Crop price prediction dataset

24.03.2021 By Mazull

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crop price prediction dataset

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Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications

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crop price prediction dataset

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Corn yield and futures price prediction Futures are contracts to buy and sell commodites on a future date at a specified price. Corporations can use futures to hedge against price increases and ensure access to limited goods, but accurate prediction of future commodity values is essential for avoiding unwise purchases. Corn grain poses a special challenge because future prices reflect weather-dependent supply as well as variable demand. This experiment demonstrates how publically-available meteorological and economic data can be used to predict corn futures price.

Feature selection In this experiment, the number of available features types of weather and market data recorded in each state in each year exceeds the size of the training set number of planting seasons. This experiment demonstrates the sequential forward selection approach to choosing a subset of features for use in prediction. Preparation Several reformatting steps and mergers are required to assemble these datasets into a shared data table that can be used for predictions with AzureML's standard tools.

Corn yield and futures price prediction

Some of the common procedures used are briefly summarized below; see the module contents for more details. Feature creation Severe weather at both extremes drought and flood, high and cold temperature can adversely impact yield. Specifically, we create binary indicators from the Palmer Drought Severity Index which signify whether water availability was extreme either drought or flood in each month.

We also add new features representing the square of monthly precipitation to account for nonlinear effects of precipitation. Weighted averaging of features Following the approach of Westcott and Jewisonwe focus our model on eight states which together account for three quarters of all corn production in the United States: Iowa, Illinois, Nebraska, Minnesota, Indiana, South Dakota, Ohio, and Missouri. Producing acreage-weighted averages of the features in our model over these eightstates reduces both noise and the number of missing values.

Unfortunately, it also reduces the number of observations available for training and validation. Adjusting for inflation Some variation in corn grain price with time is attributable to inflation.

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We also summarize the daily price information by computing a monthly average. Feature Selection and Model Training Motivation for use of Sequential Forward Selection Our goal is to predict the corn yield and corn futures prices in December earlier in the year. However, our experiment is underpowered to use all of these features: we must choose a subset to use for our predictor. One approach to feature selection would be to compute the correlation between each feature and the variable we would like to predict.

Unfortunately, this approach may lead to selection of features which are strongly correlated with one another and therefore redundant.

crop price prediction dataset

Sequential forward selection, however, ensures that each feature contributes to the model's performance independently of other chosen features. The approach is iterative: in each round, a model's current performance is compared to the same model with one candidate feature added. The candidate feature which best improves performance is added to the model; the process continues until either i no candidate feature improves the model quality or ii an upper limit on the number of features is reached.

The coefficients fit from this model suggest that early planting and unusually high July temperatures are negatively correlated with yield. Our corn yield predictor performed relatively well: its mean absolute error was 7. Corn futures price prediction Availability of corn futures price and consumer price index data needed toad just futures prices for inflation limited the observations available for corn futures price prediction to the years Sequential forward selection resulted in five features: the price of corn per bushel in June and July, precipitation in July, and planting progress for two weeks in late April.

Midsummer corn prices and early planting positively correlated with December prices, while July precipitation was negatively correlated with December corn price.Predicting the price a given crop will yield in the future is extremely valuable when determining which types of crops to encourage and plant.

Many variables go into predicting future prices for a given crop including but not limited to: climate, historical pricing, location, demand indicators, oil prices, and crop health. These and other related potentially predictive variables can be gleaned from myriad data sources including aerial imagery, weather, census, land use, market price and other data including your own.

At Xyonix, we regularly build AI and machine learning models to make predictions based on structured and unstructured data like these. Do you have an interest in accurately predicting crop prices from your data? Want to find out more first, then. Custom AI Solutions. Understanding Conversations. Sentiment Analysis. Churn Prediction. Video Segmentation.

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Solution Assessment. Dataset Analysis. Annotation Gathering. Model Development. Model Management. Virtual Chief Data Scientist. High Level System. Data Annotation. Model Experimentation. Health - Reducing Hospital Readmissions. Agriculture - Crop Monitoring. Agriculture - Crop Price Prediction. Agriculture - Crop Yield Prediction.

Analysis of agriculture data using data mining techniques: application of big data

Insurance - Churn Prediction. Insurance - Risk Measurement.

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Insurance - Fraud Detection. Insurance - Claims Forecasting. Want to find out more first, then read our whitepaper HERE describing in detail how AI is transforming agriculture — you will be affected.Artificial intelligence has created opportunities across many major industriesand agriculture is no exception.

Applying machine learning technologies to traditional agricultural systems can lead to faster, more accurate decision making for farmers and policy makers alike. As the foundation of many world economies, the agricultural industry is ripe with public data to use for machine learning. We at Lionbridge AI have gathered the best publicly available agricultural datasets for machine learning projects:.

