A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
暂无分享,去创建一个
Angappa Gunasekaran | Sachin S. Kamble | Vikas Kumar | Anil Kumar | Rohit Sharma | A. Gunasekaran | Vikas Kumar | Rohit Sharma | Anil Kumar
[1] D. Cook,et al. Central venous catheter replacement strategies: a systematic review of the literature. , 1997, Critical care medicine.
[2] T. Marteau,et al. The Place of Inter-Rater Reliability in Qualitative Research: An Empirical Study , 1997 .
[3] John Elkington,et al. Partnerships from cannibals with forks: The triple bottom line of 21st‐century business , 1998 .
[4] Finn V. Jensen,et al. Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.
[5] Jonathan L. Shapiro,et al. Genetic Algorithms in Machine Learning , 2001, Machine Learning and Its Applications.
[6] D. Corney. Food Bytes: Intelligent Systems in the Food Industry , 2002 .
[7] Chun-Wei R. Lin,et al. Dynamic allocation of uncertain supply for the perishable commodity supply chain , 2003 .
[8] Jean-Marie Aerts,et al. Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall? , 2008 .
[9] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.
[10] Mac McKee,et al. Sparse Bayesian learning machine for real‐time management of reservoir releases , 2005 .
[11] Aaron J. Johnson,et al. Food distribution channel overview : a guide for new manufacturers , 2006 .
[12] A.G.J.M. Oude Lansink,et al. Performance indicators in agri-food production chains , 2006 .
[13] Lucien Duckstein,et al. Artificial neural networks and multicriterion analysis for sustainable irrigation planning , 2006, Comput. Oper. Res..
[14] Da-Wen Sun,et al. Learning techniques used in computer vision for food quality evaluation: a review , 2006 .
[15] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[16] T. M. Gajanana,et al. Marketing Losses and Their Impact on Marketing Margins: A Case Study of Banana in Karnataka , 2007 .
[17] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[18] B. De Baets,et al. Artificial neural network models of the rumen fermentation pattern in dairy cattle , 2008 .
[19] Tapabrata Maiti,et al. Neural network imputation: An experience with the national resources inventory survey , 2008 .
[20] Liu Qiang,et al. A Study on Vehicle Routing Problem in the Delivery of Fresh Agricultural Products under Random Fuzzy Environment , 2008 .
[21] Xiaojin Zhu,et al. Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.
[22] Panos M. Pardalos,et al. A survey of data mining techniques applied to agriculture , 2009, Oper. Res..
[23] Jesus René Villalobos,et al. Application of planning models in the agri-food supply chain: A review , 2009, Eur. J. Oper. Res..
[24] Ruslan Salakhutdinov,et al. Learning Deep Generative Models , 2009 .
[25] P. Ambus,et al. Emissions of nitrous oxide from arable organic and conventional cropping systems on two soil types , 2010 .
[26] V. Alchanatis,et al. Review: Sensing technologies for precision specialty crop production , 2010 .
[27] N. Pelletier,et al. Forecasting potential global environmental costs of livestock production 2000–2050 , 2010, Proceedings of the National Academy of Sciences.
[28] Hokey Min,et al. Artificial intelligence in supply chain management: theory and applications , 2010 .
[29] G. B. Schaalje,et al. Food shelf life: estimation and optimal design , 2010 .
[30] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Comparing machine learning classifiers in potential distribution modelling , 2011, Expert Syst. Appl..
[31] D. Tilman,et al. Global food demand and the sustainable intensification of agriculture , 2011, Proceedings of the National Academy of Sciences.
[32] S. Delwiche,et al. Use of Airborne Hyperspectral Imagery to Map Soil Properties in Tilled Agricultural Fields , 2011 .
[33] Mac McKee,et al. Forecasting daily potential evapotranspiration using machine learning and limited climatic data , 2011 .
[34] A K Bhetja,et al. Study of Green Supply Chain Management in the Indian Manufacturing Industries: A Literature Review cum an Analytical Approach for the measurement of performance , 2011 .
