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Showing papers in "Computers and Electronics in Agriculture in 2018"


Journal ArticleDOI
TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

2,100 citations


Journal ArticleDOI
TL;DR: In this article, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies.

1,405 citations


Journal ArticleDOI
TL;DR: The paper concludes that the rapid advances in sensing technologies and ML techniques will provide cost-effective and comprehensive solutions for better crop and environment state estimation and decision making.

675 citations


Journal ArticleDOI
TL;DR: This work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley.

481 citations


Journal ArticleDOI
TL;DR: Experimental results indicate that while the technical constraints linked to automatic plant disease classification have been largely overcome, the use of limited image datasets for training brings many undesirable consequences that still prevent the effective dissemination of this type of technology.

340 citations


Journal ArticleDOI
TL;DR: An open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet is presented.

334 citations


Journal ArticleDOI
TL;DR: A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildews, and target leaf spots, and results showed that the DCNN was a robust tool for recognizing the cucumbers in field conditions.

323 citations


Journal ArticleDOI
TL;DR: The proposed hybrid method for detection and classification of diseases in citrus plants outperforms the existing methods and achieves 97% classification accuracy on citrus disease image gallery dataset, 89% on combined dataset and 90.4% on the authors' local dataset.

274 citations


Journal ArticleDOI
TL;DR: A survey on the different methods relevant to citrus plants leaves diseases detection and the classification reveals that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy and new tools are needed to fully automate the detection and Classification processes.

251 citations


Journal ArticleDOI
TL;DR: To enable the vision system in the detection of the weeds based on their pattern, support vector machine and artificial neural networks were employed and several shape features were integrated to establish a pattern for each variety of the plants.

186 citations


Journal ArticleDOI
TL;DR: The proposed technique incorporates two major steps of infected regions detection and finally feature extraction and classification, and outperforms several existing methods in terms of greater precision and improved classification accuracy.

Journal ArticleDOI
TL;DR: This work proposes to develop an intelligent IoT based hydroponic system by employing Deep Neural Networks which is first of its kind and is intelligent enough in providing the appropriate control action for the hydroponics environment based on the multiple input parameters gathered.

Journal ArticleDOI
TL;DR: The authors conclude that robust SLAM algorithms can support the development of forestry by providing cost-effective and acceptable quality methods for forest mapping and open up the possibility for precision localisation for forestry vehicles.

Journal ArticleDOI
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).

Journal ArticleDOI
TL;DR: It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales.

Journal ArticleDOI
TL;DR: This study deals with the problem of identifying infected areas of grapevines using Unmanned Aerial Vehicles (UAV) images in the visible domain and proposes a method based on Convolutional neural network and color information to detect symptoms in the vine yards.

Journal ArticleDOI
TL;DR: Several vegetation indices derived from aerial multispectral imagery were tested to estimate midday stem water potential of grapevines and showed high correlation between the estimated water potential through ANN (stem ANN) and the actual measured stem.

Journal ArticleDOI
TL;DR: A novel approach to automatically determine the locations for soil samples based on a soil map created from drone imaging after ploughing, and a wearable augmented reality technology to guide the user to the generated sample points is presented.

Journal ArticleDOI
TL;DR: A method for detecting the maturity levels (green, orange, and red) of fresh market tomatoes by combining the feature color value with the backpropagation neural network (BPNN) classification technique is proposed.

Journal ArticleDOI
TL;DR: The approach of integrating remotely sensed data and machine learning algorithms are promising for mapping soil properties and corn yield at a local scale, which can be useful in locating areas of potential concerns and implementing site-specific farming practices.

Journal ArticleDOI
TL;DR: This review introduces model predictive control (MPC), a process control method that originated in industry that is highly suited for application in agriculture because it can effectively address nonlinear and large time-delay systems.

Journal ArticleDOI
TL;DR: Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages, but can mature into an industrial application with the right integration framework.

Journal ArticleDOI
TL;DR: A novel methodology to generate renders of random meshes of plants based on empirical measurements, including the automated generation per-pixel class and depth labels for multiple plant parts, is proposed.

Journal ArticleDOI
TL;DR: An innovative unsupervised algorithm for vineyard detection and vine-rows features evaluation, based on 3D point-cloud maps processing, is presented and is found to be efficient and robust.

Journal ArticleDOI
TL;DR: This paper proposed to use Faster R-CNN to locate and identify individual pigs from a group-housed pen and an algorithm for associating the head of each pig with its body was designed.

Journal ArticleDOI
TL;DR: This work proposes the implementation of a Microsoft Kinect v1 depth camera for the fast, non-contact measurement of pig body dimensions such as heart girth, length and height, and two models (linear and non-linear) were developed and applied to the Kinect and manual measurement data.

Journal ArticleDOI
TL;DR: The AgroPortal project re-uses the biomedical domain's semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences, and offers a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web.

Journal ArticleDOI
TL;DR: The test results showed that the developed method could detect and identify insects under stored grain condition, and its mean Average Precision (mAP) reached 88.5%.

Journal ArticleDOI
TL;DR: The experimental results on 40 common pest species in field crops showed that the classification model with the multi-level learning features outperforms the state-of-the-art methods of pest classification.

Journal ArticleDOI
TL;DR: The potential of using Markov random fields which takes into account the spatial component among neighboring sites for herbicide resistance modeling of ryegrass is explored and results have revealed the good performance of this approach.