Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network

7 Pages Posted: 25 Nov 2020 Last revised: 7 Jan 2021

See all articles by Manoj Athreya C S

Manoj Athreya C S

Independent

Mohith Gowda H R

PES College of Engineering

Amogh Babu K A

Independent

Ravishankar R

Adichunchanagiri Institute of Technology

Date Written: November 20, 2020

Abstract

Agriculture stands as the backbone of our Nation, by Contributing 7% of the total Indian Economy. The drivers of agriculture are facing a huge problem in predicting the yield in different varieties of soil. Currently, the use of sophisticated technologies in the field of agriculture is underdeveloped when compared to other sectors over the past few decades. Self-mortality rates of farmers considerably increasing from the past four-five years. This is mainly due to the debt overhead usually caused by low yield. Crops yield decline considerably due to unpredictable weather, environmental changes, and diseases. This can say that agricultural landowners fear to use new technologies and tend to follow the age-old tradition of farming. The sphere of computing with its rigorous learning capabilities is inevitable to find a novel solution for agriculture-related issues. In this paper, this issue is addressed and have come up with an improvised idea to help farmers get a better yield for their crops. Deep learning has been used to predict the yield. This technology is made handy for every farmer to learn and use it effectively via a simple IoT device installed in their fields and a smartphone application. This improvised system has been named as Agriculture Yield Monitor System (AYMS). This has proved to be more efficient and beneficial to farmers.

Keywords: Internet of Things (IoT), machine learning, artificial intelligence (AI), smart forming, deep feedforward neural network(DFF), distance monitoring

Suggested Citation

C S, Manoj Athreya and H R, Mohith Gowda and K A, Amogh Babu and R, Ravishankar, Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network (November 20, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3734201 or http://dx.doi.org/10.2139/ssrn.3734201

Manoj Athreya C S (Contact Author)

Independent ( email )

Bangalore
India

Mohith Gowda H R

PES College of Engineering ( email )

India

Amogh Babu K A

Independent ( email )

Bangalore
India

HOME PAGE: https;//www.amoghbabuka.in

Ravishankar R

Adichunchanagiri Institute of Technology ( email )

Chikkamagaluru
India

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