk domestik regional bruto menurut lapangan usaha. Tanjung: BPS Kabupaten Tabalong. 

Badan Pusat Statistik Kabupaten Tabalong. (2017a). Kabupaten Tabalong Dalam Angka. Tanjung: BPS Kabupaten Tabalong. 

Badan Pusat Statistik Kabupaten Tabalong. (2017b). Kecamatan Muara Uya dalam Angka 2017. Tanjung: BPS Kabupaten Tabalong. 

Badan Pusat Statistik Kabupaten Tabalong. (2017c). Statistik Daerah Kabupaten Tabalong. Tanjung: BPS Kabupaten Tabalong. 

Badiger, M., Kumara, V., Shetty, S. C. N., & Poojary, S. (2022). Leaf and skin disease detection using image processing. Global Transitions Proceedings, 3(April), 272–278. https://doi.org/10.1016/j.gltp.2022.03.010 

Bai, X., Li, X., Fu, Z., Lv, X., & Zhang, L. (2017). A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Computers and Electronics in Agriculture, 136, 157–165. https://doi.org/10.1016/j.compag.2017.03.004 

Baquero, D., Molina, J., Gil, R., Bojacá, C., Franco, H., & Gómez, F. (2014). An image retrieval system for tomato disease assessment. 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA, 1–5. https://doi.org/10.1109/STSIVA.2014.7010156 

Bhange, M., & Hingoliwala, H. A. (2015). Smart farming: pomegranate disease detection using image processing. Procedia Computer Science, 58, 280–288. https://doi.org/10.1016/j.procs.2015.08.022 

Bhimte, N. R. (2018). Diseases detection of cotton leaf spot using image processing and SVM classifier. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 340–344. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS): IEEE. 

Borges, D. L., Guedes, S. T. C. d. M., Nascimento, A. R., & Melo-Pinto, P. (2016). Detecting and grading severity of bacterial spot caused by Xanthomonas spp. in tomato (Solanum lycopersicon) fields using visible spectrum images. Computers and Electronics in Agriculture, 125, 149–159. https://doi.org/10.1016/j.compag.2016.05.003 

Br.Kaban, F. A., Allwine, Alamsyah, R., Sianturi, R., & Tambunan, K. (2022). Pemanfaatan metode fuzzy mamdani dalam deteksi penyakit manusia melalui gejala dan pola hidup. Jurnal Sains Dan Teknologi Widyaloka, 1(1), 102–117. 

Camargo, A., & Smith, J. S. (2009). Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture, 66(2), 121–125. https://doi.org/10.1016/j.compag.2009.01.003 

Chouhan, S. S., Singh, U. P., & Jain, S. (2020). Web facilitated anthracnose disease segmentation from the leaf of mango tree using radial basis function (rbf) neural network. Wireless Personal Communications, 113(2), 1279–1296. https://doi.org/10.1007/s11277-020-07279-1 

Dalimunthe, C. I., Tistama, R., & Wahyuni, S. (2017). Detection of white root disease (rigidoporus microporus) in various soil types in the rubber plantations based on the serological reaction. International Conference on Environment and Technology (IC-Tech). https://doi.org/10.1088/1755-1315/97/1/012043 

Damiri, N., Pratama, Y., Febbiyanti, T. R. I. R., & Rahim, S. E. (2022). Pestalotiopsis sp . infection causes leaf fall disease of new arrivals in several clones of rubber plants. Biodiversitas, 23(8), 3943–3949. https://doi.org/10.13057/biodiv/d230811 

De-la-torre, M., Zatarain, O., Avila-george, H., Muñoz, M., Oblitas, J., Lozada, R., … Castro, W. (2019). Multivariate analysis and machine learning for ripeness classification of cape gooseberry fruits. Processes, 7(12), 1–15. https://doi.org/10.3390/pr7120928 

Devi, D. A., & Muthukannan, K. (2015). Analysis of segmentation scheme for diseased rice leaves. IEEE International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT ), 1374–1378. https://doi.org/10.1109/ICACCCT.2014.7019325 

Dewi, R. K., & Ginardi, R. V. H. (2014). Feature extraction for identification of sugarcane rust disease. International Conference on Information, Communication Technology and System (ICTS), 99–103. https://doi.org/10.1109/ICTS.2014.7010565 

Dey, A. K., Sharma, M., & Meshram, M. R. (2016). Image processing based leaf rot disease, detection of betel vine (Piper BetleL.). Procedia Computer Science, 85(Cms), 748–754. https://doi.org/10.1016/j.procs.2016.05.262 

Dhakate, M., & Ingole A.B. (2015). Diagnosis of pomegranate plant diseases using neural network. Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). https://doi.org/10.1109/NCVPRIPG.2015.7490056 

Dhaygude, S. B., & Kumbhar, N. P. (2013). Agricultural plant leaf disease detection using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(1), 599–602. https://doi.org/ISSN: 2278 – 8875 

Direktorat Jenderal Perkebunan. (2016). Pedoman Teknis Kegiatan Penanganan Organisme Penganggu Tumbuhan (OPT) Tanaman Perkebunan tahun 2016. Jakarta: Kementrian Pertanian. 

Direktorat Jenderal Perkebunan. (2019). Statistik Perkebunan Indonesia 2018 - 2020. Jakarta: Sekretariat Direktorat Jenderal Perkebunan. 

Ebrahimi, M. A., Khoshtaghaza, M. H., Minaei, S., & Jamshidi, B. (2017). Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 137, 52–58. https://doi.org/10.1016/j.compag.2017.03.016 

Fadzil, W. M. N. W. M., Rizam, M. S. B. S., Jailani, R., & Nooritawati, M. T. (2014). Orchid leaf disease detection using border segmentation techniques. Proceedings - 2014 IEEE Conference on System, Process and Control, ICSPC 2014, (December), 168–173. https://doi.org/10.1109/SPC.2014.7086251 

Faza, S., Husna, M., Panggabean, J., & Rahmat, R. F. (2021). Acute coronary syndrome disorders classification based on ecg images using adaptive neuro-fuzzy inference system (ANFIS). Indonesian Journal of Education, Social Sciences and Research (IJESSR), 2(1), 24–30. 

