Assessment of pre-treatment and classification methods for Java paddy field cropping pattern detection on MODIS images

Fatikhunnada, Alvin; Liyantono,; Solahudin, Mohamad; Buono, Agus; Kato, Tasuku

doi:10.1016/j.rsase.2019.100281

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…Secondly, remote sensing data or remotely sensed data and vegetation indices can be used in different ways in estimating paddy yield, monitoring paddy growth and diseases. Many studies are based on the mapping of rice-growing are [35] [43] [57], mapping cropping patterns [57]- [59], mapping paddy vulnerability to flooding [58].…”

Section: ) Remotely Sensed Data and Vegetation Indicesmentioning

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%

“…Indices that correlate with vegetation cover are also used in estimating paddy yield and monitoring paddy growth such as the NDVI, which is mostly used to predict paddy rice yields' estimation [43], [57], [59], assessing damage of regional rainfed paddy rice [44] and monitoring rice growth [63]. NVDI is used to measure the level of greenness and biomass of vegetation.…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

“…where ρNIR is the reflectance in the NIR and ρRed is the reflectance in the red and this NDVI ranges between -1 (no vegetation) and +1 (green vegetation). Both the NDVI and EVI are most commonly used vegetation indices to monitor the health of vegetation on the fields [35], [43], [57], [59], [66]- [69]. However, some researchers have reported that EVI is often preferred than NDVI as EVI is more responsive to biophysical variables, such as LAI [35], [67].…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

“…Given an unknown sample, k-NN finds k samples that are nearest to this unknown sample based on certain distance functions (e.g., Euclidean or Cosine distance methods) and take the average of the response variables from these k samples as the label (class) of the unknown samples [147]. k-NN can be used for paddy rice sample classification [59], [99], [103]. Compared to SVM, k-NN produces better accuracy in detecting and recognizing diseases from paddy plant leaf images [37].…”

Section: ) Monitoring Paddy Rice Diseasementioning

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: ) Remotely Sensed Data and Vegetation Indicesmentioning

confidence: 99%

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

Section: ) Monitoring Paddy Rice Diseasementioning

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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Parametric Analysis on Crop/Weed Classification via Optimized Convolutional Neural Network

Panda

Mishra

et al. 2021

2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)

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The 10-meter Winter Wheat Mapping in Shandong Province Using Sentinel-2 Data and Coarse Resolution Maps

Wang

Peng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Timely and accurate large-scale mapping of the spread of winter wheat (Triticum aestivum) is crucial to guarantee food security, study climate change, and monitor operational agriculture. Traditional winter wheat mapping frameworks are constrained by insufficient spatial resolution and heavy dependence on field surveys, while traditional machine learning models excessively rely on subjective judgment. Furthermore, collecting sufficient field samples covering a large area is expensive and time consuming. In this context, an automatic label update deep learning solution is developed to produce 10 m resolution winter wheat maps using Sentinel-2 data and existing coarse-resolution (30 m) winter wheat mapping products. In particular, a label update module considering the unique phenological (seasonal) characteristics of winter wheat is designed to update labels in the training phase. The results indicate that our method yields a satisfactory classification result with an overall accuracy exceeding 92% and an F1 score greater than 0.85 for all validation samples, even when no field survey data were used for training. Additionally, a 10 m spatial resolution winter wheat map for the entire Shandong province is generated, showing a significant correlation between the computed winter wheat map and the agricultural statistical land, with correlation coefficients of 0.95 and 0.78 at the municipal and county levels, respectively. The proposed methodology can serve as a viable and promising method for high-resolution, operational agricultural monitoring over large areas.

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Assessment of pre-treatment and classification methods for Java paddy field cropping pattern detection on MODIS images

Cited by 5 publications

References 28 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

Parametric Analysis on Crop/Weed Classification via Optimized Convolutional Neural Network

The 10-meter Winter Wheat Mapping in Shandong Province Using Sentinel-2 Data and Coarse Resolution Maps

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