AI is employed in crop monitoring to assess plant health, detect diseases, and optimize agricultural practices. It utilizes machine learning algorithms, remote sensing, and image analysis to analyze crop imagery, provide real-time insights, and aid in decision-making for efficient crop management and yield optimization.
Chinese researchers introduce a groundbreaking deep inverse convolutional neural network approach tailored for land cover remote sensing images. This novel method effectively addresses data imbalance, significantly improving classification accuracy and precision, with potential applications in urban planning, agriculture, and environmental monitoring.
The Crop Planting Density Optimization System (CPDOS) harnesses the power of artificial intelligence, including genetic algorithms and neural networks, to optimize crop planting density for improved agricultural yields. This intelligent online system offers advanced tools for farmers to fine-tune planting density and fertilizer application, ultimately enhancing crop production while considering economic factors.
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