An Intelligent Agriculture Application Based on Deep Learning

Chen, Mu-Yen; Wu, Hsin-Te; Chiu, Wen-Yu

doi:10.1109/icsse.2018.8520209

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Making a distinct platform for farmers helps them to share some information about agriculture. [1]Agriculture's contribution to a country's economic development is made up of both the expansion of items produced within the sector itself and the ability of other sectors to flourish thanks to the export of commodities to both domestic and foreign markets. [2]According to their analysis, it is conceivable to provide the people with food that has value added while also guaranteeing that farmers receive fair rates for their produce by constructing an efficient and effective supply chain utilizing cutting-edge procedures.…”

Section: Literature Surveymentioning

confidence: 99%

Agricultural Land E-Commerce Shopping Application

2023

IRJMETS

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In this project, we present an e-commerce application (Agricultural Land E-Commerce Shopping Application) for farming products. This project Aim to outline the design and implementation of an e-commerce application for farmer. In today's day to day life purchasing of agricultural products by visiting stores has become a troublesome job. As a standing in a queue long time for making the payment of the shopping product and agricultural product one station to another station travelling has become difficult for customers. The most important advantage of e-commerce over traditional commerce is that the buyer can browse online shops over internet, can compare rates and order whatever they like sitting on their mobile application and PC. Unlike traditional commerce which is carried out physically which consists man power and get products, electronic commerce has made it far easier for person to reduce physical work and to save time. To reduce these difficulties of traditional commerce, we are coming up with Agricultural E-Commerce Shopping Application, an e-commerce application which helps to choose a wide range of farming products through your smartphone rather than visiting the outlets. The application will also integrate with popular payment gateways such as PayPal and internet banking, providing customers with a range of payment options sales. Safe payment gateway with a good logistic delivery partner, A farmer can do profitable business from his/her home itself. Both time and energy are also saved.

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Section: Literature Surveymentioning

confidence: 99%

Agricultural Land E-Commerce Shopping Application

2023

IRJMETS

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“…These deep learning methods have demonstrated superior performance in handling large-scale datasets but training these models requires a significant amount of annotated data. Additionally, visual recognition is susceptible to various environmental interferences such as lighting conditions, visibility, and blurring during platform movement, leading to weak generalization ability [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

ODN-Pro: An Improved Model Based on YOLOv8 for Enhanced Instance Detection in Orchard Point Clouds

Pan,

Xiao,

et al. 2024

Agronomy

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In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps.

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