[35] M. Kummu,et al. Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. , 2012, The Science of the total environment.
[36] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[37] Anthea Sutton,et al. Systematic Approaches to a Successful Literature Review , 2012 .
[38] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[39] Selwyn Piramuthu,et al. RFID and perishable inventory management with shelf-space and freshness dependent demand , 2013 .
[40] Hamid R. Safavi,et al. Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms , 2013, Water Resources Management.
[41] Jan Adamowski,et al. Drought forecasting using new machine learning methods / Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod , 2013 .
[42] Sanjay Jharkharia,et al. Agri‐fresh produce supply chain management: a state‐of‐the‐art literature review , 2013 .
[43] Tang Jiali,et al. Identification of crop weed based on image texture features , 2014 .
[44] Eric W. T. Ngai,et al. Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles , 2014, Expert Syst. Appl..
[45] Paul C. Struik,et al. Epilogue: global food security, rhetoric, and the sustainable intensification debate , 2014 .
[46] Ingmar Nitze,et al. Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches , 2014 .
[47] Lutz Plümer,et al. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection , 2014, Precision Agriculture.
[48] George T. S. Ho,et al. Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry , 2014 .
[49] H. Godfray,et al. Food security and sustainable intensification , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.
[50] C. Weaver,et al. Processed foods: contributions to nutrition. , 2014, The American journal of clinical nutrition.
[51] Kannan Govindan,et al. Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food , 2014 .
[52] Jason M. Beddow,et al. A Bounds Analysis of World Food Futures: Global Agriculture Through to 2050 , 2014 .
[53] Evan J. Coopersmith,et al. Machine learning assessments of soil drying for agricultural planning , 2014 .
[54] Halil I. Cobuloglu,et al. A stochastic multi-criteria decision analysis for sustainable biomass crop selection , 2015, Expert Syst. Appl..
[55] T. Hodgson,et al. On the economic lot scheduling problem: stock-out prevention and system feasibility , 2015 .
[56] M. Porter,et al. How Smart, Connected Products Are Transforming Companies , 2015 .
[57] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[58] Leandro Zen Karam,et al. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning , 2015, Sensors.
[59] Shahaboddin Shamshirband,et al. Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm , 2015 .
[60] S. Saetta,et al. A logistic network to harmonise the development of local food system with safety and sustainability , 2015 .
[61] Angappa Gunasekaran,et al. Building theory of sustainable manufacturing using total interpretive structural modelling , 2015 .
[62] Erhan Çene,et al. Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey , 2015 .
[63] Saeid Minaei,et al. Potential Applications of Computer Vision in Quality Inspection of Rice: A Review , 2015, Food Engineering Reviews.
[64] George K. Karagiannidis,et al. Efficient Machine Learning for Big Data: A Review , 2015, Big Data Res..
[65] Virgilio Cruz-Machado,et al. Integration of practices and customer values in a supply chain , 2015 .
[66] Jordi Inglada,et al. Assessment of a Markov logic model of crop rotations for early crop mapping , 2015, Comput. Electron. Agric..
[67] Nai-Hua Chen,et al. Why buy organic rice? genetic algorithm‐based fuzzy association mining rules for means‐end chain data , 2015 .
[68] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[69] M. P. Singh,et al. Crop Selection Method to maximize crop yield rate using machine learning technique , 2015, 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM).
[70] James F. Cruise,et al. An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands , 2015, Environ. Model. Softw..
[71] Guy Fipps,et al. Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages , 2016 .
[72] Jean-Michel Boursiquot,et al. A Decision Support System for Vine Growers Based on a Bayesian Network , 2015, Journal of Agricultural, Biological, and Environmental Statistics.
[73] Aude Ridier,et al. A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks , 2016, Eur. J. Oper. Res..
[74] Masoud Rabbani,et al. Vehicle routing problem with considering multi-middle depots for perishable food delivery , 2016 .
