Machine Learning Methodologies for Paddy Yield Estimation in India: a Case Study

Guruprasad, Ranjini; Saurav, Kumar; Randhawa, Sukanya

doi:10.1109/igarss.2019.8900339

Cited by 21 publications

(16 citation statements)

References 8 publications

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“…Several researches have been conducted that apply machine learning algorithm (e.g., Support Vector Machine (SVM) [46], [98], [128], Convolutional Neural Network (CNN) [94], [95], and hybrid approaches [103], [125]) in paddy rice sample recognition and classification using high-resolution images. Remotely sensed, vegetation indices and climate data are commonly used to predict paddy rice yield estimation [34], [35], [48], [76], [77], [109] and to monitor paddy rice growth [63], [73], [117] using artificial neural networks and its variants and also linear regression approaches. In addition to that, hyperspectral and high-resolution images have been used to accurately and affectively monitor paddy rice disease [40], [41], [87], [88], [119] and assessing quality of paddy rice [93], [104], [105] by using deep learning algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meteorological data (or climate data) can be used to monitor paddy rice growth [45], [48] and disease [41]. For instance, Guruprasad et al conducted a yield estimation modeling paddy crop at different spatial resolution (SR) levels based on weather and soil data as input features.…”

Section: ) Sensor Datamentioning

confidence: 99%

“…For instance, Guruprasad et al conducted a yield estimation modeling paddy crop at different spatial resolution (SR) levels based on weather and soil data as input features. These features include day and night temperature (min, max, mean), diffused irradiance, precipitation (cumulative), relative humidity, wind speed, rainfall, pH, soil moisture and temperature (0-40cm) [48].…”

Section: ) Sensor Datamentioning

confidence: 99%

“…Sensor Data: Wind speed and direction [45], [48], [112], Temperature (air, water, soil) [45], [48], [110]- [112], Relative humidity [45], [48], [112], Precipitation [45], [48], [110], [112], Rainfall [45], [111], [112], [157], Soil moisture [48].…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) Sensor Datamentioning

confidence: 99%

Section: ) Sensor Datamentioning

confidence: 99%

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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“…Agriculture is an important ingredient to mankind as it's the major source of livelihood [1]. The world population is significantly increasing that makes the monitoring and estimation of crop production a necessity [9].…”

Section: Introductionmentioning

confidence: 99%

ML Methods for Crop Yield Prediction and Estimation: An Exploration

Alagurajan¹,

Vijayakumaran²

2020

IJEAT

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Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

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