WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics

Gupta, Rishi; Sharma, Akhilesh; Garg, Oorja; Modi, Krishna; Kasim, Shahreen; Baharum, Zirawani; Mahdin, Hairulnizam; Mostafa, Salama A.

doi:10.1109/access.2021.3117247

Cited by 39 publications

(10 citation statements)

References 22 publications

(14 reference statements)

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“…Another Graphic User Interface (GUI) supported crop prediction model [19] was created in the Flask environment using the Map-Reduce method. It provides the environment, where input can be given and suggested crops can be displayed on the user's screen.…”

Section: Other Techniquesmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Models for Crop Recommendation in India

Reddy,

Neerugatti

2023

RIA

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Agriculture serves as the mainstay of India's economy, bearing a vital responsibility in nourishing an expanding populace. The thriving of this sector is contingent upon numerous variables, among which the choice of the optimal crop plays a pivotal role. The advent of Machine Learning (ML) has engendered a transformative impact on the agricultural sector by facilitating the prediction of suitable crops, contingent on soil attributes. This study undertakes the examination of diverse ML algorithms, encompassing Decision Tree, Linear Regression, Naï ve Bayes, Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine, to assess their efficacy in recommending optimal crops based on soil parameters. The parameters under consideration include Phosphorus, Nitrogen, Potassium, Electrical Conductivity, pH, Organic Carbon, Boron, Iron, Zinc, Copper, Manganese, and Sulphur. The crop recommendations are focused on Rice, Cotton, and Jowar for the Kurnool district of Andhra Pradesh, India. Among the assessed models, it was observed that the XGBoost model surpassed others in terms of accuracy in determining the most suitable crop for the given soil parameters. The experimental findings substantiate the precision of the model in forecasting the apt crop, thereby underscoring the immense potential of ML in the agricultural domain. This investigation signifies a considerable stride towards optimal crop recommendation, thereby increasing the potential for enhanced yield and profitability. Through the incorporation of technological innovation, agriculture can be rendered more efficient, cost-effective, and sustainable, thus laying the groundwork for a promising future.

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Section: Other Techniquesmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Models for Crop Recommendation in India

Reddy,

Neerugatti

2023

RIA

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“…Four Machine learning algorithms namely linear regression, elastic net (EN), KNN, support vector regression (SVR) have been used for predicting the potato tuber yield in [4]. Different weather and soil parameters such as temperature, rainfall, soil, seed, and wind speed data have been collected and analysed in [7] to help the farmer for improving the crop production. In [8], deep transfer learning framework has been presented for predicting the crop yield in the developing countries by using the remote sensing data.…”

Section: Related Workmentioning

confidence: 99%

E-CropReco: A Dew-Edge-based Multi-parametricCrop Recommendation Framework for Internet ofAgricultural Things

Bera¹,

Dey²,

Mukherjee³

et al. 2022

Preprint

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Crop productivity prediction and recommendation is a significant research area of smart agriculture. This paper proposes an Internet of Things (IoT) framework based on dew computing, edge computing, and federated learning, where soil parameter, weather, and climate data are analysed to predict the crop productivity of a land, and then recommend suitable crop for the land. The dew layer pre-processes and accumulates the received sensor data, and forwards to the edge server. The edge server analyses the sensor data and the climate data, and then forwards the result along with the model characteristics to the cloud for further analysis. The proposed framework is simulated in iFogSim. The theoretical analysis shows that the proposed framework has reduced the delay by 60-70% approximately and power consumption by 70-80% approximately than the conventional IoT-cloud framework. We also observe that the proposed framework has reduced the delay by 12-35% approximately and power consumption by 30-50% approximately than the edge-cloud framework. We compare four machine learning algorithms based on their performance in data analysis in terms of precision, recall, accuracy, and F-Score. We observe that each classifier obtains more than 95% prediction accuracy. An Android application is also proposed for crop recommendation.

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“…Section 3 analyses various methods discussed in section 2. 2021) proposed a method using big data analytics to predict the top three crops which provides the best yield for a particular season and top 3 crops with the best yield throughout the year [9]. The required data was collected from a university website (The University of Dayton) and Kaggle.…”

Section: Review Articlementioning

confidence: 99%

Crop Prediction Methods: A Comparative Study

Shahana¹,

Kamal²,

Anoop³

et al. 2022

AJAEES

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In India 58% of the population depend upon agriculture and agriculture contributes about 18% of the gross domestic product. But the farmers face numerous challenges in agriculture due to various reasons. The farmers’ crop selection criteria generally depended upon their traditional base and testimonials of the success stories. The failure of selecting appropriate crop for a particular area makes them vulnerable to other vagaries also. Scientifically crop selection depends upon various factors like soil parameter (Ex: Soil moisture), weather parameters (Ex: Rainfall, temperature, Humidity), Geographical parameters (Ex: Slope) etc. Farmers and those who are venturing into farming requires a crop prediction model or a technique for the best crop selection decision in order to cope up and tide over exigencies like climate change, flood, drought etc which occur globally. The model will be accurate to tackle the issues arised by the climate change and also gives reasonable results to the farmers. This paper review on the existing crop prediction model and also their implication on the farming community.

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WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics

Cited by 39 publications

References 22 publications

A Comparative Analysis of Machine Learning Models for Crop Recommendation in India

A Comparative Analysis of Machine Learning Models for Crop Recommendation in India

E-CropReco: A Dew-Edge-based Multi-parametricCrop Recommendation Framework for Internet ofAgricultural Things

Crop Prediction Methods: A Comparative Study

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