Commonly used algorithms for crop yield prediction include regression, decision trees, and neural networks. The algorithm can be trained using a subset of the data and evaluated using another subset ...

Support Vector Machine and K-Nearest Neighbor- techniques have shown outstanding results - are implemented to predict the crop yield using remote sensing data. Authors focused on distinguish parts of ...

Then, they tasked the neural network with predicting yield. “We developed methodology using deep learning to generate yield predictions,” said Nicolas Martin, co-author of the research and an ...

Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive ...

Scientists have trained an AI algorithm to count the number of flowers on fruit trees using only smartphone images. The system could predict the ... to estimate their yields, which can have ...

and classifying the data using algorithms such as K-Nearest Neighbour, Naive Bayes, Decision Trees/Random Forest, Support Vector Machine, and Logistic Regression. The proposed system employs a vast ...

Support Vector Machine and K-Nearest Neighbor- techniques have shown outstanding results - are implemented to predict the crop yield using remote sensing data. Authors focused on distinguish parts of ...

These algorithms have improved the capacity ... to analyse the capability of ML methods for yield prediction in sunflower and wheat using a synthetic dataset obtained with crop simulation models and b ...