Rice crop yield prediction in India using support vector machines

Gandhi, Niketa; Petkar, Owaiz; Armstrong, Leisa; Tripathy, Amiya Kumar

doi:10.1109/jcsse.2016.7748856

Cited by 149 publications

(65 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although previous studies have greatly improved yield prediction accuracy from spatial and temporal domains, they only focused on partial regions due to the complicated data process [20,37]. Crop yield prediction at a larger-area scale generally requires a large amount of data and complex data processing, suggesting high costs for acquiring and processing large data sets.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

View full text Add to dashboard Cite

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Our research proposal to deploy UGV, UAV [20] in Kaayar village , Tamil Nadu , India -has been shortlisted by a government [10] agency . We have developed a prototype of UGV , deployed in the KAAYAR village , collected data of soil moisture using IoT and proved that it is a feasible solution.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence and Agriculture 5. 0

Murugesan¹,

Kidambi²,

G³

et al. 2019

IJRTE

View full text Add to dashboard Cite

Abstract : Use of Artificial Intelligence and Robotics in agriculture is called as Agriculture 5.0 Disruptive technology should help in solving the social needs . Rogers suggest to develop human centric " Ubiquitous Computing " solution , for specific domain (agriculture production). Crop yield prediction (CYP) is vital to address the ever growing demand of food requirements of burgeoning world population and to prevent starvation . Artificial Intelligence can offer effective and practical solution for the problem. Machine Learning ( ML ) and Deep learning ( DL) have been evaluated. Machine Learning models ( using python , R , Seaborn ) have been experimented in this paper. Data of crop yield is used for model evaluation , which includes horticultural product (Banana) , cash crop ( Sugarcane), food crop (Rice) , for kharif , rabi season ( Dataset of Tamil Nadu and US region); Future research could combine remote sensing data and machine learning to predict the yield ( using google earth engine). Better accuracy in crop prediction is possible when vital data like soil moisture content ( ground level, root level and extreme ends of the field), 14 micro nutrients of soil is made available , for many seasons.

show abstract

“…In [3] N. Gandhi et al (2016), presented the overview on utilization of machine learning system for Indian rice editing ranges. Machine learning systems can be used to enhance forecast of harvest yield under various climatic situations.…”

Section: Related Workmentioning

confidence: 99%

Rice Crop Yield Prediction using Data Mining Techniques: An Overview

Patil¹,

Shirdhonkar²

2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-This paper shows the overview of rice crop yield prediction. Examines Different data mining techniques utilized for foreseeing rice crop yield. Rice crop creation assumes an imperative part in sustenance security of India, contributing over 40% to general yield generation. High harvest generation is reliant on appropriate climatic conditions. Inconvenient regular atmosphere conditions, for example, low precipitation or temperature extremes can drastically diminish edit yield. Growing better strategies to foresee edit efficiency in various climatic conditions can help rancher and different partners in vital basic leadership as far as agronomy and harvest decision. This paper reports utilization of various information mining methods will anticipate rice trim yield for Maharashtra state, India. To this review, 27 regions of Maharashtra were picked on the establishment of accessible information from openly available Indian Administration records with different atmosphere and harvest parameters. Precipitation, least temperature, normal temperature, most extreme temperature, reference trim evapotranspiration, range, generation and yield for the Kharif season (June to November) were the parameters choosen for the study for the years 1998 to 2002. WEKA tool was used for dataset processing

show abstract

Rice crop yield prediction in India using support vector machines

Cited by 149 publications

References 6 publications

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Artificial Intelligence and Agriculture 5. 0

Rice Crop Yield Prediction using Data Mining Techniques: An Overview

Contact Info

Product

Resources

About