Prediction of the fruit development stage of sweet pepper (Capsicum annum var. annuum) by an ensemble model of convolutional and multilayer perceptron

Biosystems Engineering - Tập 210 - Trang 171-180 - 2021
Taewon Moon1, Junyoung Park1, Jung Eek Son1,2
1Department of Agriculture, Forestry, and Bioresources, Seoul National University, Seoul 08826, Republic of Korea
2Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea

Tài liệu tham khảo

Abadi, 2016 Al-Halimi, 2015, Long-term yield prediction of greenhouse sweet pepper crops, GSTF Journal on Agricultural Engineering, 2, 1 Bozokalfa, 2010, Mathematical modeling in the estimation of pepper (Capsicum annuum L.) fruit volume, Chilean Journal of Agricultural Research, 70, 626, 10.4067/S0718-58392010000400013 Camelo, 2004, Manual for the preparation and sale of fruits and vegetables : From field to market, Food & Agriculture Organization, 151 Cárdenas-Pérez, 2017, Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system, Biosystems Engineering, 159, 46, 10.1016/j.biosystemseng.2017.04.009 Chen, 2019, Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages, Remote Sensing, 11, 1584, 10.3390/rs11131584 Chen, 2020, Detecting citrus in orchard environment by using improved YOLOv4, Scientific Programming, 2020, 10.1155/2020/8859237 Cruz, 2018, Light, ethylene and auxin signaling interaction regulates carotenoid biosynthesis during tomato fruit ripening, Frontiers of Plant Science, 9, 1370, 10.3389/fpls.2018.01370 Dandavate, 2020, CNN and data augmentation based fruit classification model Ganesh, 2019, Deep orange: Mask R-CNN based orange detection and segmentation, IFAC-PapersOnLine, 52, 70, 10.1016/j.ifacol.2019.12.499 Gao, 2020, Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN, Computers and Electronics in Agriculture, 176, 105634, 10.1016/j.compag.2020.105634 Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8 Hossain, 2018, Automatic fruit classification using deep learning for industrial applications, IEEE Transactions on Industrial Informatics, 15, 1027, 10.1109/TII.2018.2875149 Ignat, 2013, Nonlinear methods for estimation of maturity stage, total chlorophyll, and carotenoid content in intact bell peppers, Biosystems Engineering, 114, 414, 10.1016/j.biosystemseng.2012.10.001 Jovicich, 2004, Fruit yield and quality of greenhouse-grown bell pepper as influenced by density, container, and trellis system, HortTechnology, 14, 507, 10.21273/HORTTECH.14.4.0507 Kingma, 2015 Koirala, 2019, Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of ‘MangoYOLO’, Precision Agriculture, 20, 1107, 10.1007/s11119-019-09642-0 Koirala, 2019, Deep learning–Method overview and review of use for fruit detection and yield estimation, Computers and Electronics in Agriculture, 162, 219, 10.1016/j.compag.2019.04.017 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Li, 2021, Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm, Construction and Building Materials, 273, 121949, 10.1016/j.conbuildmat.2020.121949 Lin, 2007, Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers, Canadian Journal of Plant Science, 87, 137, 10.4141/P05-231 Marcelis, 1995, Growth analysis of sweet pepper fruits (Capsicum annuum L.), Acta Horticulturae, 412, 470, 10.17660/ActaHortic.1995.412.56 Marti, 1991, Nutrient uptake and yield of sweet pepper as affected by stage of development and N form, Journal of Plant Nutrition, 14, 1165, 10.1080/01904169109364275 Moon, 2019, Interpolation of greenhouse environment data using multilayer perceptron, Computers and Electronics in Agriculture, 166, 105023, 10.1016/j.compag.2019.105023 Ni, 2020, Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield, Horticulture Research, 7, 1, 10.1038/s41438-020-0323-3 Nwachukwu, 2007, Morphological and leaf epidermal features of Capsicum annuum and Capsicum frutescens Solanaceae, Nature and Science, 5, 54 O'Shea, 2015 Pagamas, 2008, Sensitive stages of fruit and seed development of chili pepper (Capsicum annuum L. var. Shishito) exposed to high-temperature stress, Scientia Horticulturae, 117, 21, 10.1016/j.scienta.2008.03.017 Panda, 2010, Remote sensing and geospatial technological applications for site-specific management of fruit and nut crops: A review, Remote Sensing, 2, 1973, 10.3390/rs2081973 Petrakova, 2015, Heterogeneous versus homogeneous machine learning ensembles, Information Technology and Management Science, 18, 135, 10.1515/itms-2015-0021 Pizarro, 2009, Light-dependent regulation of carotenoid biosynthesis in plants, Ciencia e Investigacian Agraria, 36, 143 Qaddoum, 2011, Adaptive neuro-fuzzy modeling for crop yield prediction, Parameters, 16, 17 Ren, 2016, Ensemble classification and regression-recent developments, applications and future directions, IEEE Computational Intelligence Magazine, 11, 41, 10.1109/MCI.2015.2471235 Sa, 2016, Deepfruits: A fruit detection system using deep neural networks, Sensors, 16, 1222, 10.3390/s16081222 Sauviller, 2008, Predicting the weekly yield fluctuations of greenhouse bell pepper, Acta Horticulturae, 817, 261 Suo, 2021, Improved multi-classes kiwifruit detection in orchard to avoid collisions during robotic picking, Computers and Electronics in Agriculture, 182, 106052, 10.1016/j.compag.2021.106052 Tian, 2019, Apple detection during different growth stages in orchards using the improved YOLO-V3 model, Computers and Electronics in Agriculture, 157, 417, 10.1016/j.compag.2019.01.012 Tijskens, 2016, From fruitlet to harvest: Modelling and predicting size and its distributions for tomato, apple and pepper fruit, Scientia Horticulturae, 204, 54, 10.1016/j.scienta.2016.03.036 Vélez-Rivera, 2014, Computer vision system applied to classification of “Manila” mangoes during ripening process, Food and Bioprocess Technology, 7, 1183, 10.1007/s11947-013-1142-4 Verlinden, 2005, Bell pepper production prediction based on colour development distribution, solar radiation and glass house temperature data, Acta Horticulturae, 674, 375, 10.17660/ActaHortic.2005.674.46 Verroens, 2006, Time series analysis of Capsicum annuum fruit production cultivated in greenhouse, Acta Horticulturae, 718, 97, 10.17660/ActaHortic.2006.718.10 Yamada, 2019, Anthocyanin production and enzymatic degradation during the development of dark purple and lilac paprika fruit, Journal of the American Society for Horticultural Science, 144, 329, 10.21273/JASHS04727-19 Yap, 2021, Deep learning in diabetic foot ulcers detection: A comprehensive evaluation, Computers in Biology and Medicine, 104596, 10.1016/j.compbiomed.2021.104596 Zhao, 2019, Object detection with deep learning: A review, IEEE Transactions on Neural Networks and Learning Systems, 30, 3212, 10.1109/TNNLS.2018.2876865 Zoratti, 2014, Light-controlled flavonoid biosynthesis in fruits, Frontiers of Plant Science, 5, 534, 10.3389/fpls.2014.00534