Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

Sagar, B. M.; Cauvery, N. K.

doi:10.11591/ijeecs.v12.i3.pp1087-1093

Cited by 23 publications

(13 citation statements)

References 8 publications

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“…B M Sagar and Cauvery N K surveyed on crop yield prediction method based on machine learning, data mining and big data analytics. Predicting maximum crop yield is considered to be a major concept of all these methods (Sagar and Cauvery, 2018). D. Sabareeswaran surveyed on techniques to detect crop disease, soil moisture and crop monitoring using the artificial neural networks and support vector machine (Sabareeswaran and Edwin Rober, 2016).…”

Section: Literature Surveymentioning

confidence: 99%

A framework for plant growth analysis and simulation using data analysis techniques

Mani

Malathy

Sagi

2020

IJIUS

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PurposeIn India, agriculture is considered as the major source of income for a major sector of people. Our country's GDP (Gross Domestic Product) can increase only if we focus on agriculture and its growth toward global economy. There have been several attempts to improve the agricultural sector since decades.Design/methodology/approachThis work describes about the design of a device which continuously monitors the plant growth and sends the data to a centralized database, where data is dynamically analyzed based on base references using various machine learning algorithms like regression, gradient descent, clustering etc.FindingsThis paper aims at analyzing the plant growth in of our country and focuses on the improvement of plant growth based on factors such as temperature, air moisture, radiant energy, carbon dioxide levels, soil pH& temperature through the design of a device.Originality/valueIt is anticipated to provide a solution by analyzing the plant growth percentage in different regions over a period of time. Based on the inferences, we will be able to suggest an optimum environment for the plant species to grow best. Various sensors like temperature and humidity sensors, light sensors and pH electrodes can be used in collecting data from the plant environment.

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Section: Literature Surveymentioning

confidence: 99%

A framework for plant growth analysis and simulation using data analysis techniques

Mani

Malathy

Sagi

2020

IJIUS

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“…On the other hand, the sensor mounted practices can be effective in understanding the needs of the agricultural conditions of the growing crop in a much scientific way [4,7]. The Big data analytics is one such technology, which provides an opportunity to analyse the various crop yield influencing factors to provide an optimum condition for the enhanced crop yields and also helps in designing the strategies for crop yield marketing [8,9].…”

Section: Introductionmentioning

confidence: 99%

Technology in Indian agriculture - a review

Upendra

Umesh²,

Varma³

et al. 2020

IJEECS

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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.

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“…Farmers grow crops which suits for the environmental condition of their region. Different types of crops are grown in different region because each crop region is defined based on homogeneity in the soil type, climatic conditions, rainfall, farming practices, availability of labor, irrigation facilities and many more [1]. Crop yield prediction is a fundamental agriculture problem to solve.…”

Section: Introductionmentioning

confidence: 99%

Towards a Unified Approach for Crop Yield Prediction

Pande¹

2020

IJETER

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Agriculture is one of the fundamental occupations for majority of the countries in the world. Especially, in developing nations like India, the country is primarily driven by agriculture sector, where agriculture and its associated businesses are the backbone of the Economy making it the integral revenue generator. With technological advancements in the recent years, crop yield prediction has gained wide importance, and has shown to have significant impact on the revenue generated from agriculture in every season. Multiple factors influence crop yield prediction, which in turn makes it a non-trivial and challenging task. Despite many proposed works in the area, crop yield prediction lacks a unified solution. This paper brings out the need for a unified framework through a comparative study of standard algorithms and attributes. The algorithms considered are Linear Regression, Random Forest, K Nearest Neighbors (KNN) and Stochastic Gradient Descent (SGD). Our results show that Random Forest outperforms the other standard algorithms by showing 91.62% accuracy in crop yield prediction. Further evaluation is done where the attributes that affect the crop yield most are ranked according to their impact based on Mean Absolute Error (MAE). With this, we make a case for the need for a unified approach for crop yield prediction.

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Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

Cited by 23 publications

References 8 publications

A framework for plant growth analysis and simulation using data analysis techniques

A framework for plant growth analysis and simulation using data analysis techniques

Technology in Indian agriculture - a review

Towards a Unified Approach for Crop Yield Prediction

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