Febbiyanti, T. R., & Fairuza, Z. (2020). Identifikasi Penyebab Kejadian Luar Biasa Penyakit Gugur Daun Karet Di Indonesia. Jurnal Penelitian Karet, 37(2), 193–206. 

Francis, J., D., A. S. D., & Anoop B. K. (2016). Identification of leaf diseases in pepper plants using soft computing techniques. Conference on Emerging Devices and Smart Systems (ICEDSS), 168–173. https://doi.org/10.1109/ICEDSS.2016.7587787 

Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2022). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9). https://doi.org/10.3390/s17092022 

Gao, Z., Shao, Y., Xuan, G., Wang, Y., Liu, Y., & Han, X. (2020). Artificial intelligence in agriculture real-time hyperspectral imaging for the in- field estimation of strawberry ripeness with deep learning. Artificial Intelligence in Agriculture, 4, 31–38. https://doi.org/10.1016/j.aiia.2020.04.003 

Garcia-Lamont, F., Cervantes, J., López, A., & Rodriguez, L. (2018). Segmentation of images by color features: A survey. Neurocomputing, 292, 1–27. https://doi.org/10.1016/j.neucom.2018.01.091 

Gavhale, K. R., Gawande, U., & Hajari, K. O. (2014). Unhealthy region of citrus leaf detection using image processing techniques. International Conference for Convergence of Technology, I2CT 2014, 2–7. https://doi.org/10.1109/I2CT.2014.7092035 

Gayathri Devi, T., & Neelamegam, P. (2019). Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu. Cluster Computing, 22, 13415–13428. https://doi.org/10.1007/s10586-018-1949-x 

Go, W. Z., Chin, K. L., H’ng, P. S., Wong, M. Y., Luqman, C. A., Surendran, A., … KongGo, W. J. (2021). Virulence of Rigidoporus microporus Isolates Causing White Root Rot Disease on Rubber Trees (Hevea brasiliensis) in Malaysia. Plants, 10, 2123. https://doi.org/https://doi.org/10.3390/plants10102123 

Goodridge, W., Bernard, M., Jordan, R., & Rampersad, R. (2017). Intelligent diagnosis of diseases in plants using a hybrid multi-criteria decision making technique. Computers and Electronics in Agriculture, 133, 80–87. https://doi.org/10.1016/j.compag.2016.12.003 

Gulhane, V. A., & Kolekar, M. H. (2014). Diagnosis of diseases on cotton leaves using principal component analysis classifier. Annual IEEE India Conference (INDICON) Diagnosis. 

Gupta, V., Sengar, N., Dutta, M. K., Travieso, C. M., & Alonso, J. B. (2017). Automated Segmentation of Powdery Mildew disease from Cherry Leaves using Image Processing. International Conference and Workshop on Bioinspired Intelligence (IWOBI), 2–5. https://doi.org/10.1109/IWOBI.2017.8006454 

Han, L., Haleem, M. S., & Taylor, M. (2015). A novel computer vision-based approach to automatic detection and severity assessment of crop diseases. Science and Information Conference (SAI ), 638–644. https://doi.org/10.1109/SAI.2015.7237209 

Harakannanavar, S. S., Rudagi, J. M., Puranikmath, V. I., & Siddiqua, A. (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3(April), 305–310. https://doi.org/10.1016/j.gltp.2022.03.016 

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. Transactions on Systems, Man and Cybernetics, 3, 610–621. https://doi.org/10.1109/TSMC.1973.4309314 

Hartati, S., Harjoko, A., Rosnelly, R., Chandradewi, I., & Faizah. (2019). Performance of SVM and ANFIS for classification of malaria parasite and its life-cycle-stages in blood smear. In Communications in Computer and Information Science (Vol. 937). https://doi.org/10.1007/978-981-13-3441-2_9 

Hasan, S., Jahan, S., & Islam, I. (2022). Disease detection of apple leaf with combination of color segmentation and modified DWT. Journal of King Saud University - Computer and Information Sciences, 34(9), 7212–7224. https://doi.org/10.1016/j.jksuci.2022.07.004 

Hassanien, A. E., Gaber, T., Mokhtar, U., & Hefny, H. (2017). An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Computers and Electronics in Agriculture, 136, 86–96. https://doi.org/10.1016/j.compag.2017.02.026 

Hendrawan, Haris, A., Rasywir, E., & Pratama, Y. (2020). Diagnosis penyakit tanaman karet dengan metode fuzzy mamdani. Paradigma - Jurnal Informatika Dan Komputer, 22(2), 132–138. 

Hossain, I., Jahan, S., Al, R., & Ahmed, K. (2023). Smart agricultural technology detecting tomato leaf diseases by image processing through deep convolutional neural networks. Smart Agricultural Technology, 5(June), 100301. https://doi.org/10.1016/j.atech.2023.100301 

Hosseini, S. A., Shiri, I., Hajianfar, G., & Ghafarian, P. (2021). The impact of preprocessing on the pet-ct radiomics features in non-small cell lung cancer. Frontiers in BiomedicalTechnologies, 8(4), 261–272. 

Hu, Q. xia, Tian, J., & He, D. jian. (2017). Wheat leaf lesion color image segmentation with improved multichannel selection based on the Chan–Vese model. Computers and Electronics in Agriculture, 135, 260–268. https://doi.org/10.1016/j.compag.2017.01.016 

Imelda, I., Mulyo, J. H., Suryantini, A., & Masyhuri, M. (2023). Assessing the determinant factors of risk strategy adoption to mitigate various risks : An experience from smallholder rubber farmers in West Kalimantan Province , Indonesia. Open Agriculture, 8(1). https://doi.org/https://doi.org/10.1515/opag-2022-0196 

Iqbal, A., & Talukder, K. H. (2020). Detection of potato disease using image segmentation and machine learning. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 43–47. India. 

