The rapid innovations and liberalized market economy in agriculture demand accuracy in Crop Yield Prediction (CYP). In accurate prediction, machine learning (ML) algorithms and the selected features play a major role. The performance of any ML algorithm may improve with the utilization of a distinct set of features in the same training dataset. This research work evaluates the most needed features for accurate CYP. The ML algorithms, namely, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbour and Random Forest (RF) are proposed for better accuracy. Agricultural dataset consists of 745 instances; 70% of data are randomly selected and are used to train the model and 30% are used for testing the model to assess the predictive ability. The results show that the RF algorithm reaches the highest accuracy by means of its error analysis values for all the distinct feature subsets using the same training agricultural data. © 2019, © 2019 Taylor &amp; Francis.

Renta Chintala Bhargavi

Computer Science

School of Computer Science and Engineering

Chennai Campus

Maya Gopal P.S

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms

Applied Artificial Intelligence

Crop yield prediction is one of the challenging task in agricultural domain. Extensive research in agricultural domain has been carried out to predict better crop yield using the machine learning algorithm Artificial Neural Network (ANN) and statistical model Multiple Linear Regression (MLR). This article examines the intrinsic relationship between MLR and ANN. A hybrid MLR-ANN model has been proposed in this research work for efficient crop yield prediction. The proposed hybrid model is modeled to analyze the prediction accuracy when MLR intercept and coefficients were applied to initialize the ANN's input layer weights and bias. Feed Forward Artificial Neural Network with Back Propagation training algorithm was used for predicting accurate paddy crop yield. In conventional ANN model, the weights and bias of input and hidden layer are initilized randomly. This hybrid MLR-ANN model, instead of random weights and bias initialization, the input layer weights and bias are initialized by using MLR's coefficients and bias. The hybrid model prediction accuracy is compared with ANN, MLR, Support Vector Regression (SVR), k-Nearest Neighbour (KNN) and Random Forest (RF) models by using performance metrics. The computational time for both hybrid MLR-ANN and conventional ANN was calculated. The results show that the proposed hybrid MLR-ANN model gives better accuracy than the conventional models. © 2019

Computers and Electronics in Agriculture

A novel approach for efficient crop yield prediction

Agricultural and Forest Meteorology

Seasonal climate forecasts provide more definitive and accurate crop yield predictions

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

Agricultural Systems

Accounting for long-term soil fertility effects when assessing the climate impact of crop cultivation

Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms

Trends in wheat yields under representative climate futures: Implications for climate adaptation

Journal	Applied Artificial Intelligence
Publisher	Informa UK Limited
ISSN	0883-9514
Open Access	0