Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

Kuanr, Madhusree; Rath, Bikram Kesari; Mohanty, Sachi Nandan

doi:10.14419/ijet.v7i4.15.23006

Cited by 44 publications

(15 citation statements)

References 12 publications

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“…The fuzzy model [4] for the recommendation of crops is going to suggest crops to farmers based on the weather condition and location information. IoT model [5] uses random forest and naï ve bayes algorithm for crops prediction using sensor data by considering values such as temperature, moisture also ph of soil data.…”

Section: Related Workmentioning

confidence: 99%

IMLAPC: Interfused Machine Learning Approach for Prediction of Crops

R¹,

Ashoka²,

V³

2022

RIA

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Agriculture is the main occupation of rural India, which promotes economic growth in the country's development. To increase the yield of the crops to feed the increasing population, it is essential to identify the crops which can be grown in the respective zones. In this article, the Fused Classifier Algorithm (FCA) and Interfused Machine Learning Algorithm (IMLA) are proposed to predict crops suitable for the land based on the zones and agro-climatic parameters. Focusing on the zones of the Karnataka region, the model predicts the crop to the farmers. The different machine learning models such as naïve Bayes, decision tree, neighbors, multilayer perceptron have also been evaluated by varying the hyperparameters and checked for accuracy of the models built. The FCA algorithm merges different algorithms using the error rate with hyperparameters tuning and is given to IMLA to predict crops. This article also compares different machine learning classifiers with the proposed IMLA algorithm, which shows better accuracy with 82.7%.

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Section: Related Workmentioning

confidence: 99%

IMLAPC: Interfused Machine Learning Approach for Prediction of Crops

R¹,

Ashoka²,

V³

2022

RIA

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show abstract

“…Artificial intelligence (AI) approaches have become more prevalent in a variety of fields. For instance, recommender systems provide consumers with recommendations for selecting different items from a massive pool of items [26]. Consequently, it creates a program that can allow people to select requirements and remove the dilemma.…”

Section: B Recommendation Techniquementioning

confidence: 99%

Optimizing Smartphone Recommendation System through Adaptation of Genetic Algorithm and Progressive Web Application

Samah¹,

Azam²,

Hamzah³

et al. 2021

IJACSA

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The ubiquity of smartphone use nowadays is undeniable exponentially growing, replaced cell phones, and a host of other gadgets replaced personal computers to a certain degree. Different smartphones specifications and overwhelmed smartphone advertisements have caused broader choices for the customer. Many qualitative and quantitative criteria need to consider, and customers want to select the most suitable smartphones. They face difficulties deciding the best smartphone according to their budget and desire. Thus, a new method is needed to recommend the customer according to their preferences and budget. This study proposed a method for optimizing the recommendation system of the smartphone using the genetic algorithm (GA). Moreover, it is implemented with a progressive web application (PWA) platform to ensure the customer can use it on multiple platforms. They can choose the platform to input any specification of smartphone preferences besides the budget. Functional testing results showed the achievement of the study's objectives, and usability testing using UEQ managed to receive feedback of 93.64%, with an overall average mean of 4.682. Therefore, according to the outcome, it can be concluded that optimizing the smartphone recommendations through GA enables the customer to ease the comparison based on the obtained optimum result.

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“…Kuanr et al [14] designed a collaborative recommender system that based on prior idea as regards the suitability of crops in specific location from the knowledge of previous months' weather condition gives recommendations to farmers. The system also recommends other seeds and pesticides depending on the location and farmers' preferences.…”

Section: Recommendation Systems For Agriculturementioning

confidence: 99%

Using Prescriptive Analytics for the Determination of Optimal Crop Yield

Yange¹,

Egbunu²,

Rufai³

et al. 2020

IJDSA

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The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.

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Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

Cited by 44 publications

References 12 publications

IMLAPC: Interfused Machine Learning Approach for Prediction of Crops

IMLAPC: Interfused Machine Learning Approach for Prediction of Crops

Optimizing Smartphone Recommendation System through Adaptation of Genetic Algorithm and Progressive Web Application

Using Prescriptive Analytics for the Determination of Optimal Crop Yield

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