IRRDB. (2006). Portrait of the Global Rubber Industry. Malaysia. 

Islam, M., Talukder, A., Amin, R., Uddin, A., Akhter, A., Sharmin, S., … Kumar, S. (2023). A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intelligent Systems with Applications, 20(August), 200278. https://doi.org/10.1016/j.iswa.2023.200278 

Jesie, R. S., Premi, M. S. G., & Jarin, T. (2024). Comparative analysis of paddy leaf diseases sensing with a hybrid convolutional neural network model. Measurement: Sensors, 31(November 2023), 100966. https://doi.org/10.1016/j.measen.2023.100966 

Jhuria, M., Kumar, A., & Borse, R. (2013). Image processing for smart farming: Detection of disease and fruit grading. IEEE Second International Conference on Image Information Processing (ICIIP-2013), 521–526. https://doi.org/10.1109/ICIIP.2013.6707647 

Jiang, P., Chen, Y., & Liu, B. I. N. (2019). Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 7, 59069–59080. https://doi.org/10.1109/ACCESS.2019.2914929 

Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D., & Ortiz-Barredo, A. (2017). Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138, 200–209. https://doi.org/10.1016/j.compag.2017.04.013 

Joshi, A. a. (2016). Monitoring and controlling rice diseases using image processing techniques. International Conference on Computing, Analytics and Security Trends (CAST) College of Engineering Pune, India, 471–476. 

Junaidi, & Atminingsih. (2017). Perkembangan ontogenetik daun tanaman karet sebagai penanda awal adaptasi terhadap cekaman lingkungan dan patogen. Warta Perkaretan, 36(1), 29–38. 

Junita, R., Lubis, L., Pinem, M. I., & Dalimunthe, C. I. (2017). Hubungan antara anatomi daun dengan ketahanan penyakit gugur daun pada tanaman karet (hevea brassiliensis muell. Arg). Jurnal Agroekoteknologi FP USU, 5(1), 160–166. 

Kamerkar, U., & Othman, A. S. (2022). Transcriptome data for hevea brasiliensis associated with powdery mildew infection. Data in Brief, 42, 108254. https://doi.org/10.1016/j.dib.2022.108254 

Karthika, J., Kumar, K. M., Santhose, M., Sharan, T., & S, S. H. (2021). Disease detection in cotton leaf spot using image processing. Journal of Physics: Conferemce Series (ICCCEBS), 1916. https://doi.org/10.1088/1742-6596/1916/1/012224 

Kaur, S., Randhawa, S., & Malhi, A. (2021). An efficient ANFIS based pre-harvest ripeness estimation technique for fruits. Multimedia Tools and Applications, 80(13), 19459–19489. https://doi.org/10.1007/s11042-021-10741-2 

Kaushik, A. C., Pal, A., Kumar, A., Dwivedi, V. D., Bharadwaj, S., Pandey, A., … Sahi, S. (2017). Internal transcribed spacer sequence database of plant fungal pathogens: PFP-ITSS database. Informatics in Medicine Unlocked, 7(February), 34–38. https://doi.org/10.1016/j.imu.2017.02.006 

Khairuzzaman, N. A., Hashim, H., Kamal, M. M., Sampian, A. F. M., & Yussuf, U. R. (2017). White root disease infected rubber tree detection through statistical inference of Dry Rubber sheet Near Infrared (NIR) spectrum measurement method. Journal of Mechanical Engineering, SI 2(2), 167–177. 

Khan, S., & Narvekar, M. (2020). Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment. Journal of King Saud University - Computer and Information Sciences, 34(6), 3506–3516. https://doi.org/10.1016/j.jksuci.2020.09.006 

Khirade, S. D., & Patil, A. B. (2015). Plant Disease Detection Using Image Processing. International Conference on Computing Communication Control and Automation, 768–771. https://doi.org/10.1109/ICCUBEA.2015.153 

Kiran, S. M., & Chandrappa, D. N. (2023). Plant leaf disease detection using efficient image processing and machine learning algorithms. Journal of Robotics and Control (JRC), 4(6), 840–848. https://doi.org/10.18196/jrc.v4i6.20342 

Konakoglu, B. (2021). Prediction of geodetic point velocity using MLPNN , GRNN , and RBFNN models : a comparative study. Acta Geodaetica et Geophysica, (0123456789). https://doi.org/10.1007/s40328-021-00336-6 

Krishnan, M., & Sumithra, M. G. (2013). A novel algorithm for detecting bacterial leaf scorch (BLS) of shade trees using image processing. IEEE 11th Malaysia International Conference on Communications (MICC), (November), 474–478. https://doi.org/10.1109/MICC.2013.6805876 

Krishnan, V. G., Deepa, J., Rao, P. V., Divya, V., & Kaviarasan, S. (2022). An automated segmentation and classification model for banana leaf disease detection. Journal of Applied Biology & Biotechnology Vol., 10(01), 213–220. https://doi.org/10.7324/JABB.2021.100126 

Kruse, O. M. O., Prats-Montalbán, J. M., Indahl, U. G., Kvaal, K., Ferrer, A., & Futsaether, C. M. (2014). Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and Electronics in Agriculture, 108, 155–165. https://doi.org/10.1016/j.compag.2014.07.010 

Kusdiana, A. P. J., Munir, M., & Suryaningtyas, H. (2016). Studi pemanfaatan ekstrak kunyit (Curcuma Domestica Valeton) untuk pengendalian penyakit jamur akar putih pada tanaman karet. Warta Perkaretan, 35(1), 25–36. 

Kutty, S. B., Abdullah, N. E., Hashim, H., Afhzan, A., Rahim, A., & Sulinda, A. (2013). Classification of watermelon leaf diseases using neural network analysis. IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), 459–464. 

