Rice Classification Research Articles

An Adaptive Feature Fusion Network with Superpixel Optimization for Crop Classification Using Sentinel-2 Imagery

Crop-type mapping is the foundation of grain security and digital agricultural management. Accuracy, efficiency and large-scale scene consistency are required to perform crop classification from remote sensing images. Many current remote-sensing crop extraction methods based on deep learning cannot account for adaptation effects in large-scale, complex scenes. Therefore, this study proposes a novel adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images. The selective patch module implemented in the network can adaptively integrate the features of different patch sizes to assess complex scenes better. TabNet was used simultaneously to extract spectral information from the center pixels of the patches. Multitask learning was used to supervise the extraction process to improve the weight of the spectral characteristics while mitigating the negative impact of a small sample size. In the network, superpixel optimization was applied to post-process the classification results to improve the crop edges. By conducting the crop classification of peanut, rice, and corn based on Sentinel-2 images in 2022 in Henan Province, China, the novel method proposed in this paper was more accurate, indicated by an F1 score of 96.53%, than other mainstream methods. This indicates our model’s potential for application in crop classification in large scenes.

A STUDY AND ANALYSIS OF RICE AND WHEAT PRODUCTION IN INDIA BASED ON SEASON USING MACHINE LEARNING CLASSIFICATION ALGORITHMS

A STUDY AND ANALYSIS OF RICE AND WHEAT PRODUCTION IN INDIA BASED ON SEASON USING MACHINE LEARNING CLASSIFICATION ALGORITHMS

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Crops are the most important and beneficial food source in Pakistan. The demand for food has been an increase in Pakistan due to population growth. Pakistan produced 7,410 million tons of rice according to the financial year survey 2020 (FYS-2020). Pakistani rice has been cultivated in 3,304 hectares of the agricultural land zone, and it is also export around the world. Rice is also increased by 0.6% Gross Domestic Product (GDP) of Pakistan (FYS-2020). The old and manual process of rice classification is more expensive and time-consuming. In this study, we describe a machine vision approach for rice identification. We use four different varieties of rice for the experimental process such as Pakei_Kaynat, Kaynat_Kauchei, and Kauchei_Super_Banaspati and Tootaa_Kauchei (P1, P2, P3, and P4). The 100 images dataset have been used for practical work and total calculated of 400 (4 x 100) image of rice. The different process has been deploying on available datasets such as introduction, preprocessing methodology, and result discussion. A quality enhancement technique has been implementing for clarifying between rice color and shape sampling, and it is also converted color image in gray scale level. Every image has been employing six different non-overlapping regions of interest (ROI’s) and calculated a total of 2400 (6 x 400) ROI’s. Binary (B), Histogram (H) and Texture (T) features have been implemented and extract 43 features on each ROI’s and total calculated 103,200 (2400 x 43) machine learning (ML) features. Best First Search (BFS) Algorithm was used for feature optimization. Different ML classifiers are implementing for experimental process namely; Function Multi-Layer-Perception, Function SMO, Random Tree, J48 Tree, Meta Classifier via Regression and Meta Bagging. The Function Multi-Layer-Perception overall accuracy (OA) has describe better accuracy result is 99.8333%.

Prediction and classification of rice leaves using the improved PSO clustering and improved CNN

Prediction and classification of rice leaves using the improved PSO clustering and improved CNN

RESPONSE OF RICE UNDER SALT STRESS

About World’s half population is fed up with rice, a highly popular and staple food worldwide. Due to rice's classification as a glycophyte, soil salinity poses a significant global issue. Salinity negatively influences rice grain yield, reproduction, and growth. One of the main obstacles in the coastal region of the world, salinity is the main obstacle. With conventional and cutting-edge breeding techniques, this issue can be resolved. We have concentrated on the breeding strategies that will be employed to address this problem. With the help of salt-resistant genes and marker-assisted selection, we can develop a salt-tolerant variety. Problems with salinity worldwide have been the topic in this review regarding how it affects rice and other plants.