[75] Feng Tian,et al. An agri-food supply chain traceability system for China based on RFID & blockchain technology , 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM).
[76] Michael E. Sykuta,et al. Big Data in Agriculture: Property Rights, Privacy and Competition in Ag Data Services , 2016 .
[77] Jesús Martínez del Rincón,et al. A decision support system for managing irrigation in agriculture , 2016, Comput. Electron. Agric..
[78] K. L. Luangkesorn,et al. Analysis of production systems with potential for severe disruptions , 2016 .
[79] M. Joardder,et al. Mathematical model for intermittent microwave convective drying of food materials , 2016 .
[80] Rebecca L. Whetton,et al. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy , 2016 .
[81] Ashkan Nabavi-Pelesaraei,et al. Applying optimization techniques to improve of energy efficiency and GHG (greenhouse gas) emissions of wheat production , 2016 .
[82] Kl L. Choy,et al. A cloud-based location assignment system for packaged food allocation in e-fulfillment warehouse , 2016 .
[83] R. Deo,et al. Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq , 2016 .
[84] Lars Relund Nielsen,et al. A hierarchical Markov decision process modeling feeding and marketing decisions of growing pigs , 2016, Eur. J. Oper. Res..
[85] H. Omrani,et al. Modeling Historical Land Use Changes at A Regional Scale: Applying Quantity and Locational Error Metrics to Assess Performance of An Artificial Neural Network Based Back-Cast Model , 2016 .
[86] Ravindra C. Thool,et al. Big Data in Precision Agriculture Through ICT: Rainfall Prediction Using Neural Network Approach , 2016 .
[87] Xiang Li,et al. A multi-period ordering and clearance pricing model considering the competition between new and out-of-season products , 2016, Ann. Oper. Res..
[88] L. Coelho,et al. Demand forecasting based on natural computing approaches applied to the foodstuff retail segment , 2016 .
[89] Jafar Habibi,et al. Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature , 2016, Comput. Electron. Agric..
[90] Rommel M. Barbosa,et al. Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry , 2016, Comput. Electron. Agric..
[91] V. Borimnejad,et al. Modeling consumer’s behavior for packed vegetable in “Mayadin management organization of Tehran” using artificial neural network , 2016 .
[92] Jungho Im,et al. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches , 2016, Environmental Earth Sciences.
[93] Henry C. W. Lau,et al. Cost-optimization modelling for fresh food quality and transportation , 2016, Ind. Manag. Data Syst..
[94] Valeria Borodin,et al. Handling uncertainty in agricultural supply chain management: A state of the art , 2016, Eur. J. Oper. Res..
[95] Xanthoula Eirini Pantazi,et al. Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..
[96] Kannan Govindan,et al. A novel mathematical model for a multi-period, multi-product optimal ordering problem considering expiry dates in a FEFO system , 2016 .
[97] G. Daily,et al. Sustainable intensification of agriculture for human prosperity and global sustainability , 2016, Ambio.
[98] L. Cobiac,et al. Role of food processing in food and nutrition security , 2016 .
[99] Andreas Kamilaris,et al. A review on the practice of big data analysis in agriculture , 2017, Comput. Electron. Agric..
[100] N. Pellegrini,et al. Cooking, industrial processing and caloric density of foods , 2017 .
[101] Jaroslaw Wikarek,et al. A constraint-driven approach to food supply chain management , 2017, Ind. Manag. Data Syst..
[102] Hiroshi Mineno,et al. Multi-modal sliding window-based support vector regression for predicting plant water stress , 2017, Knowl. Based Syst..
[103] Ida Wahyuni,et al. Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method , 2017 .
[104] Crescenzio Gallo,et al. Predicting consumer healthy choices regarding type 1 wheat flour , 2017 .
[105] J. Fernando Sánchez-Rada,et al. Enhancing deep learning sentiment analysis with ensemble techniques in social applications , 2020 .
