Journal ArticleDOI
Vision-based pest detection based on SVM classification method
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Results show that using SVM method with region index and intensify as color index make the best classification with mean percent error of less than 2.25%.About:
This article is published in Computers and Electronics in Agriculture.The article was published on 2017-05-01. It has received 305 citations till now.read more
Citations
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Journal ArticleDOI
Machine Learning in Agriculture: A Review.
TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
Journal ArticleDOI
Using deep transfer learning for image-based plant disease identification
TL;DR: This work study transfer learning of the deep convolutional neural networks for the identification of plant leaf diseases and consider using the pre-trained model learned from the typical massive datasets, and then transfer to the specific task trained by the authors' own data.
Journal ArticleDOI
Identification of plant leaf diseases using a nine-layer deep convolutional neural network
G. Geetharamani,J Arun Pandian +1 more
TL;DR: It is observed that using data augmentation can increase the performance of the model, and the proposed model achieves better performance when using the validation data.
Journal ArticleDOI
Plant Disease Detection and Classification by Deep Learning—A Review
Lili Li,Shujuan Zhang,Bin Wang +2 more
TL;DR: In this paper, the authors present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques, and discuss some of the current challenges and problems that need to be resolved.
Journal ArticleDOI
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Dhivya Elavarasan,P. M. Durai Raj Vincent,Vishal Sharma,Albert Y. Zomaya,Kathiravan Srinivasan +4 more
TL;DR: This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature and compares one approach with other using various error measures like Root Mean Square Error (RMSE) and Coefficient of Determination (R2).
References
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Journal ArticleDOI
Extraction of spots in biological images using multiscale products
TL;DR: A new method to detect and count bright spots in fluorescence images coming from biological immunomicroscopy experiments is presented, based on the multiscale product of subband images resulting from the a trous wavelet transform decomposition of the original image, after thresholding of non-significant coefficients.
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Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging
TL;DR: In this article, the potential of near-infrared hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated, where healthy wheat kernels and wheat kernels visibly damaged by Sitophilus oryzae, Rhyzopertha dominica, Cryptolestes ferrugineus, and Tribolium castaneum were scanned in the 1000-1600-nm wavelength range using an NIR hyperspectra imaging system.
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A new automatic identification system of insect images at the order level
TL;DR: A new automatic identification system has been designed to identify insect specimen images at the order level with good stability and accuracy, and results from tests using the support vector machine further improved accuracy.
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Detection of insect-damaged vegetable soybeans using hyperspectral transmittance image
TL;DR: In this article, a hyperspectral imaging technique for detecting insect-damaged vegetable soybeans was developed. But the method was not suitable for the detection of insects in vegetable soybean products, as it poses potential hazard to consumers.