Levic, J., Stankovic, S., Krnjaja, V., & Bocarov-Stancic, A. (2012). Simple and efficient method for detection and identification of Pyrenochaeta terrestris on maize root. Crop Protection, 38, 66–71. https://doi.org/10.1016/j.cropro.2012.03.015 

Li, B., Yang, Y., Cai, J., Liu, X., Shi, T., Li, C., … Huang, G. (2021). Genomic Characteristics and Comparative Genomics Analysis of Two Chinese Corynespora cassiicola Strains Causing Corynespora Leaf Fall (CLF) Disease. Journal of Fungi, 7(6), 485. 

Liu, B., Ding, Z., Tian, L., He, D., & Li, S. (2020). Grape leaf disease identification using improved deep convolutional neural networks. Frontiers in Plant Science, 11(July), 1–14. https://doi.org/10.3389/fpls.2020.01082 

Liu, J., & Wang, X. (2020). Tomato diseases and pests detection based on improved yolo v3 convolutional neural network. Frontiers in Plant Science, 11(June), 1–12. https://doi.org/10.3389/fpls.2020.00898 

Liu, J., & Wang, X. (2021). Early recognition of tomato gray leaf spot disease based on MobileNetv2 - YOLOv3 model. Plant Methods, (2020), 1–16. https://doi.org/10.1186/s13007-020-00624-2 

Lu, J., Hu, J., Zhao, G., Mei, F., & Zhang, C. (2017). An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 142, 369–379. https://doi.org/10.1016/j.compag.2017.09.012 

Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017). Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378–384. https://doi.org/10.1016/j.neucom.2017.06.023 

Madiwalar, S. C., & Wyawahare, M. V. (2017). Plant disease identification : a comparative study. International Conference on Data Management, Analytics and Innovation (ICDMAI), 13–18. Zeal Education Society, Pune, India, Feb 24-26. 

Majid, K., Herdiyeni, Y., & Rauf, A. (2013). I-PEDIA : mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network. Advanced Computer Science and Information Systems (ICACSIS), 2013 International Conference On, (c), 978–979. 

Marjani, A., Babanezhad, M., & Shirazian, S. (2020). Application of adaptive network-based fuzzy inference system (ANFIS) in the numerical investigation of Cu/water nanofluid convective flow. Case Studies in Thermal Engineering, 22(September), 100793. https://doi.org/10.1016/j.csite.2020.100793 

Math, J. G. (2016). Plant disease analysis using histogram matching based on bhattacharya’s distance calculation. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 1546–1549. 

Mayerni, R., Rezki, D., & Heriza, S. (2017). Pemberdayaan masyarakat melalui optimalisasi pemanfaatan Trichoderma sp sebagai dekomposer limbah serasah karet dan peranannya dalam mengendalikan penyakit jamur akar putih. Jurnal Ilmiah Pengabdian Kepada Masyarakat, 1(2), 33–40. 

Mayr, A., Weigelt, M., Masuch, M., Meiners, M., Hüttel, F., & Franke, J. (2018). Application scenarios of artificial intelligence in electric drives production. Procedia Manufacturing, 24, 40–47. https://doi.org/10.1016/j.promfg.2018.06.006 

Mayr, Andreas, Kißkalt, D., Meiners, M., Lutz, B., Schäfer, F., Selmaier, A., … Franke, J. (2023). Machine learning in production – potentials, challenges and exemplary applications. Procedia CIRP, 86(March), 49–54. https://doi.org/10.1016/j.procir.2020.01.035 

Meena, S. D., Susank, M., Guttula, T., & Chandana, S. H. (2023). Crop yield improvement with weeds, pest and disease detection. Procedia Computer Science, 218(2022), 2369–2382. https://doi.org/10.1016/j.procs.2023.01.212 

Michel, L., Brun, F., & Makowski, D. (2017). A framework based on generalised linear mixed models for analysing pest and disease surveys. Crop Protection, 94, 1–12. https://doi.org/10.1016/j.cropro.2016.12.013 

Mohanaiah, P., Sathyanarayana, P., & Gurukumar, L. (2013). Image texture feature extraction using GLCM approach. International Journal of Scientific & Research Publication, 3(5), 1–5. https://doi.org/10.1.1.414.96981 

Mohd Azmi, A. F., Hashim, H., Sulaiman, M. S., Wahid, N. B., Khairuzzaman, N. A., & Mohd Sampian, A. F. (2015). Determination of white root infected rubber tree through dielectric characteristic in latex. Advanced Materials Research, 1134, 231–235. https://doi.org/10.4028/www.scientific.net/AMR.1134.231 

Mokhtar, U., Alit, M. A. S., Hassenian, A. E., & Hefny, H. (2015). Tomato leaves diseases detection approach based on support vector machines. 2015 11th International Computer Engineering Conference (ICENCO), 246–250. https://doi.org/10.1109/ICENCO.2015.7416356 

Molina, J. F., Gil, R., Bojacá, C., Gómez, F., & Franco, H. (2015). Automatic detection of early blight infection on tomato crops using a color based classification strategy. 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014, 1, 1–5. https://doi.org/10.1109/STSIVA.2014.7010166 

Mondal, D., Chakraborty, A., Kole, D. K., & Majumder, D. D. (2016). Detection and classification technique of yellow vein mosaic virus disease in okra leaf images using leaf vein extraction and naive bayesian classifier. International Conference on Soft Computing Techniques and Implementations, ICSCTI 2015, 166–171. https://doi.org/10.1109/ICSCTI.2015.7489626 

Morgan, M., Blank, C., & Seetan, R. (2021). Plant disease prediction using classification algorithms. International Journal of Artificial Intelligence (IJ-AI), 10(1), 257–264. https://doi.org/10.11591/ijai.v10.i1.pp257-264 

Mutalib, S., Abdullah, M. H., Abdul-Rahman, S., & Aziz, Z. A. (2017). A brief study on paddy applications with image processing and proposed architecture. Proceedings - 2016 IEEE Conference on Systems, Process and Control, ICSPC 2016, (December), 124–129. https://doi.org/10.1109/SPC.2016.7920716 

Narmadha, R. P. (2017). Detection and measurement of paddy leaf disease symptoms using image processing. International Conference on Computer Communication and Informatics (ICCCI -2017), 5–8. Coimbatore, INDIA Detection. 

