The document discusses several applications of machine learning in agriculture, including using image analysis of satellite and drone pictures to monitor soil and crop health, implementing precision drip irrigation based on soil moisture and other sensor data, applying targeted herbicide through automated weed detection, enhancing crop yields by optimizing water, fertilizer and pesticide applications based on historical analytics, and precisely predicting harvest yields from images and past data to gain advantages in commodity markets.