[106] Gilad Ravid,et al. Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge , 2017, Precision Agriculture.
[107] Joachim Hertzberg,et al. Plant classification with In-Field-Labeling for crop/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system , 2017, Comput. Electron. Agric..
[108] Kwok-wing Chau,et al. Energy consumption enhancement and environmental life cycle assessment in paddy production using optimization techniques , 2017 .
[109] Ping Guo,et al. An interval multistage joint-probabilistic chance-constrained programming model with left-hand-side randomness for crop area planning under uncertainty , 2017 .
[110] Angappa Gunasekaran,et al. Role of Technology in Servitization , 2017 .
[111] Alneu de Andrade Lopes,et al. A survey of the applications of Bayesian networks in agriculture , 2017, Eng. Appl. Artif. Intell..
[112] Carlos Eugenio Oliveros,et al. Automatic fruit count on coffee branches using computer vision , 2017, Comput. Electron. Agric..
[113] Surendra M. Gupta,et al. A holistic approach for performance evaluation using quantitative and qualitative data: A food industry case study , 2017, Expert Syst. Appl..
[114] Soleiman Hosseinpour,et al. Application of multi-objective genetic algorithms for optimization of energy, economics and environmental life cycle assessment in oilseed production , 2017 .
[115] Ray Y. Zhong,et al. Data-driven food supply chain management and systems , 2017, Ind. Manag. Data Syst..
[116] Omolbanin Yazdanbakhsh,et al. An intelligent system for livestock disease surveillance , 2017, Inf. Sci..
[117] P. D. Voil,et al. To mulch or to munch? Big modelling of big data , 2017 .
[118] S. Wolfert,et al. Big Data in Smart Farming – A review , 2017 .
[119] Sendhil Mullainathan,et al. Machine Learning: An Applied Econometric Approach , 2017, Journal of Economic Perspectives.
[120] Alex Alves Freitas,et al. An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives , 2017, Expert Syst. Appl..
[121] Edgar Santos-Fernández,et al. Effects of imperfect testing on presence-absence sampling plans , 2017, Qual. Reliab. Eng. Int..
[122] L. F. Sanches Fernandes,et al. Improved framework model to allocate optimal rainwater harvesting sites in small watersheds for agro-forestry uses , 2017 .
[123] Panos M. Pardalos,et al. Data mining and optimisation issues in the food industry , 2017 .
[124] Zhang Zhiyong,et al. Distance-based separability criterion of ROI in classification of farmland hyper-spectral images , 2017 .
[125] H. Neil Geismar,et al. Integrated production and distribution scheduling with a perishable product , 2017, Eur. J. Oper. Res..
[126] Yang Xu,et al. Weed identification based on K-means feature learning combined with convolutional neural network , 2017, Comput. Electron. Agric..
[127] Bernard Kamsu-Foguem,et al. Data mining techniques on satellite images for discovery of risk areas , 2017, Expert Syst. Appl..
[128] Ilias Kyriazakis,et al. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring , 2017, Scientific Reports.
[129] C. W. Fox,et al. Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland , 2017, Comput. Electron. Agric..
[130] D. Berckmans,et al. Precision livestock farming for the global livestock sector , 2017 .
[131] Rajesh Ranganathan,et al. Modelling and optimisation of Indian traditional agriculture supply chain to reduce post-harvest loss and CO2 emission , 2017, Ind. Manag. Data Syst..
[132] Rohit Sharma,et al. A review on use of big data in warehousing to enhance accessibility of food , 2017, 2017 2nd International Conference on Communication and Electronics Systems (ICCES).
[133] Seyed Reza Hejazi,et al. A bi-objective, reliable single allocation p-hub maximal covering location problem: Mathematical formulation and solution approach , 2017 .
[134] Manoj Kumar Tiwari,et al. Optimising integrated inventory policy for perishable items in a multi-stage supply chain , 2018, Int. J. Prod. Res..
