A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks

2015 ◽  
pp. 105-116 ◽  
Author(s):  
Sebastian Haug ◽  
Jörn Ostermann
Keyword(s):  
Computer Vision ◽  
Precision Agriculture ◽  
Image Dataset

scholarly journals Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽  
2018 ◽  
Vol 2018 (6) ◽  
Author(s):  
Yiannis Ampatzidis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Agricultural Systems ◽  
Disease Identification ◽  
Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

scholarly journals Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

2020 ◽  
Vol 27 (4) ◽  
pp. 20-33
Author(s):  
Paulo César Pereira Júnior ◽  
Alexandre Monteiro ◽  
Rafael Da Luz Ribeiro ◽  
Antonio Carlos Sobieranski ◽  
Aldo Von Wangenheim
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Ground Truth ◽  
Aerial Images ◽  
Weed Mapping ◽  
Classical Models ◽  
Classical Computer ◽  
Better Than

In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models

scholarly journals FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2440
Author(s):  
Faris A. Kateb ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid ◽  
Abu Quwsar Ohi ◽  
Muhammad Firoz Mridha
Keyword(s):  
Computer Vision ◽  
Learning Strategies ◽  
Precision Agriculture ◽  
Detection System ◽  
Detection Methods ◽  
High Complexity ◽  
Detection Mechanism ◽  
Detection Model ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detecting fruits. The FruitDet architecture is designed on the YOLO pipeline and achieves better performance in detecting fruits than any other detection model. The backbone of the detection model is implemented using DenseNet architecture. Further, the FruitDet is packed with newer concepts: attentive pooling, bottleneck spatial pyramid pooling, and blackout mechanism. The detection mechanism is benchmarked using five datasets, which combines a total of eight different fruit classes. The FruitDet architecture acquires better performance than any other recognized detection methods in fruit detection.

scholarly journals Recognition of Soybean Diseases Using Machine Learning Techniques Based on Segmentation of Images Captured By UAVs

2020 ◽  
Author(s):  
Gercina Da Silva ◽  
Alessandro Ferreira ◽  
Denilson Guilherme ◽  
José Fernando Grigolli ◽  
Vanessa Weber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Computer Program ◽  
Random Forests ◽  
Machine Learning Techniques ◽  
Target Spot ◽  
Learning Techniques ◽  
Image Dataset ◽  
Segmentation Of Images ◽  
Soybean Diseases

Soybean is an important product for the Brazilian economy, however it has factors that can limit its productive income, like the diseases that are generally difficult to control. Thus, this article aims to use a computer program to recognize diseases in images obtained by a UAV in a soybean plantation. The program is based on computer vision and machine learning, using the SLIC algorithm to segment the images into superpixels. To achieve the objective, after the segmentation of the images, an image dataset was created with the following classes: mildew, target spot, Asian rust, soil, straw and healthy leaves, totaling 22,140 images. Diagrammatic scales were used to assess disease severity. The disease recognition computer program explored four supervised learning techniques: SVM, J48, Random Forest and KNN. The techniques that obtained the best performance were SVM and Random Forests, taking into account the results obtained with all the evaluation metrics used. It was found that the program is efficient to differentiate the classes of diseases treated in this article.

scholarly journals How to Generate Image Dataset based on 3D Model and Deep Learning Method

2018 ◽  
Vol 7 (3.34) ◽  
pp. 221
Author(s):  
Sooyoung Cho ◽  
Sang Geun Choi ◽  
Daeyeol Kim ◽  
Gyunghak Lee ◽  
Chae BongSohn
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Recognition ◽  
Object Tracking ◽  
3D Model ◽  
Object Segmentation ◽  
Learning Method ◽  
Data Set ◽  
Image Dataset ◽  
Deep Learning Model

Performances of computer vision tasks have been drastically improved after applying deep learning. Such object recognition, object segmentation, object tracking, and others have been approached to the super-human level. Most of the algorithms were trained by using supervised learning. In general, the performance of computer vision is improved by increasing the size of the data. The collected data was labeled and used as a data set of the YOLO algorithm. In this paper, we propose a data set generation method using Unity which is one of the 3D engines. The proposed method makes it easy to obtain the data necessary for learning. We classify 2D polymorphic objects and test them against various data using a deep learning model. In the classification using CNN and VGG-16, 90% accuracy was achieved. And we used Tiny-YOLO of YOLO algorithm for object recognition and we achieved 78% accuracy. Finally, we compared in terms of virtual and real environments it showed a result of 97 to 99 percent for each accuracy.

scholarly journals Computer vision for wildfire research: An evolving image dataset for processing and analysis

2017 ◽  
Vol 92 ◽  
pp. 188-194 ◽  
Author(s):  
Tom Toulouse ◽  
Lucile Rossi ◽  
Antoine Campana ◽  
Turgay Celik ◽  
Moulay A. Akhloufi
Keyword(s):  
Computer Vision ◽  
Image Dataset

scholarly journals Different Classifiers in Classification of Raw Arecanut

2019 ◽  
Vol 8 (6) ◽  
pp. 1671-1676
Keyword(s):  
Precision Agriculture ◽  
Color Histogram ◽  
Image Dataset ◽  
Supervised Classifiers

Classification of crops is one of the important processes in precision agriculture. Classification of crops based on their verity, enhances the quality. In this paper, we presented a study of three main supervised classifiers, KNN, SVM and ANN for classifying the raw arecanut using color histogram and color moments as features. Experiments conducted over arecanut image dataset of 800 images across 4 classes. Among these classifiers K-NN gave a good result of 98.16% of with color histogram as feature.

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