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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Journal ArticleDOI
Detection of early plant stress responses in hyperspectral images
TL;DR: An approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images, and it is shown that some VIs have overall relevance, while others are specific to particular senescence stages.
Journal ArticleDOI
Leaf image based cucumber disease recognition using sparse representation classification
TL;DR: This work proposes a novel cucumber disease recognition approach which consists of segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying disease leaf images using sparse representation (SR).
Journal ArticleDOI
Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods
TL;DR: In this paper, a crop yield forecast model for barley, canola and spring wheat grown on the Canadian Prairies was developed using vegetation indices derived from satellite data and machine learning methods.
Journal ArticleDOI
Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features
TL;DR: An application of neural network and image processing techniques for detecting and classifying Phalaenopsis seedling diseases, including bacterial soft rot (BSR), bacterial brown spot (BBS), and Phytophthora black rot (PBR).
Journal ArticleDOI
Rice diseases classification using feature selection and rule generation techniques
TL;DR: A rule base classifier has been built that cover all the diseased rice plant images and provides superior result compare to traditional classifiers.