Crop Yield Prediction Using Data Analytics and Hybrid Approach

Bhosale, Shreya V.; Thombare, Ruchita A.; Dhemey, Prasanna G.; Chaudhari, Anagha

doi:10.1109/iccubea.2018.8697806

Cited by 43 publications

(11 citation statements)

References 3 publications

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“…Despite challenges in accurately forecasting rainfall, the fuzzy logic integration ensures reasonable yield estimations within defined ranges, mitigating weather forecast uncertainties.Bhosale, S. V., Thombare, R.A., Dhemey, P.G., and Chaudhari, A.N. 's [14] study on crop yield prediction in India through data analytics and hybrid approaches facilitates informed decision-making for farmers, utilizing techniques like K-means clustering and Apriori algorithm. However, challenges such as data availability and algorithm complexity may impede widespread adoption, despite the potential to optimize agricultural productivity.Kavita and Pratistha Mathur's [15] research demonstrates the potential of machine learning in predicting crop yields for Indian farmers, offering insights for optimized agricultural production.…”

Section: Literature Reviewmentioning

confidence: 99%

Crop Yield Prediction for Cereals using Machine Learning

Koparde,

Behare,

Kasare

et al. 2024

Advancements in Communication and Systems

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Agriculture is a major contributor to India's financial well-being. However, challenges like population growth and climate change impact crop production. Machine learning is vital for crop forecasting and decision-making, assisting in selecting crops and optimizing farming practices According to our analysis, the most used features are humidity, temperature, soil type, rainfall, pH, area, production and the applied algorithm is Decision Tree, Support Vector Machine (SVM), Random Forest & Gradient Boosting to cultivate. This prediction helps identify the best cereal crops based on weather conditions. In recent years, farmers have faced issues like reduced rainfall and poor soil quality, leading to crop failures. Precision farming helps adapt crop management to changing environmental conditions, promoting smart farming. The study aims to help people grow high-yield cereal crops, plan their activities, and find solutions to agricultural challenges.

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Section: Literature Reviewmentioning

confidence: 99%

Crop Yield Prediction for Cereals using Machine Learning

Koparde,

Behare,

Kasare

et al. 2024

Advancements in Communication and Systems

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“…The quality of the agricultural sector is improved; hence, the overall economy is enhanced. This issue has been discussed in detail in the literature [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In [21], a review of machine learning algorithms to predict palm oil yield is discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

Crop Prediction Model Using Machine Learning Algorithms

et al. 2023

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Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.

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“…Using data analytics, researchers in [19] examined a massive agricultural data collection to provide meaningful information. We used K-Means and the Apriori technique to investigate the data's qualities.…”

Section: Related Workmentioning

confidence: 99%

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

Kumar¹,

Lahza²,

Sreenivasa³

et al. 2023

Computer Systems Science and Engineering

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Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren't getting the most out of their land because they don't use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two from Case-1 (dataset with many numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction.

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Crop Yield Prediction Using Data Analytics and Hybrid Approach

Cited by 43 publications

References 3 publications

Crop Yield Prediction for Cereals using Machine Learning

Crop Yield Prediction for Cereals using Machine Learning

Crop Prediction Model Using Machine Learning Algorithms

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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