Book ChapterDOI
A Review on Agricultural Advancement Based on Computer Vision and Machine Learning
Abriti Paul,Sourav Ghosh,Amit Kumar Das,Saptarsi Goswami,Sruti Das Choudhury,Soumya Sen +5 more
- pp 567-581
TLDR
This review paper gives an overview of machine learning and computer vision techniques which are inherently associated with this domain and tries to give an analysis, which can help researchers to look at some relevant problems in the context of India.Abstract:
The importance of agriculture in modern society need not be overstated. In order to meet the huge requirements of food and to mitigate, the conventional problems of cropping smart and sustainable agriculture have emerged over the conventional agriculture. From computational perspective, computer vision and machine learning techniques have been applied in many aspects of human and social life, and agriculture is not also an exception. This review paper gives an overview of machine learning and computer vision techniques which are inherently associated with this domain. A summary of the works highlighting different seeds, crops, fruits with the country is also enclosed. The paper also tries to give an analysis, which can help researchers to look at some relevant problems in the context of India.read more
Citations
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Journal ArticleDOI
Insect pest monitoring with camera-equipped traps: strengths and limitations
TL;DR: The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps to support the use of software and image recognition algorithms to identify and/or count insect species from pictures.
Journal ArticleDOI
A novel deep learning method for detection and classification of plant diseases
TL;DR: A robust plant disease classification system is introduced by introducing a Custom CenterNet framework with DenseNet-77 as a base network and is more proficient and reliable to identify and classify plant diseases than other latest approaches.
Book ChapterDOI
Visual Product Inspection Based on Deep Learning Methods
TL;DR: The present article deals with the above-mentioned method of deep learning, and especially with its application when recognizing certain objects and elements during the visual product inspection.
Journal ArticleDOI
Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts
Gayatri Mishra,Brajesh Kumar Panda,Wilmer Ariza Ramirez,Hye-Won Jung,Chandra B. Singh,Chandra B. Singh,Sang-Heon Lee,Ivan Lee +7 more
TL;DR: In this article, a review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging.
Journal ArticleDOI
Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging
TL;DR: A new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs and the results show that the segmentation algorithm could segment wheat ears from a complex field background and the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion.
References
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Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards
TL;DR: This paper evaluates the feasibility of applying visible-near infrared spectroscopy for in-field detection of Huanglongbing (HLB) in citrus orchards with high overall classification accuracies with low false negatives.
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A computer vision approach for weeds identification through Support Vector Machines
TL;DR: An automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops and a new strategy involving two processes: image segmentation and decision making is designed.
Proceedings ArticleDOI
Diagnosis and classification of grape leaf diseases using neural networks
TL;DR: In this paper, the authors used feed forward back propagation neural network (FPRNN) for classification of grape plant leaf image with complex background to identify the diseased portion from segmented images.
Journal ArticleDOI
Detecting Bakanae disease in rice seedlings by machine vision
TL;DR: The morphological and color traits of 3-week-old seedlings are examined and an approach to nondestructively distinguish infected and healthy seedlings at the age of 3weeks is proposed using machine vision.
Journal ArticleDOI
Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.
TL;DR: An in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion is given.