Environmental fluctuations have a strong influence on soil, plant, water, air, and flora and fauna, and have a strongassociation and interaction with them. As a result, crop yield is adversely affected by both biotic and abiotic stresses. Therefore, effective crop production requires early and accurate identification of biotic and abiotic stresses. Traditional methods for detecting various stresses are laborious and may result in imprecise management. Recently, appreciable results have been achieved in the early detection of plant stresses in crops using non-invasive, high-resolution optical sensors that can cope with problems associated with traditional methods. Remote sensing and proximal sensing techniques have been shown to provide better and more precise results in the detection of crop stresses through differences in spectral lines reflected from the surface of plants. In addition, different biotic and abiotic stresses occurring due to high and freezing temperatures can be detected easily with remote sensing and proximal sensing techniques. Both remote sensing and proximal sensing techniques help in the detection of various changes caused by alterations in physical, chemical, and biological environments using satellites, manned aircraft, and unmanned aerial vehicles. However, the increasing data size requires advanced data storage and processing techniques such as cloud computing and machine learning. Thus, development of reliable, user-friendly, and cost-effective sensing machines may result in broader adoption of remote sensing and proximal sensing techniques in early detection of plant stress symptoms.
