Suryawanshi et al., 2021 - Google Patents

Aerial imagery for plant disease detection by using machine learning of typical crops in marathwada

Suryawanshi et al., 2021

Document ID
4578794881894350677
Author
Suryawanshi A
Khurjekar M
Publication year
Publication venue
2021 International Conference on Computing, Communication and Green Engineering (CCGE)

External Links

Snippet

Agriculture plays an important role by contributing to the economy of India. 75% of the population has agriculture as their major occupation and only source of income. There are various parts in the process of production where we need to pay more attention to the higher …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
Chen et al. An AIoT based smart agricultural system for pests detection
EP3571629B1 (en) Adaptive cyber-physical system for efficient monitoring of unstructured environments
Peña et al. Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution
Hamidisepehr et al. Comparison of object detection methods for corn damage assessment using deep learning
EP3998856A1 (en) Method for generating an application map for treating a field with an agricultural equipment
Balyan et al. Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture
Wang et al. Mushroom-YOLO: A deep learning algorithm for mushroom growth recognition based on improved YOLOv5 in agriculture 4.0
Manoharan et al. Identification of mango leaf disease using deep learning
Refaai et al. [Retracted] Application of IoT‐Based Drones in Precision Agriculture for Pest Control
Chhillar et al. Survey of plant disease detection using image classification techniques
Wang et al. Development and application of an intelligent plant protection monitoring system
Suryawanshi et al. Aerial imagery for plant disease detection by using machine learning of typical crops in marathwada
Čirjak et al. Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks
Wang et al. Diagnosis of soybean bacterial blight progress stage based on deep learning in the context of data-deficient
Mndela et al. Irrigation scheduling for small-scale crops based on crop water content patterns derived from UAV multispectral imagery
Araneta et al. Controlled Environment for Spinach Cultured Plant with Health Analysis using Machine Learning
WO2023242236A1 (en) Synthetic generation of training data
CN110555343B (en) Method and system for extracting three elements of forest, shrub and grass in typical resource elements
Khuwaja et al. Sustainable Agriculture: An IoT-Based Solution for Early Disease Detection in Greenhouses
Qiang et al. Pest disease detection of Brassica chinensis in wide scenes via machine vision: method and deployment
Yang et al. GEE-Based monitoring method of key management nodes in cotton production
Meyer et al. For5g: Systematic approach for creating digital twins of cherry orchards
Wanninayake et al. IoT-Enabled Smart Solution for Rice Disease Detection, Yield Prediction, and Remediation
Chinnasamy et al. Crop optimization and disease detection using satellite imagery & artificial intelligence
Malini et al. Convergence of Internet of things, machine learning, blockchain, big data, cloud, 5G for building the ecosystem for cyber-physical agricultural systems