{"title":"Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network","authors":"M. C S, Mohith Gowda H R, A. K A, R. R","doi":"10.2139/ssrn.3734201","DOIUrl":"https://doi.org/10.2139/ssrn.3734201","url":null,"abstract":"Agriculture stands as the backbone of our Nation, by Contributing 7% of the total Indian Economy. The drivers of agriculture are facing a huge problem in predicting the yield in different varieties of soil. Currently, the use of sophisticated technologies in the field of agriculture is underdeveloped when compared to other sectors over the past few decades. Self-mortality rates of farmers considerably increasing from the past four-five years. This is mainly due to the debt overhead usually caused by low yield. Crops yield decline considerably due to unpredictable weather, environmental changes, and diseases. This can say that agricultural landowners fear to use new technologies and tend to follow the age-old tradition of farming. The sphere of computing with its rigorous learning capabilities is inevitable to find a novel solution for agriculture-related issues. In this paper, this issue is addressed and have come up with an improvised idea to help farmers get a better yield for their crops. Deep learning has been used to predict the yield. This technology is made handy for every farmer to learn and use it effectively via a simple IoT device installed in their fields and a smartphone application. This improvised system has been named as Agriculture Yield Monitor System (AYMS). This has proved to be more efficient and beneficial to farmers.","PeriodicalId":267570,"journal":{"name":"Agriculture Engineering eJournal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122887974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of a Model to Predict and Intimate Optimum Farm Matching System for Sikkim Using Ms Office 2016 Software","authors":"Singh Mu","doi":"10.38177/ajast.2020.4208","DOIUrl":"https://doi.org/10.38177/ajast.2020.4208","url":null,"abstract":"Choice and usage of optimum tractor power and agricultural machinery size is important to decrease cost and complete agricultural operations in available time. Improper size machinery increases the production costs in the farms. Determination of optimum tractor power and machinery size is a tedious and complex procedure that requires many calculations and computational work. In this study, a Microsoft office 2016 software was developed to enable the model and imitate different conditions to determine optimum size of farm machinery and power considering all parameters for selection of farm machinery base on “the least cost method” for Sikkim. The program developed in this study was applied to the representative farm size and crops such as buck wheat, rice, and maize.","PeriodicalId":267570,"journal":{"name":"Agriculture Engineering eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}