Sorghum Yield Prediction using Machine Learning

被引：7

作者：

Zannou, Judicael Geraud N. ^{[1
]}

Houndji, Vinasetan Ratheil ^{[1
]}

机构：

[1] UAC, Abomey Calavi, Benin

来源：

2019 3RD INTERNATIONAL CONFERENCE ON BIO-ENGINEERING FOR SMART TECHNOLOGIES (BIOSMART) | 2019年

关键词：

Estimate farm yields; Machine Learning; Africa; Agriculture; TensorFlow; Sorghum;

D O I：

10.1109/biosmart.2019.8734219

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Estimation of a future agricultural production is an important challenge for farmers. In this paper, we propose a system based on machine learning algorithms to estimate farm yields. The experiments were conducted on a Sorghum field. We use TensorFlow with Convolutional Neural Networks and Linear Regression. These algorithms allow us 1) to detect the different ears of Sorghum on an image and 2) to estimate their weight. On our dataset, we obtain an average accuracy of 74,5% for the detection of sorghum and an average precision of 99% for the estimation of the weight.

引用

页数：4

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