Agricultural internet of things using machine learning

Bhanu, J. Sasi; Bigul, Sunitha Devi; Prakash, Arun

doi:10.1063/5.0058012

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Recent papers show DNN algorithms are quite similar to ANN algorithms only varying in number of hidden layers [18]. CNN models showed improved accuracy in classification and LSTM neural network is most widely used model which shows great accuracy for regression analysis [19]. Time series data is analyzed using LSTM and gated recurrent unit (GRU).…”

Section: Literature Surveymentioning

confidence: 99%

Optimized Farming: Crop Recommendation System Using Predictive Analytics

2023

IJIES

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Agriculture is been majorly contributing to the country's GDP. So, it is foremost important to improve the production quality and quantity to improve the agricultural economy. Using modern technologies like IoT, big data, blockchain, robotics, 5G technologies, etc., farming can be improved by replacing humans in agricultural operations. The proposed work focuses on forecasting of yield and prices of agro-products which will be useful for farmers to increase their productivity and which in-turn increase their economy. Data science techniques can be used to perform this by predicting which crop can be grown for given environmental features and what is the outcome before harvesting period. The proposed model uses computational data-driven approach for crop yield prediction and price forecasting of agro-products. The study uses hybrid approach for crop yield prediction which integrates LSTM and genetic algorithm based evolutionary algorithm known as enhanced long short-term memory (ELSTM). The weight assignment plays a major role in learning the behaviour of the network. The gradient value with respect to weight is calculated for each epoch, and weight is updated to compute the new weight of the network to minimize error. The model accurately predicts the crop yield for the dataset which comprises of features like soil data, rainfall, history of production, fertilizers etc., the accuracy of the model is measured using metric root mean square error (RMSE) and mean absolute error (MAE). In the proposed study, the computation model is used to enhance the knowledge about agricultural yield before the crop sowing period. The proposed model will help farmers and government agencies to improve production. The proposed ELSTM model is compared with other machine learning models such as naïve bayes, decision tree, which shows accuracy between 75% to 80% and the experimental result show the proposed ELSTM model is performing better with 85% accuracy. The price forecasting model is necessary to identify the commodity prices well in advance. The agro-product price forecasting is made using auto-regressive integrated moving average (ARIMA) which uses dataset collected from various agricultural produce market committee (APMC). Auto correlation function (ACF) and partial auto correlation function (PACF) time series plots have been used to assess the proposed model's performance in predicting prices of crops.

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Section: Literature Surveymentioning

confidence: 99%

Optimized Farming: Crop Recommendation System Using Predictive Analytics

2023

IJIES

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An Architecture on Drome Agriculture IoT Using Machine Learning

Sasi Bhanu,

Kamesh,

Durga Bhavani

et al. 2023

Cognitive Science and Technology

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Machine Learning-Enabled Internet of Things Solution for Smart Agriculture Operations

Kuppusamy

Suresh

Shanmugananthan

2023

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Internet of things solutions with machine learning capabilities is a hot research area in industries, including agriculture. They can be used for data analysis and further forecasting the big data and intelligent applications in farming. In traditional farming, the main obstacles are disease prediction, automatic irrigation, energy harvesting, and constant monitoring. Today, farmers' cultivation of their crops has changed by introducing automated harvesters, drones, autonomous tractors, sowing, and weeding. Smart farming with ML-enabled IoT systems can improve crop harvesting decisions. The main topic of this chapter is to provide an ML-enabled IoT solution for smart agriculture. The MIoT solution in agriculture allows farmers to use predictive analytics to help them make better harvesting decisions. Designing a MIoT system for smart agriculture can assist farmers in improving yields, planning more effective irrigation, and making harvest forecasts by monitoring essential data like humidity, air temperature, and soil quality via remote sensors.

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Agricultural internet of things using machine learning

Cited by 3 publications

References 18 publications

Optimized Farming: Crop Recommendation System Using Predictive Analytics

Optimized Farming: Crop Recommendation System Using Predictive Analytics

An Architecture on Drome Agriculture IoT Using Machine Learning

Machine Learning-Enabled Internet of Things Solution for Smart Agriculture Operations

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