Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring.

Intelligent agriculture can achieve information perception, quantitative decision-making, and intelligent control throughout agricultural production by integrating information technologies such as the Internet of Things, big data, artificial intelligence, and intelligent equipment with agriculture. Jiang et al. [[12]] proposed an SMC estimation approach for mixed soil types based on PCA and machine learning, with hyperspectral data as the input. The machine learning methods include conventional machine learning techniques such as KNN, RF, SVM, and ANN, and deep learning techniques such as LSTM, VGG, YOLO, and SSD. Wang et al. [[4]] developed an information extraction method for the accurate determination of the spatial distribution of crops by integrating spatiotemporal image information using a fractal model. [Extracted from the article]