This research developed an efficient predictive model for crop yield by creating ensembles of several ML algorithms, including extreme gradient boosting (XGBoost), decision tree regressor (DT), K-nearest neighbour regressor (KNN), and random forest regressor (RF). Historical crop yield data from ...
SVM is suitable for yield prediction using weather and MODIS-based vegetation indices [41]. ANNs are often used for crop production prediction models [42]. Machine learning predictors for classification include PSO-SVM, KNN, and RF [43]. Conventional machine learning methods require pre-processing...
Crop maturity is a kind of crop yield prediction, but it is based on image data. This technique has been used in fruit detection, like apples, tomatoes, oranges, etc, and provides an early estimation of yield. It is also used for crop monitoring to provide information to farmers with the...
Pandey Amit, Jain Achin (2017) Comparative analysis of knn algorithm using various normalization techniques. I. J. Comp Netw and Info Secu 11:36–42 MATH Google Scholar Van Klompenburg T, Kassahun A, Catal C (2020) Crop yield prediction using machine learning: s systematic literature review...
Prediction of crop yield using phenological information extracted from remote sensing vegetation index. Sensors. 2021;4:1406. https://doi.org/10.3390/s21041406. 61. Jiang Q, Wang QFZ. Study on delineation of irrigation management zones based on management zone analyst ...
The artificial intelligence module 173 may apply techniques including, but not limited to, k-nearest neighbor (KNN), logistic regression, support vector machines or networks (SVM), and one or more neural networks. The use of artificial intelligence in the harvest advisory model 100 of the ...
(2021) Normalized red, green, blue, RF, SVM, KNN Using an inexpensive RGB color camera can produce good accuracy. A manual labeling process with coloring in the Region of Interest (ROI). Y. Li et al. (2021) HSI spectrograph, thresholding, superpixels, SVM, partial least squares-...
Hence prediction plays a major role to find out the demand of crop production for maximizing the yield. For that in this paper we propose a prediction method for the major crops of Tamilnadu using K-means and Modified K Nearest Neighbor (KNN). Matlab and WEKA are used as the tool for ...
The final prediction is the mean of the individual tree predictions. This technique is applicable in R and requires two main parameters: the number of randomly selected splitting variables at each tree (mtry), and the number of trees (ntree). We used the default parameters provided by the ...
Procedure to build the ML system for GS prediction The procedure of constructing an ML system for a specific task involves multiple steps to ensure an objective assessment of the system. For model selection, five basal methods, including SVR, ANN, KNN, RF, and GB, were compared with rrBLUP ...