Agriculture Assistant for Crop Prediction and Farming Selection Using Machine Learning Model with Real-Time Data Using Imaging Through UAV Drone

The article deals with the issue of economic efficiency of the use of drones in agricultural production. There is an opinion about their inefficiency, which is refuted by the study. The purpose of the study is to determine the effectiveness of the use of agricultural drones (using the example of U-30L-6 (BROUAV) in comparison with other technological options. The use of agricultural drones allows not only to reduce the cost of manufactured products, but also to increase crop yields by reducing losses during cultivation, as the number of passes of wheeled vehicles across the field during the growing season is reduced. Among the options considered (trailed sprayer, self-propelled sprayer, agrodrone), the use of copters took the second place in terms of production costs. But due to a decrease in the spraying rate and losses from trampling, the economic effect of using agricultural drones is the highest (3417.34 rubles/ha), which is more than twice as high as when using a self-propelled sprayer.

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“…The sprayer introduces chemicals pointwise. The consumption of expensive drugs, as well as fertilizers, is reduced by 25-35% [20][21][22][23].…”

Section: Resultsmentioning

confidence: 99%

Efficiency of using drones in agricultural production

et al. 2023

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“…The roll movement is influenced by the speed of the right part (1,2) and left part (3,4) motors. Pitch movement is affected by the speed of the front part (1,4) and rear part (2,3) motors. Yaw movement is influenced by combination of motors 1,3 and 2,4.…”

Section: Methodology 21 Quadcopter Modelmentioning

confidence: 99%

“…A drone can do much work. In agriculture, drones are used as sprinklers of liquid fertilizer and observation of crops from the air [1]. Drones are used as spies from the air in the military field [2].…”

Section: Introductionmentioning

confidence: 99%

A Vision and GPS Based System for Autonomous Precision Vertical Landing of UAV Quadcopter

Priambodo

Arifin²,

Nasuha³

et al. 2022

J. Phys.: Conf. Ser.

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This research aims to develop an algorithm for the precision vertical landing process on a quadcopter using GPS sensors and computer vision. This research consists of 2 main parts: GPS-based control used by the quadcopter to return to the home area and computer vision-based control in the form of ArUco marker detection, which is used by the quadcopter in order to land precisely. Detection of the ArUco marker that is used goes through 2 stages: the application of threshold and ratio and the process of calculating the black and white area, which can then be searched for four corners of the marker. The algorithm designed works well on the Webots simulation program with mid-computer specifications. The ArUco marker detection process is fast and reliable, so it does not overload the processor unit so that the simulation process can run well. From 30 tests with different initial positions, the average error of the landing position of the quadcopter at coordinate X is 0.02 meters, and coordinates Y is 0.03 meters. All test results show that the quadcopter can land inside the landing pad area without any parts coming out.

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“…Another important application of ML techniques is to detect the change from the normal scenarios. Images are captured through satellites or drones and then ML techniques are applied to predict the behavior or change [55]. SVM and GA are combined to detect the change.…”

Section: Machine Learningmentioning

confidence: 99%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

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Agriculture Assistant for Crop Prediction and Farming Selection Using Machine Learning Model with Real-Time Data Using Imaging Through UAV Drone

Cited by 7 publications

References 48 publications

Efficiency of using drones in agricultural production

Efficiency of using drones in agricultural production

A Vision and GPS Based System for Autonomous Precision Vertical Landing of UAV Quadcopter

Remote Sensing Image Classification: A Comprehensive Review and Applications

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