[135] A. Crane-Droesch. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture , 2018, Environmental Research Letters.
[136] Abdolabbas Jafari,et al. Evaluation of support vector machine and artificial neural networks in weed detection using shape features , 2018, Comput. Electron. Agric..
[137] Helman Enrique Hernández Riaño,et al. Vehicle routing problem for the minimization of perishable food damage considering road conditions , 2018, Logist. Res..
[138] Devesh Kumar Srivastava,et al. Supervised Rainfall Learning Model Using Machine Learning Algorithms , 2018, AMLTA.
[139] Lei Guo,et al. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery , 2018, Comput. Electron. Agric..
[140] Anthony Lam,et al. Research on Monitoring Platform of Agricultural Product Circulation Efficiency Supported by Cloud Computing , 2018, Wirel. Pers. Commun..
[141] Erik Hofmann,et al. Big data analytics and demand forecasting in supply chains: a conceptual analysis , 2018 .
[142] Seyed Taghi Akhavan Niaki,et al. A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution , 2018, Comput. Oper. Res..
[143] Stefanos D. Kollias,et al. An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[144] Yan Li,et al. Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition , 2018, Geoderma.
[145] David W. Lamb,et al. Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM) , 2018, Comput. Ind..
[146] Ravinesh C. Deo,et al. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting , 2018, Atmospheric Research.
[147] Ravinesh C. Deo,et al. Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting , 2018, Comput. Electron. Agric..
[148] Tamás Krisztin,et al. Semi-parametric spatial autoregressive models in freight generation modeling , 2018, Transportation Research Part E: Logistics and Transportation Review.
[149] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[150] Ravinesh C. Deo,et al. Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties , 2018, Comput. Electron. Agric..
[151] Mohammad Sadegh Allahyari,et al. Transition towards sustainability in agriculture and food systems: Role of information and communication technologies , 2018, Information Processing in Agriculture.
[152] Giuseppe Pirlo,et al. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement , 2018, Machines.
[153] María Romero,et al. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management , 2018, Comput. Electron. Agric..
[154] Patrizia Busato,et al. Machine Learning in Agriculture: A Review , 2018, Sensors.
[155] Albert Y. Zomaya,et al. Forecasting yield by integrating agrarian factors and machine learning models: A survey , 2018, Comput. Electron. Agric..
[156] Felix T. S. Chan,et al. Evaluating the Drivers to Information and Communication Technology for Effective Sustainability Initiatives in Supply Chains , 2017, Int. J. Inf. Technol. Decis. Mak..
[157] Salah Sukkarieh,et al. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..
[158] Senén Barro,et al. Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods , 2018, Comput. Electron. Agric..
[159] John P. Fulton,et al. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield , 2018, Comput. Electron. Agric..
[160] Angappa Gunasekaran,et al. Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives , 2018, Comput. Electron. Agric..
[161] Shahrina Md Nordin,et al. The effects of knowledge transfer on farmers decision making toward sustainable agriculture practices: In view of green fertilizer technology , 2018 .
[162] Francisco J. López-Pellicer,et al. Machine learning for automatic rule classification of agricultural regulations: A case study in Spain , 2018, Comput. Electron. Agric..
[163] Q. Feng,et al. How Research in Production and Operations Management May Evolve in the Era of Big Data , 2017 .
[164] Ravinesh C. Deo,et al. Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region , 2018, Atmospheric Research.
[165] G. Naik,et al. Challenges of creating sustainable agri-retail supply chains , 2018, IIMB Management Review.
[166] Guobao Song,et al. Chinese household food waste and its’ climatic burden driven by urbanization: A Bayesian Belief Network modelling for reduction possibilities in the context of global efforts , 2018, Journal of Cleaner Production.
[167] Nishikant Mishra,et al. Social media data analytics to improve supply chain management in food industries , 2017, Transportation Research Part E: Logistics and Transportation Review.