Contains data for countries and more than primary products and inputs. Daily Vegetable and Fruits Prices data : This data set is having historical prices of Fruits and vegetables in Bengaluru, India from China Agro. Worldwide foodfeed production and distribution : Contains food and agriculture data for over countries and territories, from This dataset provides an insight on our worldwide food production — focusing on a comparison between food produced for human consumption and feed produced for animals.

The National Summary of Meats : Released by the US Department of Agriculture, this dataset contains records on meat production and quality as far back as Pesticide Use in Agriculture : This dataset includes annual county-level pesticide use estimates for pesticides active ingredients applied to agricultural crops grown in the contiguous United States.

V2 Plant Seedlings Dataset : A dataset of 5, images of crop and weed seedlings belonging to 12 species. Each class contains rgb images that show plants at different growth stages. The images are in various sizes and are in png format. Food Environment Atlas : A dataset containing over variables for researchers to study the interaction of access to healthy food options, demographic factors and economic indicators to inform policymakers. Feed Grains Database : Statistics on four feed grains corn, grain sorghum, barley, and oatsforeign coarse grains, hay, and related items.

Fertilizer Use and Price : Data on fertilizer consumption in the United States from by plant nutrient and major selected product, as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients.

In case you missed our previous dataset compilations, you can find them all here. Lionbridge AI provides custom AI training data in languages for your specific machine learning project needs. Originally from San Francisco but based in Tokyo, she loves all things culture and design. Sign up to our newsletter for fresh developments from the world of training data.

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Some features of the site may not work correctly. DOI: In this paper, we will discuss about the applications and techniques of Data mining in agriculture. This paper will consider the problem of price prediction of crops. View via Publisher. Open Access. Save to Library. Create Alert. Launch Research Feed.

Share This Paper. Topics from this paper. Data model K-means clustering. Support vector machine. Citations Publications citing this paper. Agricultural production output prediction using Supervised Machine Learning techniques Md.

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Multi-classifier ensemble system with dynamic rule based algorithm for stock prediction Sandeep SharmaGurpreet Kaur Computer Science Durgabai Computer Science Bharathi Environmental Science Smritikana Mitra Ghosh A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics Saripilli RajeswariK. SuthendranK. Building better predictive models for health-related outcomes Yamuna Kankanige Computer Science References Publications referenced by this paper.

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In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning.

Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change.

Today, India ranks second worldwide in the farm output.

crop price prediction dataset

Agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India.

Agriculture is a unique business crop production which is dependent on many climate and economy factors. Some of the factors on which agriculture is dependent are soil, climate, cultivation, irrigation, fertilizers, temperature, rainfall, harvesting, pesticide weeds and other factors.

Historical crop yield information is also important for supply chain operation of companies engaged in industries. These industries use agricultural products as raw material, livestock, food, animal feed, chemical, poultry, fertilizer, pesticides, seed and paper. An accurate estimate of crop production and risk helps these companies in planning supply chain decision like production scheduling. Business such as seed, fertilizer, agrochemical and agricultural machinery industries plan production and marketing activities based on crop production estimates [ 12 ].

The Best Way to Prepare a Dataset Easily

There are 2 factors which are helpful for the farmers and the government in decision making namely:. It helps farmers in providing the historical crop yield record with a forecast reducing the risk management.

It helps the government in making crop insurance policies and policies for supply chain operation. Data mining technique plays a vital role in the analysis of data. Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database system.

Unsupervised clustering and supervised classifications are two different types of learning methods in the data mining. The goal is that data points in the same cluster have a small distance from one another, while data points in different clusters are at a large distance from one another.

Cluster analysis divides data into well-formed groups. These methods are used to categorize the different districts of Karnataka which are having similar crop production. Clustering is considered as an unsupervised classification process [ 4 ]. A large number of clustering algorithms have been developed for different purposes [ 456 ].Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

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Email Address. Sign In. Access provided by: anon Sign Out. This paper aims at providing a new method to predict the crop yield based on big-data analysis technology, which differs with traditional methods in the structure of handling data and in the means of modeling. Firstly, the method can make full use of the existing massive agriculture relevant datasets and can be still utilized with the volume of data growing rapidly, due to big-data friendly processing structure.

Secondly, the "nearest neighbors" modeling, which employs results gained from the former data processing structure, provides a well-balanced result on the account of accuracy and prediction time in advance. Numerical examples on actual crop dataset in China from have showed a better performance and an improved prediction accuracy of the proposed method compared with traditional ones.

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