Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective.

Textile dyes especially azo dyes are the prime pollutants in wastewater due to the presence of complex azo bond (−N=N−). Many conventional methods such as physicochemical, mechanical and biochemical were employed recently for removal of these organic pollutants. These methods have not been proved to be so efficient. Photocatalysis, a latest physio-chemical methods is employed currently. Present work investigated the synthesis of a Carbon Nano Tubes (CNT’s) doped ZnO/Fe2O3 catalyst by microwave-assisted sol-gel method. Removal efficiency of the synthesized catalyst was observed through removal of two azo dyes direct orange-26 and acid red-151 for different concentrations. The prepared ternary nano-hybrid was characterized by X-ray diffractometry (XRD), scanning electron microscopy (SEM) and Energy Dispersive Spectroscopy (EDS). The average crystalline size of the catalyst was calculated as 8.47nm by using data from XRD analysis. SEM images revealed the formation o f nano-flakes of catalyst. And results of EDS investigation publicized that the distribution of all the atoms of Zn, Fe, O and C is homogeneous throughout the catalyst. Percentage removal by synthesized catalyst for direct orange-26 and acid red-151 was determined as 58.8% and 72.7% respectively without adding oxidant. After adding oxidant the percentage removal increases to 70.6% for direct orange-26 and 85.2% for acid red-151. The effect of pH, time and oxidant dose were optimized by developing relationships between these variables using central composite design (CCD) in response surface methodology (RSM). The results of RSM analysis show prepared catalyst performs best in acidic conditions at pH of 3 and irradiation time of 2.7 hours for acid red-151 and at pH of 5 and irradiation time of 3.8 hours for direct orange-26.