Soil Spectroscopy Evolution: A Review of Homemade Sensors, Benchtop Systems, and Mobile Instruments Coupled with Machine Learning Algorithms in Soil Diagnosis for Precision Agriculture.

In precision agriculture, soil spectroscopy has become an invaluable tool for rapid, low-cost, and nondestructive diagnostic approaches. Various instrument configurations are utilized to obtain spectral data over a range of wavelengths, such as homemade sensors, benchtop systems, and mobile instruments. These data are then modeled using a variety of calibration algorithms, including Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machines (SVM), these datasets are further improved and optimized.