ISO/IEC 25010 based evaluation of rice seed analyzer: a machine vision application using image processing technique

The traditional approach for quality assessment of rice is done by a human inspector manually which leads to inconsistencies and uncertainties in the assessment due to human error. To address this problem, researchers develop rice classification systems applying different methods. The development of these kinds of applications will contribute to the larger objective of maximizing the production of global food. This study introduced a new method of rice seed classification that applies hashing techniques pre-processing of image prediction and its precision rate is 93.06 percent, with a speed of 8.31 seconds per image. The developed application in this study was evaluated using ISO/IEC 25010 with total mean scores of 4.31 for functional suitability, 4.31 for performance efficiency, 4.58 for compatibility, 4.31 for usability, 4.58 for reliability, 4.51 for security, 4.28 for maintainability, and 4.42 for portability.

Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a “golden corn belt”. The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas.

Mapping Paddy Rice in Complex Landscapes with Landsat Time Series Data and Superpixel-Based Deep Learning Method

The spatial pattern and temporal variation in paddy rice areas captured by remote sensing imagery provide an effective way of performing crop management and developing suitable agricultural policies. However, fragmented and scattered rice paddies due to undulating and varied topography, and the availability and quality of remote sensing images (e.g., frequent cloud coverage) pose significant challenges to accurate long-term rice mapping, especially for traditional pixel and phenological methods in subtropical monsoon regions. This study proposed a superpixel and deep-learning-based time series method to analyze Landsat time series data for paddy rice classification in complex landscape regions. First, a superpixel segmentation map was generated using a dynamic-time-warping-based simple non-iterative clustering algorithm with preprocessed spectral indices (SIs) time series data. Second, the SI images were overlaid onto the superpixel map to construct mean SIs time series for each superpixel. Third, a multivariate long short-term memory full convolution neural network (MLSTM-FCN) classifier was employed to learn time series features of rice paddies to produce accurate paddy rice maps. The method was evaluated using Landsat imagery from 2000 to 2020 in Cengong County, Guizhou Province, China. Results indicate that the superpixel MLSTM-FCN achieved a high performance with an overall accuracy varying from 0.9547 to 0.9721, which presents an 0.17–1.23% improvement compared to the random forest method. This study showed that combining spectral, spatial, and temporal features with deep learning methods can generate accurate paddy rice maps in complex landscape regions.

Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation

Timely and accurate estimation of rice-growing areas and forecasting of production can provide crucial information for governments, planners, and decision-makers in formulating policies. While there exists studies focusing on paddy rice mapping, only few have compared multi-scale datasets performance in rice classification. Furthermore, rice mapping of large geographical areas with sufficient accuracy for planning purposes has been a challenge in Pakistan, but recent advancements in Google Earth Engine make it possible to analyze spatial and temporal variations within these areas. The study was carried out over southern Punjab (Pakistan)-a region with 380,400 hectares devoted to rice production in year 2020. Previous studies support the individual capabilities of Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) for paddy rice classification. However, to our knowledge, no study has compared the efficiencies of these three datasets in rice crop classification. Thus, this study primarily focuses on comparing these satellites’ data by estimating their potential in rice crop classification using accuracy assessment methods and area estimation. The overall accuracies were found to be 96% for Sentinel-2, 91.7% for Landsat-8, and 82.6% for MODIS. The F1-Scores for derived rice class were 83.8%, 75.5%, and 65.5% for Sentinel-2, Landsat-8, and MODIS, respectively. The rice estimated area corresponded relatively well with the crop statistics report provided by the Department of Agriculture, Punjab, with a mean percentage difference of less than 20% for Sentinel-2 and MODIS and 33% for Landsat-8. The outcomes of this study highlight three points; (a) Rice mapping accuracy improves with increase in spatial resolution, (b) Sentinel-2 efficiently differentiated individual farm level paddy fields while Landsat-8 was not able to do so, and lastly (c) Increase in rice cultivated area was observed using satellite images compared to the government provided statistics.

Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies

The accurate classification of rice is important to effectively identify food fraud, especially in China, where rice is widely used as a staple. This study explored the application of near-infrared (NIR) and Raman spectroscopies and low-level and mid-level data fusion for the classification of four types of rice from similar origins. Classification models were constructed on separate data blocks after different preprocessing methods using partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). Low-level data fusion spliced the two types of spectra and a classification technique was applied. Mid-level data fusion consisted of band selection and feature extraction from the spectra from each technique and the establishment of classification models. Based on fuzzy set theory, high-level data fusion was carried out for the results of PLS-DA models based on a single spectrum. The results show that mid-level data fusion is effective because its performance was superior to the use of one instrument. The highest accuracy was 87.5% for both NIR and Raman and 100% for the SVM model based on the mid-level data fusion. Mid-level data fusion of Raman and NIR spectroscopies is effective for the nondestructive identification of rice.

Quality Analysis and Classification of Rice using Image processing

Abstract: Grain quality analysis is a huge challenge in agricultural industries. Internal control is critical in the food industry because food products are characterized and rated into various categories after quality data has been collected. Grain quality assessment is performed by hand, but the results are subjective, lengthy, and pricey. To overcome the limitations and drawbacks of image processing techniques, different resolutions are used for grain quality analysis. Using image processing techniques, this paper proposes a method for grading and analyzing rice based on grain size and form. An edge detection algorithmic software is used in particular to determine the area of each grain's borders. we discover the endpoints of each grain using this technique, and we can then live the grain's endpoints using caliper.

Evaluation Of Machine Learning Classification Methods For Rice Detection Using Earth Observation Data: Case Of Senegal

Agriculture is considered one of the most vulnerable sectors to climate change. In addition to rainfed agriculture, irrigated crops such as rice have been developed in recent decades along the Senegal River. This new crop requires reliable information and monitoring systems. Remote sensing data have proven to be very useful for mapping and monitoring rice fields. In this study, a rice classification system based on machine learning to recognize and categorize rice is proposed. Physical interpretations of rice with other land cover classes in relation to the spectral signature should identify the optimal periods for mapping rice plots using three machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). The database is composed of field data collected by GPS and high spatial resolution (10 to 30 m) satellite data acquired between January and May 2018. The analysis of the spectral signature of different land cover show that the ability to differentiate rice from other classes depends on the level of rice development. The results show the efficiency of the three classification algorithms with overall accuracies and Kappa coefficients for SVM (96.2%, 94.5%), for CART (97.6%, 96.5%) and for RF (98% 97.1%) respectively. Unmixing analysis was made to verify the classification and compare the accuracy of these three algorithms according to their performance.

The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach

Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification.

Weedy Rice Classification Using Image Processing and a Machine Learning Approach

Weedy rice infestation has become a major problem in all rice-growing countries, especially in Malaysia. Challenges remain in finding a rapid technique to identify the weedy rice seeds that tend to pose similar taxonomic and physiological features as the cultivated rice seeds. This study presents image processing and machine learning techniques to classify weedy rice seed variants and cultivated rice seeds. A machine vision unit was set up for image acquisition using an area scan camera for the Red, Green and Blue (RGB) and monochrome images of five cultivated rice varieties and a weedy rice seed variant. Sixty-seven features from the RGB and monochrome images of the seed kernels were extracted from three primary parameters, namely morphology, colour and texture, and were used as the input for machine learning. Seven machine learning classifiers were used, and the classification performance was evaluated. Analyses of the best model were based on the overall performance measures, such as the sensitivity, specificity, accuracy and the average correct classification of the classifiers that best described the unbalanced dataset. Results showed that the best optimum model was developed by the RGB image using the logistic regression (LR) model that achieved 85.3% sensitivity, 99.5% specificity, 97.9% accuracy and 92.4% average correct classification utilising all the 67 features. In conclusion, this study has proved that the features extracted from the RGB images have higher sensitivity and accuracy in identifying the weedy rice seeds than the monochrome images by using image processing and a machine learning technique with the selected colour, morphological and textural features.