Neumann, M., Hallau, L., Klatt, B., Kersting, K., & Bauckhage, C. (2014). Erosion band features for cell phone image based plant disease classification. 2014 22nd International Conference on Pattern Recognition, d, 3315–3320. https://doi.org/10.1109/ICPR.2014.571 

Ng, L. C., Anuar, S. N. A., Jong, J. W., & Elham, M. S. H. (2016). Phytobeneficial and plant growth-promotion properties of silicon-solubilising rhizobacteria on the growth and control of rice sheath blight disease. Asian Journal of Plant Sciences, 15, 92–100. https://doi.org/10.3923/ajps.2016.92.100 

Nguyen, D. V., Nguyen, Tr., Nguyen, T. V., Nguyen, T. N., Massacci, F., & H.Phung, P. (2019). HIT4Mal : Hybrid image transformation for malware classification. Transactions on Emerging Telecommunications Technologies, 31(11), e3789. https://doi.org/10.1002/ett.3789 

Nikith, B. V., Keerthan, N. K. ., M.S, P., & T, D. A. (2023). Leaf disease detection and classification. Procedia Computer Science, 218, 291–300. https://doi.org/10.1016/j.procs.2023.01.011 

Nwosu-obieogu, E. O. O. K., Ude, C. U., Okolo, B. I., Adeyi, O., & Omotoso, A. O. (2021). Hevea brasiliensis oil epoxidation : hybrid genetic algorithm – neural fuzzy – Box – Behnken ( GA – ANFIS – BB ) modelling with sensitivity and uncertainty analyses. Multiscale and Multidisciplinary Modeling, Experiments and Design, 4(2), 1–14. https://doi.org/10.1007/s41939-020-00086-y 

Okinda, C., Nyalala, I., Korohou, T., Okinda, C., Wang, J., Achieng, T., … Shen, M. (2020). A review on computer vision systems in monitoring of poultry : A welfare perspective. Artificial Intelligence in Agriculture, 4, 184–208. https://doi.org/10.1016/j.aiia.2020.09.002 

Oktavia, F., Sudarsono, Kuswanhadi, Dinarty, D., & Widodo. (2016). Identifikasi ketahanan plasma nutfah karet irrdb 1981 terpilih terhadap penyakit gugur daun corynespora berdasarkan aktivitas toksin cassiicolin. Jurnal Penelitian Karet, 34(1), 35–48. 

Omrani, E., Khoshnevisan, B., Shamshirband, S., Saboohi, H., Anuar, N. B., & Nasir, M. H. N. M. (2014). Potential of radial basis function-based support vector regression for apple disease detection. Measurement: Journal of the International Measurement Confederation, 55, 512–519. https://doi.org/10.1016/j.measurement.2014.05.033 

Orchi, H., Sadik, M., & Khaldoun, M. (2022). On using artificial intelligence and the internet of things for crop disease detection: a contemporary survey. Agriculture (Switzerland), 12(9), 1–29. https://doi.org/10.3390/agriculture12010009 

Orillo, J. W., Cruz, J. Dela, Agapito, L., Jensen, P., & Ira, S. (2014). Identification of diseases in rice plant (oryza sativa) using back propagation artificial neural network. &th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM). 

Palei, S., Behera, S. K., & Sethy, P. K. (2023). A systematic review of citrus disease perceptions perceptions and and fruit grading using machine vision. Procedia Computer Science, 218, 2504–2519. https://doi.org/10.1016/j.procs.2023.01.225 

Patel, S., Jaliya, D. U. K., & Patel, P. (2020). A survey on plant leaf disease detection. International Journal for Modern Trends in Science and Technology, 6(4), 129–134. https://doi.org/10.46501/IJMTST 

Patil, J. K., & Kumar, R. (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food, 10(2), 69–78. https://doi.org/10.1016/j.eaef.2016.11.004 

Paul, S. G., Amin, A. A., Saha, A., Zulfiker, M. S., Ritu, N. A., Zahan, I., … Islam, M. A. (2023). A real-time application-based convolutional neural network approach for tomato leaf disease classification. Array, 19(August), 100313. https://doi.org/10.1016/j.array.2023.100313 

Pavithra, G., & S Palanisamy, P. K. (2017). Computer based diagnosis of leaf rust disease using multiclass SVM. International Journal of Engineeing Research & Technology (IJERT), 5(17), 1–7. Retrieved from www.ijert.org 

Pawar, M. P., Turkar, D. V., & Patil, P. P. (2016). Cucumber disease detection using artificial neural network. International Conference on Inventive Computation Technologies (ICICT), 391–395. https://doi.org/10.1109/INVENTIVE.2016.7830151 

Perkebunan, D. J. (2023). Laporan Kinerja Direktorat Jenderal Perkebunan Tahun 2023. Jakarta. 

Petrellis, N. (2016). Plant lesion characterization for disease recognition. 2nd International Conference on Frontiers of Signal Processing StegIbiza: New Method for Information Hiding in Club Music, 20–24. 

