Proceedings Article10.1109/ICSSIT.2018.8748404
Application of UAV for Pest, Weeds and Disease Detection using Open Computer Vision
RL Hari Shankar,A. K. Veeraraghavan,Uvais,K. Sivaraman,S. Shreyas Ramachandran +4 more
- 01 Dec 2018
21
TL;DR: The proposed system uses a technology which utilizes machine learning and ANN algorithms using UAV that helps to locate regions that are affected by diseases and pesticides so that it can particularly focus on the regions that were affected and apply chemicals only in that particular area, with the entire system being cost effective.
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Abstract: Sustainable agriculture is an important field where not much attention is given though it is highly necessary, so as to monitor the growth of crops for their efficient growth in most nutritious ways. For effective growth of crops, lot of chemicals like fertilizers and pesticides are used, however, excessive usage of them results in damage to land and water resources. The attack of pests is a major criteria which affect crop yield. Various crop monitoring technologies are available which are highly expensive and not all farmers can afford. Moreover in India, farmers are not capable of understanding the operation and handling of such sophisticated technology. In this paper, we propose a system which is cheaper and easy to operate with multiple application. The proposed system uses a technology which utilizes machine learning and ANN algorithms using UAV that helps us to locate regions that are affected by diseases and pesticides so that we can particularly focus on the regions that are affected and apply chemicals only in that particular area, with the entire system being cost effective. For this purpose, we divide the entire area into n x n segments and using image processing, the segmented areas are analysed and processed using Ardupilot and a central operating system to monitor using python and open CV, giving the simplest user interface for monitoring purposes.
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Citations
Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review
Arif Muhammad,Alghamdi Khloud K,Sahel Salma A,Alosaimi Samar O,Alsahaft Mashael E,Alharthi Maram A,Arif Maryam +6 more
- 15 Feb 2021
TL;DR: In this paper, the authors summarize recent trends in the forest fire events prediction, detection, spread rate, and mapping of the burned areas, aiming to summarize the recent trends of forest fire occurrence, its spread, and its impact on the environment.
49
Applying deep neural networks to predict incidence and phenology of plant pests and diseases
Marc Grünig,Elisabeth Razavi,Pierluigi Calanca,Dominique Mazzi,Jan Dirk Wegner,Jan Dirk Wegner,Loïc Pellissier +6 more
TL;DR: In this article, a framework for the development of deep neural networks for pest and pathogen damage classification was presented, which showed their potential for predicting the phenology of damages and showed good performance with degree-day models.
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Application of Unmanned Aerial Vehicles (UAVs) for Pest Surveillance, Monitoring and Management
N. V. Maslekar,Kiran P. Kulkarni,Akshay Kumar Chakravarthy +2 more
- 01 Jan 2020
TL;DR: UAVs attached with cameras can resolve many issues in crop protection that conventional pest management tools cannot, and Automated pest damage in cultivated tracts using UAVs has been realized.
13
Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City
TL;DR: In this article , a genetic programming (GP) approach was used for the automatic design of an algorithm to detect mistletoe using multispectral aerial images, which achieved an overall accuracy of 96% and a value of fitness function based on weighted Cohen's Kappa (kw) equal to 0.45 in the test data set.
Design of a highly efficient crop damage detection ensemble learning model using deep convolutional networks
Akshay Dhande,Rahul Malik +1 more
TL;DR: A deep convolutional network (DCN) design that integrates both near-field and far-field images in order to perform effective correlation allows the system to predict crop-damages with higher efficiency than individual models.
7
References
Agricultural sustainability: concepts, principles and evidence
TL;DR: Agricultural sustainability suggests a focus on both genotype improvements through the full range of modern biological approaches and improved understanding of the benefits of ecological and agronomic management, manipulation and redesign.
Plant Disease Detection Using Image Processing
Sachin D. Khirade,A.B. Patil +1 more
- 26 Feb 2015
TL;DR: The methods used for the detection of plant diseases using their leaves images are discussed and some segmentation and feature extraction algorithm used in the plant disease detection are discussed.
709
Fast and Accurate Detection and Classification of Plant Diseases
TL;DR: The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases and can achieve 20% speedup over the approach proposed in [1].
Detection of unhealthy region of plant leaves using image processing and genetic algorithm
Vijai Singh,Varsha,Anoop Misra +2 more
- 19 Mar 2015
TL;DR: An algorithm for image segmentation technique used for automatic detection as well as classification of plant leaf diseases and survey on different diseases classification techniques that can be used for plant leaf disease detection are presented.
208
The influence of drone monitoring on crop health and harvest size
Marthinus Reinecke,Tania Prinsloo +1 more
- 19 Jul 2017
TL;DR: In this article, the benefits of drones in agriculture, and their limitations, are discussed, and the authors recommend that more farmers invest in drone technology to better their agricultural outputs by using specific cameras to detect pests and water shortages.
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