Simultaneous determination of apparent amylose, amylose and amylopectin content and classification of waxy rice using near-infrared spectroscopy (NIRS)

Rice starch properties of apparent amylose content (AAC), amylose content (AC), and amylopectin content (AP) are considered as the most important factors influencing grain quality as they are highly correlated with eating quality. This report is the first effort of predicting AC and AP values in rice flours, and recognizing waxy rice from non-waxy rice using NIRS technique. Calibration models generated by different mathematical, preprocessing treatments and combinations of wavelengths and signals were compared and optimized. The model established by modified partial least squares (MPLS) with “2, 8, 8, 2”/ Inverse MSC and ∼138 wavelengths signals yielded high RSQ of 0.977, 0.928, and 0.912 for AAC, AC and AP, respectively, as simultaneous measurement. MPLS-DA (discriminant analysis) could classify waxy and non-waxy rice with 100% accuracy. This high-throughput technology is valuable for breeding programs, and for the purposes of quality control in the food industry.

Classification of good and damaged rice using convolutional neural network

Rice production is massive in Indonesia, therefore maintaining the quality of the product is necessary. Detection and classification of objects have become a very important part in image processing. We performed object detection namely rice. After the object is found, it can be classified into two categories, namely good and damaged rice. We conducted a new study on rice which was carried out per group not per grain to obtain or classify good and damaged rice where we had carried out several steps, namely segmentation process using HSV (hue, saturation, value) color space. HSV is used because of its excellence in representing brightness of the image. We considered evaluating brightness because the tendency of damaged rice is darker or paler compared to good rice. To accomodate environment lighting ambiguity we perform the image acquisition in a controlled environment, so that all the images have the same light intensity. Here we use only channel V of HSV to be used in feature extraction using the gray-level co-occurrence matrix (GLCM) and finally convolutional neural network (CNN) is used for classification. From the test experiments that we have done, we have produced 83% prediction accuracy. Considering how similar the good rice is to the spoiled rice, the results are quite impressive.

Research progress regarding the function and mechanism of rice AP2/ERF transcription factor in stress response

<p id="C3">AP2/ERF (APETALA2/ethylene responsive factor) is a family of plant specific transcription factors that are widely involved in various biological processes including plant growth and development and stress responses. Rice is an important food crop in China, but it is severely affected by multiple adverse environmental factors during growth period. It has been found that AP2/ERF transcription factors play important roles in stress response in rice. In this paper, we reviewed the classification and structure architecture of rice AP2/ERF transcription factors and summarized the function and molecular mechanism of different AP2/ERF subfamilies in rice response to disease, drought, saline, and low temperature stresses. This study provides a reference for further interpretation of the molecular network of rice AP2/ERFs-mediated regulatory network in stress responses and their application potential for stress resistance improvement of rice cultivars.

Sensing and classification of rice (Oryza sativa L.) drought stress levels based on chlorophyll fluorescence

Sensing and classification of drought stress levels are very important to agricultural production. In this work, rice drought stress levels were classified based on the commonly used chlorophyll a fluorescence (ChlF) parameter (Fv/Fm), feature data (induction features), and the whole OJIP induction (induction curve) by using a Support Vector Machine (SVM). The classification accuracies were compared with those obtained by the K-Nearest Neighbors (KNN) and the Ensemble model (Ensemble) correspondingly. The results show that the SVM can be used to classify drought stress levels of rice more accurately compared to the KNN and the Ensemble and the classification accuracy (86.7%) for the induction curve as input is higher than the accuracy (43.9%) with Fv/Fm as input and the accuracy (72.7%) with induction features as input. The results imply that the induction curve carries important information on plant physiology. This work provides a method of determining rice drought stress levels based on ChlF.

Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series

Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utilized SAR data. The model learned region-specific and common features simultaneously, and yielded a significant improved performance compared with RF and AtBiLSTM in both global and local training scenarios. We found that the LSTM-MTL model achieved a regional F1 score up to 10% higher than both global and local baseline models. The results demonstrated that the consideration of spatial variability via LSTM-MTL approach yielded an improved crop classification performance at a large spatial scale. We analyzed the input-output relationship through gradient backpropagation and found that low VH value in the early period and high VH value in the latter period were critical for rice classification. The results of in-season analysis showed that the model was able to yield a high accuracy (F1 score = 0.746) two months before rice maturity. The integration between multi-task learning and multi-temporal deep learning approach provides a promising approach for crop mapping at large spatial scales.

Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.