Petrellis, N. (2017). A smart phone image processing application for plant disease diagnosis. 2017 6th International Conference on Modern Circuits and Systems Technologies (MOCAST), 1–4. https://doi.org/10.1109/MOCAST.2017.7937683 

Phadikar, S., Sil, J., & Das, A. K. (2013). Rice diseases classification using feature selection and rule generation techniques. Computers and Electronics in Agriculture, 90, 76–85. https://doi.org/10.1016/j.compag.2012.11.001 

Pican, N., Trucco, E., Ross, M., Lane, D. M., Petillot, Y., & Ruiz, I. T. (1998). Texture analysis for seabed classification: co-occurrence matrices\nvs. self-organizing maps. IEEE Oceanic Engineering Society. OCEANS’98. Conference Proceedings (Cat. No.98CH36259), 1, 424–428. https://doi.org/10.1109/OCEANS.1998.725781 

Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2015). EAGROBOT - A robot for early crop disease detection using image processing. 2nd International Conference on Electronics and Communication Systems, ICECS 2015, 1684–1689. https://doi.org/10.1109/ECS.2015.7124873 

Pinki, F. T., Khatun, N., & Islam, S. M. M. (2017). Content based paddy leaf disease recognition and remedy prediction using support vector machine. 2017 20th International Conference of Computer and Information Technology (ICCIT), 22–24. Dhaka: IEEE. 

Pinto, L. S. L. S., Ray, A., Reddy, M. U. U., Perumal, P., & Aishwarya, P. (2016). Crop disease classification using texture analysis. IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT, 825–828. https://doi.org/10.1109/RTEICT.2016.7807942 

Pires, R. D. L., Gonçalves, D. N., Oruê, J. P. M., Kanashiro, W. E. S., Rodrigues, J. F., Machado, B. B., & Gonçalves, W. N. (2016). Local descriptors for soybean disease recognition. Computers and Electronics in Agriculture, 125, 48–55. https://doi.org/10.1016/j.compag.2016.04.032 

Prajapati, B. S., Dabhi, V. K., & Prajapati, H. B. (2016). A survey on detection and classification of cotton leaf diseases. International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016, 2499–2506. https://doi.org/10.1109/ICEEOT.2016.7755143 

Prakash, R. M., Saraswathy, G. P., Ramalakshmi, G., Mangaleswari, K. H., & Kaviya, T. (2017). Detection of leaf diseases and classification using digital image processing. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 1–4. Coimbatore, India: IEEE. 

Prasad, S., Peddoju, S. K., & Ghosh, D. (2014). Energy efficient mobile vision system for plant leaf disease identification. Wireless Communications and Networking Conference (WCNC), 4, 3356–3361. https://doi.org/10.1109/WCNC.2014.6953083 

Prasetyo, E., Adityo, R. D., Suciati, N., & Fatichah, C. (2017). Mango leaf image segmentation on HSV and YCbCr color spaces using otsu thresholding. 2017 3rd International Conference on Science and Technology - Computer (ICST), 99–103. https://doi.org/10.1109/ICSTC.2017.8011860 

Rahayu, M. S., Lubis, L., & Oemry, S. (2017). Distribusi peta awal serangan penyakit jamur akar putih (rigidoporus microporus (swartz: fr)) pada beberapa perkebunan karet rakyat di kabupaten Asahan. Jurnal Agroekoteknologi FP USU, 5(1), 131–137. 

Ramakrishnan, M., & Sahaya, A. N. A. (2015). Groundnut leaf disease detection and classification by using back probagation algorithm. 2015 International Conference on Communication and Signal Processing, ICCSP 2015, (7092512506), 964–968. https://doi.org/10.1109/ICCSP.2015.7322641 

Ramesh, S, & Vydeki, D. (2020). Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Information Processing in Agriculture, 7(2), 249–260. https://doi.org/10.1016/j.inpa.2019.09.002 

Ramesh, Shima, Hebbar, M. R., M, N., R, P., Bhat N, P., N, S., & Vinod, M. P. V. (2018). Plant disease detection using machine learning. International Conference on Design Innovations for 3Cs Compute Communicate Control, 41–45. https://doi.org/10.1109/ICDI3C.2018.00017 

Rastogi, A., Arora, R., & Sharma, S. (2015). Leaf disease detection and grading using computer vision technology & fuzzy logic. 2nd International Conference on Signal Processing and Integrated Networks, SPIN 2015, 500–505. https://doi.org/10.1109/SPIN.2015.7095350 

Ratnasari, E. K., Mentari, M., Dewi, R. K., & Ginardi, R. V. H. (2014). Sugarcane leaf disease detection and severity estimation based on segmented spots image. International Conference on Information, Communication Technology and System, ICTS, 93–98. https://doi.org/10.1109/ICTS.2014.7010564 

Reddy, K. A., & Reddy, N. V. M. C. (2020). Precision method for pest detection in plants using the clustering algorithm in image processing. International Conference on Communication and Signal Processing, 894–897. India. 

Reza, Z. N., Nuzhat, F., Mahsa, N. A., & Ali, M. H. (2016). Detecting jute plant disease using image processing and machine learning. 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1–6. https://doi.org/10.1109/CEEICT.2016.7873147 

Rojanamontien, M., Sihanatkathakul, P., Piemkaroonwong, N., Kamales, S., & Watchareeruetai, U. (2016). Leaf identification using apical and basal features. 2016 8th International Conference on Knowledge and Smart Technology (KST), 234–238. Chiang Mai, Thailand. 

Rokhmah, D. N. (2017). Pengendalian penyakit gugur daun colletotrichum pada tanaman karet. 

Rothe, P. R., & Kshirsagar, R. V. (2015). Cotton leaf disease identification using pattern recognition techniques. International Conference on Pervasive Computing (ICPC), 1–6. Pune, India. 

Rothe, P. R., & Kshirsagar, R. V. (2014). Automated extraction of digital images features of three kinds of cotton leaf diseases. 2014 International Conference on Electronics, Communication and Computational Engineering, ICECCE 2014, 67–71. https://doi.org/10.1109/ICECCE.2014.7086637 

Rouf, A., Hadi, H., Setiono, Pamungkas, A. S., & Nugrahani, M. O. (2017). Paket teknologi optimasi pertumbuhan dan produktivitas tanaman karet pada daerah beriklim kering (studi kasus di perkebunan karet PTPN XII). Warta Perkaretan, 36(1), 39–54. 

