Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

The grade of irrigation water available to irrigators has a significant impact on crops as well as yields. Therefore, it is a need to better understand irrigation water quality. The present study mainly focuses on the assessment of the suitability of water of forty-four fixed bore wells of Kanchipuram district, Tamil Nadu, India. The groundwater sample datasets of post-monsoon (2005–2013) and pre-monsoon (2006–2013) season were collected for 9 years. Water quality indices, namely sodium adsorption ratio, exchangeable sodium percent (SSP or %Na), residual sodium carbonate (RSC or RA), Kelly’s ratio, permeability index, chloroalkaline indices (CAI1 and CAI2), potential salinity (PS), magnesium hazard, total dissolved solids and total hardness, have been calculated for separate bore wells. The r₁ and r₂ indices show that groundwater of the study area is Na⁺–SO₄²⁻ and deep meteoric percolation type. Majority of the wells are fall under moderate to unsuitable category of water for irrigation purposes. Further, wells water has also been classified on the base of meteoric genesis index.

The present study includes a systematic analysis of sediment contamination by heavy metals of the River Ghaghara flowing through the Uttar Pradesh and Bihar in Indian Territory. To estimate the geochemical environment of the river, seven heavy metals, namely Co, Cu, Cr, Ni, Cd, Zn, and Pb were examined from the freshly deposited river bed sediment. All the sediment samples were collected on a seasonal basis for the assessment of fluctuation in 2014–2015 and after preparation samples were analyzed using standard procedure. Result showed that heavy metal concentration ranged between 11.37 and 18.42 mg/kg for Co, 2.76 and 11.74 mg/kg for Cu, 61.25 and 87.68 mg/kg for Cr, 15.29 and 25.59 mg/kg for Ni, 0.21 and 0.28 mg/kg for Cd, 13.26 and 17.59 mg/kg for Zn, 10.71 and 14.26 mg/kg for Pb in different season. Metal contamination factor indicates the anthropogenic input in the river sediment was in the range of (0.62–0.97) for Co, (0.04–0.26) for Cu, (0.68–0.97) for Cr, (0.22–0.38) for Ni, (0.70–0.93) for Cd, (0.14–0.19) for Zn, and (0.54–0.71) for Pb. The highest contamination degree of the sediment was noticed as 4.01 at Ayodhya and lowest as 3.16 at Katerniaghat. Geo-accumulation index was noted between (0 and 1) which showed that sediment was uncontaminated to moderately contaminated and may have adverse affects on freshwater ecology of the river. Pollution load index (PLI) was found highest at Chhapra which was 0.45 and lowest at Katerniaghat which was 0.35 and it indicates that the river sediment has a low level of contamination. Significant high correlation was observed between Co, Cu, and Zn, it suggests same source of contamination input is mainly due to human settlement and agriculture activity. Positive correlation between Zn, Co, Cu, Cr, and Ni indicated a natural origin of these elements in the river sediment. Cluster analysis suggests grouping of similar polluted sites. The strong similarity between Co, Zn, Pb, Ni, Cu, and Cd showed relationship of these metals come from the same origin, which is possibly from natural and anthropogenic input which was also confirmed by correlation analysis. Using the various pollution indicators it was found that the river bed sediment is less contaminated by toxic metals during the study but the sediment quality may degrade in the near future due to increasing anthropogenic inputs in the river basin, hence proper management strategies are required to control the direct dumping of wastewater in the river.