Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier

Archana, K. S.; K.G, Dr.Saranya

doi:10.14445/23488387/ijcse-v7i5p101

Cited by 27 publications

(4 citation statements)

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“…SVM algorithm outperformed when compared to other machine learning algorithms such as RF, and KNN for the crop and fertilizer recommendation. K and K.G, 2020;Ali, (2021) proposed an ML model for crop yield prediction and fertilizer recommendation systems. In this, many machine learning algorithms were performed namely SVM, KNN, RF, and vot-ing-based ensemble classifiers were used.…”

Section: Literature Surveymentioning

confidence: 99%

Fertilizer type and quantity recommendation to increase oilseed crops yield prediction with inorganic fertilizers using machine learning algorithms

Mithra¹,

Suhasini²

2023

EEC

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Agriculture contributes significantly to India’s economy. The most serious threat to food security is population growth. Population growth increases demand, forcing farmers to produce more to increase supply. Crop yield prediction technology can help farmers to increase their output. Optimal fertilizer dose are required for boosting oilseed crop yield cultivation. However, when nutrients are scarce or overfertilization occurs, yields are considerably lowered and the environmental burden is increased. To address these issues, our proposed work employs machine learning techniques in the prediction of crop yield using inorganic fertilizer as well as the amount and type of agricultural fertilizer to be used for a specific crop in various districts of Tamil Nadu. Actual yield data from 1961 to 2007 is used as a training set, and data from 2008 to 2019 is used as a validation set. The results of the proposed algorithm are compared with those of the other machine learning algorithms namely random forest, linear regression, support vector machine, and naive bayes with an accuracy rate of 94%, 91.33%, 88.4% and 75.56% respectively are observed. According to the study, random forest results outperform other algorithms for crop yield prediction, and the decision tree algorithm works better for recommendation systems. The research also helps farmers by providing a recommendation system for determining which crop to plant and which type of inorganic fertilizer and how much quantity of fertilizer to use in a specific area and time. The proposed study also seeks to examine different observations for each method by changing parameters to see if the varying parameter influences the accuracy rate or not.

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Section: Literature Surveymentioning

confidence: 99%

Fertilizer type and quantity recommendation to increase oilseed crops yield prediction with inorganic fertilizers using machine learning algorithms

Mithra¹,

Suhasini²

2023

EEC

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“…In Agriculture, RSs have a significant impact on managing and using the resources efficiently, such as fertilizers, agrochemicals, irrigation. In [7], a fertilizer RS was developed to enrich the soil and increase its productivity. The authors used an ensemble classifier to suggest crops and evaluated their system using response time and accuracy measures.…”

Section: Recommendation Systems In Agriculturementioning

confidence: 99%

“…Application Reference e-commerce Items recommendations to buyers [4,5] Movie or video recommendations [43] Transportation Path Recommendation for transporting goodOr passengers [8,39,49] Recommendations to Tourists [50][51][52] Venue recommendation [53][54][55] e-health Medical advice or treatment plan recommendation [6,46,63,64] Recommending Personalized services to patients [44] Appointments recommendation to clinicians [45] Health recommendations in mobile systems [59] Healthy behavioral recommendations [61] Diet recommendation [62] Agriculture Fertilizer recommendation to farmers [7] Crops issue recommendation [47] Assisting farmers inquiries [48] Agricultural products recommendation [65] Crop cultivation suggestion [40,[66][67][68] Media Event recommendations [80] Museum recommendations [81,82] Multimedia recommendations [83][84][85] Open Social Networks recommendations [86][87][88][89][90]…”

Section: Areamentioning

confidence: 99%

“…These companies acquire massive users' data and tailor the RSs to meet the users' and business needs [4,5]. RSs are widely utilized in e-commerce and retail; they are also used in many other industries such as healthcare, transportation, and agriculture [6][7][8]. High-quality RSs positively impact the users' experience and the overall enterprises' revenue or decision.…”

Section: Introductionmentioning

confidence: 99%

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Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

et al. 2020

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Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

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Soil Classification and Crop Prediction Using Machine Learning Techniques

Goswami,

Ganapathi,

Goswami

2024

Signals and Communication Technology

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Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier

Cited by 27 publications

References 0 publications

Fertilizer type and quantity recommendation to increase oilseed crops yield prediction with inorganic fertilizers using machine learning algorithms

Fertilizer type and quantity recommendation to increase oilseed crops yield prediction with inorganic fertilizers using machine learning algorithms

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

Soil Classification and Crop Prediction Using Machine Learning Techniques

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