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Heavy metal pollution is a significant source of pollution in the environment. Heavy metal contamination in aquifers endangers public health and the freshwater and marine ecosystems. Traditional wastewater treatment methods are mainly expensive, ecologically damaging, ineffective, and take much time. Phyto-remediation is a plant-based technique that gained popularity by discovering heavy metal accumulating plants that can accumulate, transport, and consolidate enormous quantities of certain hazardous contaminants. This is a low-cost sustainable evolving technique featuring long-term utility. Several terrestrial and aquatic vegetation have now been examined for their ability to repair polluted soils and streams. Several submerged plants have already been discovered to remove harmful pollutants such as Zn, As, Cu, Cd, Cr, Pb & Hg. The most important part of effective phyto-remediation is selecting and choosing effective plant species. Aquatic macrophytes have high effectiveness for removing chemical contaminates. Watercress, hydrilla, alligator weed, pennywort, duckweed plants, water hyacinth are examples of aquatic macrophytes. Several macrophytes' metal absorption capability and procedures have now been explored or analyzed. Most of these research demonstrated that macrophytes had bioremediation capability. The bioremediation capability of macrophytes can be increased even more by employing novel bioremediation techniques. To demonstrate the extensive application of phyto-remediation, a comprehensive summary assessment of the usage of macrophytes for phyto-remediation is compiled.

Artificial Intelligence (AI) can revolutionize agriculture which impacts a country’s economy, employing more than 30% of the world’s population directly or indirectly. It can fulfill the needs of an ever-growing world’s population through automation. Traditional farmland practices like weeding, pesticide spraying, irrigation, monitoring soil nutritional and moisture status, etc. can be performed quicker using robots, sensors, drones, and algorithms. It reduces water wastage and pesticide overuse, maintains soil fertility, helps in reducing labor and enhances crop yield and productivity despite world problems. However, its penetration into agriculture is still in its infancy due to its uneconomical nature, lack of expertise and big data requirement for accuracy among others. This paper delves deeper into the various applications and impacts of AI in agriculture, new tools being used, challenges and future scope related to this field. Combined with Artificial Neural Network (ANN) models and Machine Learning (ML), along with Expert systems (ES) and Internet of Things (IoT), AI can do wonders in agriculture in the subsequent years to come.

Because of India's large population, the fish supply demand is increasing daily. Among all fishes, the Indian major carp (Labeo rohita, Catla catla, etc.) is one of the most demanded fishes in India. Indian major carps breed once a year during the early monsoon season (June and July). At that time, the collection of spawns from natural sources is insufficient for our population. To overcome this situation, induced breeding is the most effective process to increase carp culture. IMC bred twice in a season using this method. The first step in this process is collecting and preserving pituitary glands from donor fish. This gland-pituitary extract is later produced and injected into spawn to facilitate spawning. So, in our survey, we know how many pituitary glands are collected daily as well as weekly averages from the survey site and where they are transferred.

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