Roy, C. B., Liu, H., Rajamani, A., & Saha, T. (2019). Transcriptome profiling reveals genetic basis of disease resistance against Corynespora cassiicola in rubber tree ( Hevea brasiliensis ). Current Plant Biology, 17(February), 2–16. https://doi.org/10.1016/j.cpb.2019.02.002 

Sabo, D., McMurray, G., & Rains, G. (2016). Presymptomatic disease detection and nanoparticle-enhanced electrical capacitance tomography. IFAC-PapersOnLine, 49(16), 116–120. https://doi.org/10.1016/j.ifacol.2016.10.022 

Sabrol, H., & Satish, K. (2016). Tomato plant disease classification in digital images using classification tree. International Conference on Communication and Signal Processing, 1242–1246. https://doi.org/10.1109/ICCSP.2016.7754351 

Sakeef, N., Scandola, S., Kennedy, C., Lummer, C., Chang, J., Uhrig, R. G., & Lin, G. (2023). Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data. Computational and Structural Biotechnology Journal, 21, 3183–3195. https://doi.org/10.1016/j.csbj.2023.05.005 

Saleem, M. H., Potgieter, J., & Arif, K. M. (2020). Plant disease classification : a comparative evaluation of convolutional neural networks and deep learning optimizers. Plants, 9(10). https://doi.org/https://doi.org/10.3390/plants9101319 

Sampian, A. F. M., Hashim, H., Kamal, M. M., Abdullah, N. E., Yussuf, U. R., Khairuzzaman, N. A., & Azmi, A. F. M. (2017). Vision system for detection of white root disease infection based on capacitance properties. Proceedings - 2016 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2016, (October), 186–189. https://doi.org/10.1109/I2CACIS.2016.7885312 

Sarayloo, Z., & Asemani, D. (2015). Designing a classifier for automatic detection of fungal diseases in wheat plant: By pattern recognition techniques. ICEE 2015 - Proceedings of the 23rd Iranian Conference on Electrical Engineering, 10, 1193–1197. https://doi.org/10.1109/IranianCEE.2015.7146396 

Sarkar, R. K., & Pramanik, A. (2015). Segmentation of plant disease spots using automatic srg algorithm : a look up table approach. International Conference on Advances in Computer Engineering and Applications (ICACEA), 129–133. 

Sengupta, S., & Das, A. K. (2017). Particle Swarm Optimization based incremental classifier design for rice disease prediction. Computers and Electronics in Agriculture, 140, 443–451. https://doi.org/10.1016/j.compag.2017.06.024 

Shabbir, I., Yusoff, M., Samad, A., Othman, R., & Wong, M. (2020). Rhizosphere White root rot disease suppression in rubber plant with microbial co- inoculants and silicon addition. Rhizosphere, 15(May), 100221. https://doi.org/10.1016/j.rhisph.2020.100221 

Shafik, W., Tufail, A., De Silva Liyanage, C., & Apong, R. A. A. H. M. (2024). Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biology, 24(1), 1–19. https://doi.org/10.1186/s12870-024-04825-y 

Shah, J. P., Prajapati, H. B., & Dabhi, V. K. (2016). A survey on detection and classification of rice plant diseases. 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 1–8. https://doi.org/10.1109/ICCTAC.2016.7567333 

Sharif, M., Attique, M., Iqbal, Z., Faisal, M., Ullah, M. I., & Younus, M. (2018). Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Computers and Electronics in Agriculture, 150(April), 220–234. https://doi.org/10.1016/j.compag.2018.04.023 

Shewale, M. V, & Daruwala, R. D. (2023). High performance deep learning architecture for early detection and classification of plant leaf disease. Journal of Agriculture and Food Research, 14(June), 100675. https://doi.org/10.1016/j.jafr.2023.100675 

Sibiya, M., & Sumbwanyambe, M. (2019). A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 1, 119–131. https://doi.org/10.3390/agriengineering1010000 

Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49. https://doi.org/10.1016/j.inpa.2016.10.005 

Sinha, A., & Shekhawat, R. S. (2020). Review of image processing approaches for detecting plant diseases. IET Image Processing, 14, 1427–1439. https://doi.org/10.1049/iet-ipr.2018.6210 

Sub Direktorat Statistik Tanaman Perkebunan. (2016). Statistik karet Indonesia 2016. Jakarta: Badan Pusat Statistik Indonesia. 

Sujatha, R., Moy, J., Jhanjhi, N. Z., & Nawaz, S. (2021). Microprocessors and microsystems performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 80(October 2020), 103615. https://doi.org/10.1016/j.micpro.2020.103615 

Sulaiman, M. S., Madzhi, N. K., Sajari, R., & Abdullah, N. E. (2017). Statistical discrimination of latex between healthy and white. Journal of Fundamental and Applied Sciences, 9(5S). https://doi.org/http://dx.doi.org/10.4314/jfas 

Sun, H., Xu, H., Liu, B., He, D., He, J., Zhang, H., & Geng, N. (2021). MEAN-SSD : A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture, 189(November 2020), 106379. https://doi.org/10.1016/j.compag.2021.106379 

Tan, D. S., Leong, R. N., Laguna, A. F., Ngo, C. A., Lao, A., Amalin, D. M., & Alvindia, D. G. (2018). AuToDiDAC: Automated tool for disease detection and assessment for cacao black pod rot. Crop Protection, 103(October 2017), 98–102. https://doi.org/10.1016/j.cropro.2017.09.017 

Tarigan, R. A. B., Ramadhan, M., & Kustini, R. (2021). Sistem pakar mendiagnosa penyakit pada tanaman havea brasiliensis ( karet ) menggunakan metode Certainlty Factor (CF). Jurnal CyberTech, 4(2). 

Tian, Y. W., Zheng, P. H., & Shi, R. Y. (2016). The detection system for greenhouse tomato disease degree based on android platform. Proceedings - 2016 3rd International Conference on Information Science and Control Engineering, ICISCE 2016, 706–710. https://doi.org/10.1109/ICISCE.2016.156 

Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., & Bhardwaj, D. S. (2020). Potato leaf diseases detection using deep learning. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS, 461–466. Madurai, India. 

