Crop yield prediction using machine learning: A systematic literature review

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The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

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Big data and disaster management: a systematic review and agenda for future research

India is a country where agriculture and agriculture related industries are the major source of living for the people Agriculture is a major source of economy of the country It is also one of the country which suffer from major natural calamities like drought or flood which damages the crop This leads to huge financial loss for the farmers thus leading to the suicide Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc Predicting a crop well in advance requires a systematic study of huge data coming from various variables like soil quality, pH, EC, N, P, K etc As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining Through data mining we extract the knowledge from the huge size of data This paper presents the study about the various data mining techniques used for predicting the crop yield The success of any crop yield prediction system heavily relies on how accurately the features have been extracted and how appropriately classifiers have been employed This paper summarizes the results obtained by various algorithms which are being used by various authors for crop yield prediction, with their accuracy and recommendation

A study on various data mining techniques for crop yield prediction

FANET are wireless ad hoc networks on unmanned aerial vehicles, and are characterized by high nodes mobility, dynamically changing topology and movement in 3D-space. FANET routing is an extremely complicated problem. The article describes the bee algorithm and the routing process based on the mentioned algorithm in ad hoc networks. The classification of FANET routing methods is given. The overview of the routing protocols based on the bee colony algorithms is provided. Owing to the experimental analysis, bio-inspired algorithms based on the bee colony were proved to show good results, having better efficiency than traditional FANET routing algorithms in most cases.

Application of bee colony algorithm for FANET routing

In many multistage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision-making approaches in multistage manufacturing supply chains to explicitly account for non-linear transportation costs. We have explored this problem by considering a two-stage production-transportation. A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created) and the finished goods inventories are stored, which is used to meet the final demand of the buyers. During each period, the optimised production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production vendor to warehouses and then to buyers, must be determined.

T. Arunkumar

Papers

Modelling of single sourcing, multiperiod, multivendor, multibuyer based VMI