[168] Mu-Chen Chen,et al. Last-mile distribution planning for fruit-and-vegetable cold chains , 2018, The International Journal of Logistics Management.
[169] Roemi Fernández,et al. Automatic Detection of Field-Grown Cucumbers for Robotic Harvesting , 2018, IEEE Access.
[170] Brian G. Leib,et al. Prediction of cotton lint yield from phenology of crop indices using artificial neural networks , 2018, Comput. Electron. Agric..
[171] Sachin S. Kamble,et al. Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives , 2018, Process Safety and Environmental Protection.
[172] Rijo Jackson Tom,et al. IoT based hydroponics system using Deep Neural Networks , 2018, Comput. Electron. Agric..
[173] Xuping Wang,et al. Multi-objective optimization for delivering perishable products with mixed time windows , 2018, Advances in Production Engineering & Management.
[174] Dong Liu,et al. Application of Particle Swarm Optimization and Extreme Learning Machine Forecasting Models for Regional Groundwater Depth Using Nonlinear Prediction Models as Preprocessor , 2018 .
[175] C. Rama Krishna,et al. An IoT based smart irrigation management system using Machine learning and open source technologies , 2018, Computers and Electronics in Agriculture.
[176] Jiali Shang,et al. Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation. , 2018, The Science of the total environment.
[177] Gabriel Trierweiler Ribeiro,et al. Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand , 2018, International Journal of Production Economics.
[178] R. Patidar,et al. Development of novel strategies for designing sustainable Indian agri-fresh food supply chain , 2018, Sādhanā.
[179] Apichaya Lilavanichakul,et al. Classifying Consumer Purchasing Decision for Imported Ready-to-Eat Foods in China Using Comparative Models , 2018, Journal of Asia-Pacific Business.
[180] Sushma Jain,et al. A survey towards an integration of big data analytics to big insights for value-creation , 2018, Inf. Process. Manag..
[181] Young K. Chang,et al. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems , 2019, Comput. Electron. Agric..
[182] Xanthoula Eirini Pantazi,et al. Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers , 2019, Comput. Electron. Agric..
[183] Pete Smith,et al. Sustainability in global agriculture driven by organic farming , 2019, Nature Sustainability.
[184] Xianyi Zeng,et al. A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry , 2019, IEEE Access.
[185] Devanathan Sudharshan,et al. Examining multi-category cross purchases models with increasing dataset scale - An artificial neural network approach , 2019, Expert Syst. Appl..
[186] G. Morota,et al. Machine learning and data mining advance predictive big data analysis in precision animal agriculture , 2019 .
[187] Shefali Sonavane,et al. IoT based Smart Farming : Feature subset selection for optimized high-dimensional data using improved GA based approach for ELM , 2019, Comput. Electron. Agric..
[188] C. Folberth,et al. Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning , 2019, Agricultural and Forest Meteorology.
[189] Sushma Jain,et al. Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning , 2019, Comput. Electron. Agric..
[190] André Ludwig,et al. Event processing in supply chain management - The status quo and research outlook , 2019, Comput. Ind..
[191] Sachin S. Kamble,et al. Modeling the internet of things adoption barriers in food retail supply chains , 2019, Journal of Retailing and Consumer Services.
[192] R. Manzini,et al. Quality assessment of temperature-sensitive high-value food products: An application to Italian fine chocolate distribution , 2019, Sustainable Food Supply Chains.
[193] G. Antoniou,et al. Supply chain risk management and artificial intelligence: state of the art and future research directions , 2018, Int. J. Prod. Res..
[194] Mario Castelán,et al. A contextualized approach for segmentation of foliage in different crop species , 2019, Comput. Electron. Agric..
[195] Adel Azar,et al. A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks , 2019, Expert Syst. Appl..
[196] Angappa Gunasekaran,et al. Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications , 2019 .
[197] Angappa Gunasekaran,et al. Modeling the blockchain enabled traceability in agriculture supply chain , 2020, Int. J. Inf. Manag..