Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …
Machine learning in agriculture: A comprehensive updated review
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …
artificial intelligent systems for the sake of making value from the ever-increasing data …
Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches
The art of predicting crop production is done before the crop is harvested. Crop output
forecasts will help people make timely judgments concerning food policy, prices in markets …
forecasts will help people make timely judgments concerning food policy, prices in markets …
[HTML][HTML] Crop yield prediction using machine learning: A systematic literature review
Abstract Machine learning is an important decision support tool for crop yield prediction,
including supporting decisions on what crops to grow and what to do during the growing …
including supporting decisions on what crops to grow and what to do during the growing …
Machine learning for smart agriculture and precision farming: towards making the fields talk
In almost every sector, data-driven business, the digitization of the data has generated a
data tsunami. In addition, man-to-machine digital data handling has magnified the …
data tsunami. In addition, man-to-machine digital data handling has magnified the …
Predicting and map** of soil organic carbon using machine learning algorithms in Northern Iran
Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding
the chemical, physical, and biological functions of the soil. This study proposes machine …
the chemical, physical, and biological functions of the soil. This study proposes machine …
[HTML][HTML] Integrated phenology and climate in rice yields prediction using machine learning methods
Rice (Oryza sativa L.) is a staple cereal crop and its demand is substantially increasing with
the growth of the global population. Precisely predicting rice yields are of vital importance to …
the growth of the global population. Precisely predicting rice yields are of vital importance to …
Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach
As the world's leading corn producer, the United States supplies more than 30% of the
global corn production. Accurate and timely estimation of corn yield is therefore essential for …
global corn production. Accurate and timely estimation of corn yield is therefore essential for …
Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning
Alfalfa is a valuable and intensively produced forage crop in the United States, and the
timely estimation of its yield can inform precision management decisions. However …
timely estimation of its yield can inform precision management decisions. However …
Remote-sensing data and deep-learning techniques in crop map** and yield prediction: A systematic review
Reliable and timely crop-yield prediction and crop map** are crucial for food security and
decision making in the food industry and in agro-environmental management. The global …
decision making in the food industry and in agro-environmental management. The global …