Uguz, S., & Uysal, N. (2021). Classification of olive leaf diseases using deep convolutional neural networks. Neural Computing and Applications, 33(9), 4133–4149. https://doi.org/10.1007/s00521-020-05235-5 

Vrigazova, B. (2021). The proportion for splitting data into training and test set for the bootstrap in classification problems. Business Systems Research, 12(1), 228–243. 

W, M. P. K., Prabowo, S., & Yasirandi, R. (2020). Klasifikasi jenis daun menggunakan adaptive neuro-fuzzy inference system (ANFIS) dan image processing. E-Proceeding of Engineering :, 7(1), 2506–2516. 

Wang, H., Li, G., Ma, Z., & Li, X. (2012). Image recognition of plant diseases based on backpropagation networks. 2012 5th International Congress on Image and Signal Processing, CISP 2012, 894–900. https://doi.org/10.1109/CISP.2012.6469998 

Wulansari, Z., & Chulkamdi, M. T. (2021). Rubber plant disease diagnosis system using depth first search and certainty factor method. Josar, 6(1), 45–58. 

Xiong, Y., Liang, L., Wang, L., She, J., & Wu, M. (2020). Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Computers and Electronics in Agriculture, 177(July), 105712. https://doi.org/10.1016/j.compag.2020.105712 

Yadav, S., Sengar, N., Singh, A., Singh, A., & Kishore, M. (2021). Ecological informatics identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecological Informatics, 61(December 2020), 101247. https://doi.org/10.1016/j.ecoinf.2021.101247 

Yani, M., Machfud, Asrol, M., Hambali, E., Papilo, P., Mursidah, S., & Marimin, M. (2022). An adaptive fuzzy multi-criteria model for sustainability assessment of sugarcane agroindustry supply chain. Systems, Man And Cybernetics Society Section, 10, 5497–5517. https://doi.org/10.1109/ACCESS.2022.3140519 

Yao, Q., Chen, G. te, Wang, Z., Zhang, C., Yang, B. jun, & Tang, J. (2017). Automated detection and identification of white-backed planthoppers in paddy fields using image processing. Journal of Integrative Agriculture, 16(7), 1547–1557. https://doi.org/10.1016/S2095-3119(16)61497-1 

Yogeshwari, M., & Thailambal, G. (2023). Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks. Materials Today: Proceedings, 81(2), 530–536. https://doi.org/10.1016/j.matpr.2021.03.700 

Yu, K., Lin, L., Alazab, M., Member, S., Tan, L., & Gu, B. (2020). Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5g-enabled intelligent transportation system. IEEE Transactions On Intelligent Transportation Systems, 22(7), 4337–4347. 

Yulia, E., Istifadah, N., Widiantini, F., & Utami, H. S. (2017). Antagonisme trichoderma spp . Terhadap jamur rigidoporus lignosus ( klotzsch ) imazeki dan penekanan penyakit jamur akar putih pada tanaman karet. Jurnal Agrikultura, 28(1), 47–55. 

Yusoff, N. M., Abdul Halim, I. S., Abdullah, N. E., & Ab Rahim, A. A. (2019). Real-time hevea leaves diseases identification using sobel edge algorithm on FPGA: A preliminary study. IEEE Control and System Graduate Research Colloquium (ICSGRC 2018), (August), 168–171. https://doi.org/10.1109/ICSGRC.2018.8657603 

Zeng, T., Fang, J., Yin, C., Li, Y., Fu, W., Zhang, H., … Zhang, X. (2023). Recognition of rubber tree powdery mildew based on uav remote sensing with different spatial resolutions. Drones, 7, 533. 

Zeng, T., Li, C., Zhang, B., Wang, R., Fu, W., Wang, J., & Zhang, X. (2022). Rubber leaf disease recognition based on improved deep convolutional neural networks with a cross-scale attention mechanism. Frontiers in Plant Science, 13(February), 1–12. https://doi.org/10.3389/fpls.2022.829479 

Zhai, D. L., & Xu, J. C. (2023). The legacy effects of rubber defoliation period on the refoliation phenology, leaf disease, and latex yield. Plant Diversity, 45(1), 98–103. https://doi.org/10.1016/j.pld.2022.01.003 

Zhai, D., Thaler, P., Luo, Y., & Xu, J. (2021). The powdery mildew disease of rubber ( Oidium heveae ) is jointly controlled by the winter temperature and host phenology. International Journal of Biometeorology, 65, 1707–1718. https://doi.org/https://doi.org/10.1007/s00484-021-02125-w 

Zhai, D., & Xu, J. (2023). The legacy effects of rubber defoliation period on the refoliation phenology , leaf disease , and latex yield. Plant Diversity, 45(1), 98–103. https://doi.org/10.1016/j.pld.2022.01.003 

Zhang, S., Wu, X., You, Z., & Zhang, L. (2017). Leaf image based cucumber disease recognition using sparse representation classification. Computers and Electronics in Agriculture, 134, 135–141. https://doi.org/10.1016/j.compag.2017.01.014 

Zhang, S., Zhu, Y., You, Z., & Wu, X. (2017). Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases. Computers and Electronics in Agriculture, 140, 338–347. https://doi.org/10.1016/j.compag.2017.06.016 

Zhang, W., Teng, G., & Wang, C. (2013). Identification of jujube trees diseases using neural network. Optik, 124(11), 1034–1037. https://doi.org/10.1016/j.ijleo.2013.01.014 

Zhou, R., Kaneko, S., Tanaka, F., Kayamori, M., & Shimizu, M. (2014). Disease detection of cercospora leaf spot in sugar beet by robust template matching. Computers and Electronics in Agriculture, 108, 58–70. https://doi.org/10.1016/j.compag.2014.07.004