A novel approach based on deep learning techniques and UAVs to yield assessment of paddy fields

Tri, Nguyen Cao; Duong, Hieu N.; Hoài, Trần Văn; Hoa, Tran Van; Nguyen, Vu; Toan, Nguyen Thanh; Snášel, Václav

doi:10.1109/kse.2017.8119468

Cited by 17 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This also includes developing an approach for mapping rice-growing areas at field level using phenology-based rice crop classification or paddy growth stages classification [34]- [36]. Predicting paddy rice yield estimation involves tasks such as yield assessment of paddy fields using machine learning algorithms [42] or mapping rice planted area using the hyperspectral data or remotely sensed data and vegetation indices [43].…”

Section: Phases and Tasks In Paddy Rice Smart Farmingmentioning

confidence: 99%

“…Remotely Sensed Data and Vegetation Indices: LSWI [35], [62], EVI [35], [43], [58], [59], [62], [67], NDVI [43], [57], [59], [67], [68], [109], MNDWI [57], Hyperspectral Images (Band 1 ∼ 4) [75]- [77], C-Band Synthetic Aperture Radar (SAR) [82] Drones Based Data: High-resolution Images [42], [84]- [86] Monitoring paddy rice disease Sensor Data: Wind speed and direction [41], Temperature (air, water, soil) [41], [164], Relative humidity [41], Rainfall [41].…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

“…Next, drone based data include all imageries captured using the drone technology. The high resolution images captured using drone can be used to estimate the paddy rice yield [42], [84]- [86], monitor paddy rice disease [37], [39], [87]- [92], classify paddy rice samples [46], [47], [93]- [101] and also assess the quality of paddy rice [102]- [104]. For instance, a near real-time deep learning approach for detecting rice phenology has also been designed based on high resolutions images taken by using drones [86].…”

Section: ) Drone Based Datamentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Phases and Tasks In Paddy Rice Smart Farmingmentioning

confidence: 99%

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

Section: ) Drone Based Datamentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, because this network requires two steps—offline training and online segmentation of rice images—it is not convenient enough. Tri et al [ 11 ] used a combination of drone shooting and deep learning to predict the yield of rice fields, whereby drones were first used to perform image acquisition of rice fields. The classification of rice was then completed through deep learning models.…”

Section: Introductionmentioning

confidence: 99%

“…The classification of rice was then completed through deep learning models. For the estimation of rice yield, Tri et al [ 11 ] manually counted one square meter of rice. After obtaining a large amount of data, the average rice yield per square meter was obtained.…”

Section: Introductionmentioning

confidence: 99%

Rice Ear Counting Based on Image Segmentation and Establishment of a Dataset

Shao

Tang

et al. 2021

Plants

View full text Add to dashboard Cite

The real-time detection and counting of rice ears in fields is one of the most important methods for estimating rice yield. The traditional manual counting method has many disadvantages: it is time-consuming, inefficient and subjective. Therefore, the use of computer vision technology can improve the accuracy and efficiency of rice ear counting in the field. The contributions of this article are as follows. (1) This paper establishes a dataset containing 3300 rice ear samples, which represent various complex situations, including variable light and complex backgrounds, overlapping rice and overlapping leaves. The collected images were manually labeled, and a data enhancement method was used to increase the sample size. (2) This paper proposes a method that combines the LC-FCN (localization-based counting fully convolutional neural network) model based on transfer learning with the watershed algorithm for the recognition of dense rice images. The results show that the model is superior to traditional machine learning methods and the single-shot multibox detector (SSD) algorithm for target detection. Moreover, it is currently considered an advanced and innovative rice ear counting model. The mean absolute error (MAE) of the model on the 300-size test set is 2.99. The model can be used to calculate the number of rice ears in the field. In addition, it can provide reliable basic data for rice yield estimation and a rice dataset for research.

show abstract

Autonomous Multifunctional Quadcopter for Real-Time Object Tracking and Seed Bombing in a Dynamic Environment

Nar

Amin

Banerjee

et al. 2019

Lecture Notes on Multidisciplinary Industrial Engineering

View full text Add to dashboard Cite

A novel approach based on deep learning techniques and UAVs to yield assessment of paddy fields

Cited by 17 publications

References 12 publications

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

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

Rice Ear Counting Based on Image Segmentation and Establishment of a Dataset

Autonomous Multifunctional Quadcopter for Real-Time Object Tracking and Seed Bombing in a Dynamic Environment

Contact Info

Product

Resources

About