Crop Yield Prediction Using Deep Neural Network

Agriculture has made it’s way to make every living being healthy and survive in this world, for which the environment affecting has been taken into consideration. The parameters that have impacted on the crops significant yield water, ultraviolet (UV), pesticides, fertilizer, and the area of the land covered for the region is referenced. In this paper, a machine learning model proposed illustrated the use of neural network and the concerned algorithm artificial neural network (ANN) has been evaluated. The dataset has been taken of 140 data points depicting the attributes effect on the yield of the crops. The error rate with the actual has been shown with the assist of Mean Square Error (MSE) and the standard deviation between the yield results with the actual was also shown, which came out to be 0.0045 for the MSE, that’s around and 0.000345 as the standard deviation.

[1]  Divye Gala,et al.  Smart Farming System: Crop Yield Prediction Using Regression Techniques , 2018 .

[2]  Maryam Rahnemoonfar,et al.  Deep Count: Fruit Counting Based on Deep Simulated Learning , 2017, Sensors.

[3]  Kai Heinrich,et al.  Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting , 2019, Wirtschaftsinformatik.

[4]  M. P. Singh,et al.  Crop Selection Method to maximize crop yield rate using machine learning technique , 2015, 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM).

[5]  Stefano Ermon,et al.  Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data , 2018, COMPASS.

[6]  MengChu Zhou,et al.  An Efficient Cooperative Medium Access Control Protocol for Wireless IoT networks in Smart World System , 2019, J. Netw. Comput. Appl..

[7]  Kumar Yelamarthi,et al.  Crop Yield Analysis Using Machine Learning Algorithms , 2020, 2020 IEEE 6th World Forum on Internet of Things (WF-IoT).

[8]  John W. Sheppard,et al.  Using Deep Learning in Yield and Protein Prediction of Winter Wheat Based on Fertilization Prescriptions in Precision Agriculture , 2018 .

[9]  Ahmed Khattab,et al.  Design and implementation of a cloud-based IoT scheme for precision agriculture , 2016, 2016 28th International Conference on Microelectronics (ICM).

[10]  Ming Sun,et al.  A Survey on Deep Learning in Crop Planting , 2019, IOP Conference Series: Materials Science and Engineering.

[11]  Yan Li,et al.  Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition , 2018, Geoderma.

[12]  Sarika Jain,et al.  Quality Assessment of Crops using Machine Learning Techniques , 2019, 2019 Amity International Conference on Artificial Intelligence (AICAI).

[13]  Yulia R. Gel,et al.  Deep Learning for Improved Agricultural Risk Management , 2019, HICSS.

[14]  Xanthoula Eirini Pantazi,et al.  Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..

[15]  Young K. Chang,et al.  Current and future applications of statistical machine learning algorithms for agricultural machine vision systems , 2019, Comput. Electron. Agric..

[16]  R. Priya,et al.  Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression , 2019, Environment, Development and Sustainability.

[17]  Patrizia Busato,et al.  Machine Learning in Agriculture: A Review , 2018, Sensors.

[18]  S. R. Juhi Reshma,et al.  Impact of Machine Learning and Internet of Things in Agriculture: State of the Art , 2016, SoCPaR.

[19]  R. Linker Machine learning based analysis of night-time images for yield prediction in apple orchard , 2018 .

[20]  Bram van Ginneken,et al.  Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning , 2017, Radiological Physics and Technology.

[21]  Niketa Gandhi,et al.  Rice crop yield prediction using artificial neural networks , 2016, 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR).

[22]  Salah Sukkarieh,et al.  Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..

[23]  João Paulo Papa,et al.  Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic , 2018, Comput. Electron. Agric..

[24]  K. Yelamarthi,et al.  Implementation of a Modular IoT Framework with Scalability and Efficient Routing Md , 2016 .