Using a handheld GreenSeeker sensor to time and size nitrogen doses in mustard raised seed yield 22.75% while using 18.7% less fertilizer than standard practice.
Slow-release fertilizer coatings can cut nutrient waste and boost yields, but plastic coatings shed microplastics and release timing still fails under real field conditions.
Fusing near-daily 3 m satellite images with sparse 0.05 m drone photos produced daily 0.5 m crop maps, with the ESTARFM algorithm plus CACAO smoothing performing best.
No single sensor or algorithm can estimate orchard yield alone — ground cameras count fruit best, while drones and spectral sensors map yield across regions.
Drone-based crop yield prediction works, but its accuracy is limited less by fancy models than by mismatched, poor-quality ground-truth yield data and untested transfer across years and regions.
Drone LiDAR tracking of maize height over time predicts tasseling within about 2.6 days, replacing subjective manual field scouting.
A network of field sensors feeding live soil and weather data into predictive algorithms helped farmers raise crop yields and cut wasted water and fertiliser.
Drones, sensors, IoT and machine learning can raise crop yields while cutting waste, but cost, data management and slow farmer adoption still hold precision agriculture back.
A survey of machine learning in farming finds it already predicts yields, spots diseases and weeds, and monitors livestock, soil and water from sensor data.
Machine learning now underpins smart farming — predicting soil properties, crop yields, diseases, weeds, livestock health and automating irrigation and harvesting.
Combining farm sensor data with big-data and AI methods can make crop yield forecasts far more useful than traditional statistical models alone.