A Feature-Based Machine Learning Agent for Automatic Rice and Weed Discrimination

Cheng, Beibei; Matson, Eric T.

doi:10.1007/978-3-319-19324-3_46

Cited by 33 publications

(16 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Its application is primarily focused in the area of classifying the rice grains based on variety or milling quality (El- Telbany, 2006;Fayyazi et al, 2017;Gujjar and Siddappa, 2013;Guzman and Peralta, 2008;Kaur and Singh, 2013;Liu et al, 2005;Mousavi et al, 2012;Prajapati and Patel, 2013). Cheng and Matson (2015) used ML technique to differentiate between rice and weed. Zareiforoush et al…”

Section: Machine Learning In Rice Researchmentioning

confidence: 99%

Machine learnt image processing to predict weight and size of rice kernels

Singh

Vidyarthi

Tiwari

2019

Preprint

View full text Add to dashboard Cite

Accurate measurement of rice kernel sizes after milling is critical to design, develop and optimize rice milling operations. The size and mass of the individual rice kernels are important parameters typically associated with rice quality attributes, particularly head rice yield. In this study, we propose a novel methodology that combines image processing and machine learning (ML) ensemble to accurately measure the size and mass of several rice kernels simultaneously.We have developed an image processing algorithm with the help of recursive method to identify the individual rice kernels from an image and estimate the size of the kernels based on the pixels a kernel occupies. The number of pixels representing a rice kernel has been used as its digital fingerprint in order to predict its size and mass. We have employed a number of popular machine learning models to build a stacked ensemble model (SEM), which can predict the mass of the individual rice kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of our image processing and SEM are quantified using uncertainty quantification. The uncertainty quantification showed 3.6%, 2.5%, and 2.4% for mean errors in estimating the kernel length of small-grain (Calhikari-202), medium-grain (Jupiter), and long-grain (CL153) rice, respectively. Similarly, mean errors associated with predicting the 1000 grain weight are 4.1%, 2.9%, and 4.3% for Calhikari-202, Jupiter, and CL153, respectively. Use of the developed algorithm in rice imaging analyzers could facilitate head rice yield quantifications and promote quicker rice quality appraisals.

show abstract

Section: Machine Learning In Rice Researchmentioning

confidence: 99%

Machine learnt image processing to predict weight and size of rice kernels

Singh

Vidyarthi

Tiwari

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Pedro et al [34] applied fuzzy decisionmaking to identify weed shape, with fuzzy multicriteria decision-making strategy; they achieved the best accuracy of 92.9%. Cheng and Matson [35] adopted Decision Tree, Support Vector Machine (SVM), and Neural Network to identify weed and rice; the best accuracy they achieved is 98.2% by using Decision Tree. Sankaran and Ehsani [36] used quadratic discriminant analysis (QDA) and k-nearest neighbour (kNN) to classify citrus leaves infected with canker and Huanglongbing (HLB) from healthy citrus leaves; they got the highest overall accuracy of 99.9% by kNN.…”

Section: Related Workmentioning

confidence: 99%

Can Deep Learning Identify Tomato Leaf Disease?

Zhang

Liu

et al. 2018

Advances in Multimedia

152

View full text Add to dashboard Cite

This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.

show abstract

“…Later SVM classifier is used in predicting whether the plant is a maize crop or a weed. Similarly Cheng, Beibei, and Eric T. Matson [23] have explained how Clustering algorithm is used in the automatic discrimination of rice and weed crops. This research explains how a machine learning technique is used to discriminate between rice and weeds.…”

Section: Classification Of Agricultural Parameters Using Machine Learmentioning

confidence: 99%

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

Pai¹,

Of²,

Hegde³

2019

IJCA

View full text Add to dashboard Cite

Agriculture is considered as the economic backbone of almost all the countries in the world. As there is increase in population, the need for basic requirements such as food, clothing, shelters, and medicines also increases. To meet all these needs, advancement in agriculture is needed. Various technologies have been introduced in agriculture to increase the yield of several crops. One among them is the machine learning technology. Machine learning is an important area in computer science that allows the computers to write programs on its own without being explicitly programmed. Machine learning techniques in agriculture help to improve the traditional farming techniques in a very efficient way. The main purpose of this paper is to learn different use of machine learning techniques in agriculture and to develop machine learning model for agriculture.

show abstract

A Feature-Based Machine Learning Agent for Automatic Rice and Weed Discrimination

Cited by 33 publications

References 4 publications

Machine learnt image processing to predict weight and size of rice kernels

Machine learnt image processing to predict weight and size of rice kernels

Can Deep Learning Identify Tomato Leaf Disease?

Study on Machine Learning for Identification of Farmer’s Query in Kannada Language

Contact Info

Product

Resources

About