Increasing population increases the need for food. As most population migrates towards cities for employment, the cultivable lands are turning into factories and apartments. The landlords are selling the plots due to the loss they face after cultivation. This loss occurs due to improper selection of crop for the particular field. The loss could be rectified if they are suggested with a suitable crop, based on the meteorological factors over the land area like testing soil quality, humidity, pH, etc. The farmers in interior places face difficulty in consulting with the experts for selection or rotation of crop. To overcome this problem, ANN came to play a role and also gave an effective solution. After knowing the suitable crop for the field, it is getting easier to decide the fertilizers and intercrop alongside. The profit rate will be considerably high using this method. It is also cost-efficient. This paper discusses the model for crop prediction using Machine learning algorithms. The model is compared with different approaches such as random forest, decision tree and SVM aiming to get a complete solution for the crop prediction and recommendation problem.
{"title":"Machine Learning Approach: Recommendation of Suitable Crop for Land Using Meteorological Factors","authors":"S. A, M. K, G. K","doi":"10.2139/ssrn.3736550","DOIUrl":"https://doi.org/10.2139/ssrn.3736550","url":null,"abstract":"Increasing population increases the need for food. As most population migrates towards cities for employment, the cultivable lands are turning into factories and apartments. The landlords are selling the plots due to the loss they face after cultivation. This loss occurs due to improper selection of crop for the particular field. The loss could be rectified if they are suggested with a suitable crop, based on the meteorological factors over the land area like testing soil quality, humidity, pH, etc. The farmers in interior places face difficulty in consulting with the experts for selection or rotation of crop. To overcome this problem, ANN came to play a role and also gave an effective solution. After knowing the suitable crop for the field, it is getting easier to decide the fertilizers and intercrop alongside. The profit rate will be considerably high using this method. It is also cost-efficient. This paper discusses the model for crop prediction using Machine learning algorithms. The model is compared with different approaches such as random forest, decision tree and SVM aiming to get a complete solution for the crop prediction and recommendation problem.","PeriodicalId":150383,"journal":{"name":"AgriSciRN: Soil (Topic)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124130934","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}
A. More, Ashishkumar Mouria, Namrata Panchal, Kavita Bathe
For endurable development in the agricultural field continuous cropping is necessary along with the constant check of soil fertility. Agricultural yield primarily depends on soil fertility. Soil nutrient measurement is very important for proper plant growth and effective fertilization. The current approach of measuring the soil nutrients is time-consuming because soil samples are to be collected from the field and it is measured in a laboratory situated in cities away from the farms. Due to more time consuming, There is a need to create a system that will generate similar results within less time. In this paper, a system is proposed that measures Soil nutrients (N, P, K) for rice crop using color sensor TCS3200. Results will be generated in a short time. The user can view the soil fertility as per their convenience on the web application. It will also suggest the farmers which organic fertilizers they can use to get better yield and maintain soil fertility.
{"title":"Soil Analysis Using Iot","authors":"A. More, Ashishkumar Mouria, Namrata Panchal, Kavita Bathe","doi":"10.2139/ssrn.3366756","DOIUrl":"https://doi.org/10.2139/ssrn.3366756","url":null,"abstract":"For endurable development in the agricultural field continuous cropping is necessary along with the constant check of soil fertility. Agricultural yield primarily depends on soil fertility. Soil nutrient measurement is very important for proper plant growth and effective fertilization. The current approach of measuring the soil nutrients is time-consuming because soil samples are to be collected from the field and it is measured in a laboratory situated in cities away from the farms. Due to more time consuming, There is a need to create a system that will generate similar results within less time. In this paper, a system is proposed that measures Soil nutrients (N, P, K) for rice crop using color sensor TCS3200. Results will be generated in a short time. The user can view the soil fertility as per their convenience on the web application. It will also suggest the farmers which organic fertilizers they can use to get better yield and maintain soil fertility.","PeriodicalId":150383,"journal":{"name":"AgriSciRN: Soil (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128929391","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}
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