scholarly journals Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

2018 ◽  
Vol 12 (3) ◽  
pp. 1087 ◽  
Author(s):  
B.M. Sagar ◽  
Cauvery N K
Keyword(s):  
Big Data ◽  
Crop Yield ◽  
Data Analytics ◽  
Crop Production ◽  
Big Data Analytics ◽  
Agricultural Practices ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Main Challenge ◽  
Crop Health

<p>Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of          Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics.  Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.</p>

scholarly journals Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 3521-3526
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Data Analytics ◽  
Seed Quality ◽  
Big Data Analytics ◽  
Computational Techniques ◽  
Yield Prediction ◽  
Crops Yield

Agriculture is one of the biggest fields to improve the economic rate of the country. Crop yield prediction is a new emerging idea in agriculture. There are several challenges of crops yield prediction in the field of precision agriculture are (i). Obtain minimized production due to climate change; (ii). Lead to different diseases; (iii). Availability of Water; (iv). No awareness of fertilizers and crop features; (v). Climate change; (vi). Unexpected weather events.Other loss factors in the agriculture are lowly seed quality, unplanned irrigation and exploitation of insecticides and fertilizers. The main aim of this research is to design the effective crop yield production and health risk analysis model by big data analytics model. Hence in this research our focus is on optimizing the significant parameters such as rainfall, temperature and fertilizers rate to obtain the P-values for testing the crop and also analyze the human health safety (farmers and suppliers) due to the dynamic change of environment and also soil nutrients. Big data analytics is the feasible platform to test and measure the crop grow in the particular agriculture field. It helps in climate, weather events prediction and also it is used to compute the sufficient resources for crop cultivation.

scholarly journals Technology in Indian agriculture - a review

2020 ◽  
Vol 20 (2) ◽  
pp. 1070
Author(s):  
R S Upendra ◽  
I M Umesh ◽  
R B Ravi Varma ◽  
B Basavaprasad
Keyword(s):  
Big Data ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Agricultural Practices ◽  
Advanced Technology ◽  
Economic Losses ◽  
Smart Farming ◽  
Digital Agriculture

Optimization of agricultural practices for enhanced crop yield is considered to be essential phenomena for the countries like India. In order to strengthen the economy and also to meet the food demand for the exponentially growing population, optimizing the agricultural practices has become necessity. In India, weather and geographical conditions are highly variable and were thought to be the major bottleneck of agricultural practices to achieve improved crop yield. Agricultural practices in India are facing many challenges such as change in climatic conditions, different geographical environment, conventional agricultural practices; economic and political scenario. Economic loss due to the lack of information on crop yield productivity is another major concern in the country. These hurdles can be overcome by the implementation of advanced technology in agriculture. Some of the trends observed are smart farming, digital agriculture and Big Data Analytics which provide useful information regarding various crop yields influencing factors and predicting the accurate amounts of crop yield. The exact prediction of crop yield helps formers to develop a suitable cultivation plan, crop health monitoring system, management of crop yield efficiently and also to establish the business strategy in order to decrease economic losses. This also makes the agricultural practices as one of the highly profitable venture. This paper presents insights on the various applications of technology advancements in agriculture such as Digital Agriculture, Smart Farming or Internet of Agriculture Technology (IoAT), Precision Agriculture, Crop Management, Weed and Pest control, Crop protection and Big data analytics.

scholarly journals Crop yield prediction integrating genotype and weather variables using deep learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252402
Author(s):  
Johnathon Shook ◽  
Tryambak Gangopadhyay ◽  
Linjiang Wu ◽  
Baskar Ganapathysubramanian ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Short Term Memory ◽  
Time Windows ◽  
Climatic Conditions ◽  
Support Vector ◽  
Yield Prediction ◽  
Lasso Regression ◽  
Important Time ◽  
Interpretable Models

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.

A Review on Spatial Big Data Analytics and Visualization

2018 ◽  
pp. 179-197
Author(s):  
Bangaru Kamatchi Seethapathy ◽  
Parvathi R
Keyword(s):  
Big Data ◽  
Mobile Phones ◽  
Spatial Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Climatic Conditions ◽  
Size Analysis ◽  
Spatial Big Data ◽  
Visualization Tools ◽  
Spatial Dataset

Spatial dataset, which is becoming nontraditional due to the increase in usage of social media sensor networks, gaming and many other new emerging technologies and applications. The wide variety of sensors are used in solving real time problems like natural calamities, traffic analysis, analyzing climatic conditions and the usage of GPS, GPRS in mobile phones all together creates huge amount of spatial data which really exceeds the traditional spatial data analytics platform and become spatial big data .Spatial big data provide new demanding situations for their size, analysis, and exploration. This chapter discusses about the analysis of spatial data and how it gets descriptive manipulation, so that one can understand how multi variant variables get interact with each other along with the different visualization tools which make the understanding of spatial data easier.

Big Data Analytics

2019 ◽  
pp. 24-52
Author(s):  
Iman Raeesi Vanani ◽  
Faezeh Mohammadipour
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Distinct Advantage ◽  
Mobile Data ◽  
Main Challenge ◽  
Change Leaders ◽  
The Way

The idea that we can get value from data has been discussed, but the main challenge is to use data effectively in order to facilitate smarter and better decision making and surpass our competitors. The change leaders in organization are now dealing with big data from both within and outside the enterprise, including structured and unstructured data, machine data, online and mobile data to supplement their organizational data pool and provide and facilitate the way through which the businesses can compete and operate successfully. Companies that invest in big data can have a distinct advantage over their competitors. Therefore, in this chapter, the concepts of big data analytics along with the relevant description of different categorization, capabilities, challenges are firstly explained, and then big data analytics techniques and methods are introduced and discussed to make the readers familiar with the way big data is applied in the enterprises.

scholarly journals Deep Neural Network Model for Proficient Crop Yield Prediction

2021 ◽  
Vol 309 ◽  
pp. 01031
Author(s):  
K. Pravallika ◽  
G. Karuna ◽  
K. Anuradha ◽  
V. Srilakshmi
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Knowledge Process ◽  
Fundamental Understanding ◽  
Yield Forecast ◽  
Learning Machine ◽  
Crop Yield Forecasting ◽  
And Storage

Crop yield forecasting mainly focus on the domain of agriculture research which has a great impact on making decisions like import-export, pricing and distribution of respective crops. Accurate predictions with well timed forecasts is very important and is a tremendously challenging task due to numerous complex factors. Mainly crops like wheat, rice, peas, pulses, sugarcane, tea, cotton, green houses etc. can be used for crop yield prediction. Climatic changes and unpredictability influence mainly on crop production and maintenance. Forecasting crop yield well before harvest time can help farmers for selling and storage. Agriculture deals with large datasets and knowledge process. Many techniques are there to predict the crop yield. Farmers are benefited commercially by these predictions. Factors such as Geno type, Environment, Climatic conditions and Soil types used in predicting the Yield. For predicting accurately we need to know the fundamental understanding and relationship between the interactive factors and the yield to reveal the relationships between the datasets which are comprehensive and powerful algorithms. Based on the study of various survey papers it has been found that in all the crop predictions, various deep learning, machine learning and ANN algorithms implemented to predict yield forecast and the results are analyzed.

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