Rice Farming Age Detection Use Drone Based on SVM Histogram Image Classification

Marsujitullah,; Zainuddin, Zainal Abidin; Manjang, Salama; Wijaya, Aksan Surya

doi:10.1088/1742-6596/1198/9/092001

Cited by 23 publications

(11 citation statements)

References 4 publications

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“…Classification of Paddy crop age detection is done through Ariel Photograph Drone Devices using Support Vector Machine and histogram methods [5]. Rice crop monitoring using multirotor UAV and RGBdigitalcamera [6]and Rice Farming Age Detection Using Drone based SVM Histogram Image Classification [7]rice crop monitoring was done using SVM and AgisoftPhotoscan software.…”

Section: Literature Surveymentioning

confidence: 99%

Identification of Low Growing Zone in Agricultural Land

Jaiswal¹,

Datta²,

Ochawar³

2022

Int.J.Res.Publ.Rev.

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The objective of our research is to identify the low growing zone in agricultural land. The agricultural land under study is of the citrus food productive land. In this work, we have collected the image dataset of an orange farm, corresponding to the various stages of its growth and representing plant growth in the form of the height of the plants. On this image dataset, various image processing algorithms are applied. Otsu’s thresholding and segmentation are carried out. Further, on the resulting images, the masking technique is applied and finally, the low growing zone is identified in agricultural land. The experimental images show that Otsu’s Method is effective in segmenting the images and identifying the low growing region.

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Section: Literature Surveymentioning

confidence: 99%

Identification of Low Growing Zone in Agricultural Land

Jaiswal¹,

Datta²,

Ochawar³

2022

Int.J.Res.Publ.Rev.

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“…In terms of its role, maps are a form of presenting spatial (spatial) information about the earth's surface to be used in decision making. While mapping is a form of graphical communication between map makers and users that has long been known to people [1,5].…”

Section: Maps and Mappingmentioning

confidence: 99%

“…Geographical Information System or often referred to as Geospatial Information System is an information system used to compile, store, revise and analyze data and attributes that refer to the location or position of objects on earth [1] This means that data or information that refers to the location or position of objects on earth is termed spatial data or information, while attributes describe the characteristics of the spatial data. More specifically, the components of spatial data include geographic position / location, attribute data, spatial relationships and time.…”

Section: Geographicial Information System -Gismentioning

confidence: 99%

“…The majority of Indonesians work as farmers, for this reason the role of the agricultural sector is one of the supporters in economic development that is able to provide a solution for the wider Indonesian community, especially in Merauke Regency [1]. Merauke Regency is an agricultural district located in Papua Province, which is also directly adjacent to Papua New Guine State, and is one of the national food storage areas engaged in the agricultural sector [2].…”

Section: Introductionmentioning

confidence: 99%

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Geographical Information System for Mapping and Analysis of Agricultural Areas in Merauke Regency

et al. 2021

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This Geographical Information System Mapping and Analysis Study aims to map and analyze the range of potential seed plant returns in several sub-districts in Merauke, which consists of several criteria for structural land use and land use intensity. Building Management, Circulation and Connecting Systems, Open Space and Green Systems, Quality Environmental Management, Environmental Infrastructure and Utilities Systems and Building and Environmental Conservation. This research was made using Simple Additive Weighting (SAW) which allows the search for optimal alternative solutions and the latest technological developments related to a method and presentation of information. The final result of this research is that web-based GIS is expected to be a source of reference / reference that is easily accessed by stakeholders related to agriculture, providing an overview of the various potentials of farming, especially sub-districts that have potential. availability of suitable land, provides an overview of the quantity of agricultural land use that has been utilized, and can be used as a guideline for policy development in the field of superior agriculture at the regional and national levels.

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“…DL network has a more complex and deeper structure that could learn the feature automatically inside the network layer, to accomplish improved crop detection. During crop classification and recognition, few crops are hard to determine because of low resolution of crop mapping, similar spectral features, and salt and pepper noise that have a serious effect on the performance of crop recognition and classification [8]. With the ongoing progress of RS technologies, it can able to perform a large number of crop classification studies by using RS images [9].…”

Section: Introductionmentioning

confidence: 99%

Satin Bowerbird Optimization With Convolutional LSTM for Food Crop Classification on UAV Imagery

2023

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Food crop classification and identification are crucial aspects of modern agriculture. With progression of drones or unmanned aerial vehicles (UAVs), crop detection from RGB images goes through a paradigm shift from traditional image processing methods to deep learning (DL) methods due to effective breakthroughs in convolutional neural networks (CNN). Drone images are reliable for identifying different crops because of its higher spatial resolution. Food crop classification utilizing deep learning on drone images includes machine learning techniques for distinguishing and identifying different types of crops in images captured by UAVs. It is beneficial for various applications, like crop monitoring and precision agriculture. This paper presents a new Satin Bowerbird Optimization with deep learning for Food Crop Classification (SBODL-FCC) technique on UAV images. The presented SBODL-FCC technique exploits DL models with hyperparameter optimizers for food crop classification on UAV images. To accomplish this, the presented SBODL-FCC technique employs adaptive bilateral filtering technique for image preprocessing. Besides, the SBODL-FCC technique uses MobileNetv2 feature extractor with Bayesian optimization (BO) algorithm for parameter optimization. Moreover, the food crop classification process is performed through convolutional long short-term memory (ConvLSTM) model. Furthermore, the hyperparameter tuning of the ConvLSTM method is accomplished through SBO algorithm. The experimental validation of the SBODL-FCC technique is validated on UAV image database and the results are inspected under different aspects. The simulation outcomes inferred that the SBODL-FCC technique reaches better performance over other models in terms of several performance measures.

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Rice Farming Age Detection Use Drone Based on SVM Histogram Image Classification

Cited by 23 publications

References 4 publications

Identification of Low Growing Zone in Agricultural Land

Identification of Low Growing Zone in Agricultural Land

Geographical Information System for Mapping and Analysis of Agricultural Areas in Merauke Regency

Satin Bowerbird Optimization With Convolutional LSTM for Food Crop Classification on UAV Imagery

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