A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

[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..