Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

Reactive oxygen species (ROS, partial reduction or derivatives of free radicals) are highly reactive, dangerous and can cause oxidative cell death. In addition to their role as toxic by-products of aerobic metabolism, ROS play a role in the control and regulation of biological processes such as growth, the cell cycle, programmed cell death, hormone signaling, biotic and abiotic stress reactions and development. ROS always arise in plants as a by-product of several metabolic processes that are located in different cell compartments, or as a result of the inevitable escape of electrons to oxygen from the electron transport activities of chloroplasts, mitochondria and plasma membranes. These reactive species are formed in chloroplasts, mitochondria, plasma membranes, peroxisomes, apoplasts, the endoplasmic reticulum and cell walls. The action of many non-enzymatic and enzymatic antioxidants present in tissues is required for efficient scavenging of ROS generated during various environmental stressors. The current review provides an in-depth look at the fate of ROS in plants, a beneficial role in managing stress and other irregularities. The production sites are also explained with their negative effects. In addition, the biochemical properties and sources of ROS generation, capture systems, the influence of ROS on cell biochemistry and the crosstalk of ROS with other signaling molecules/pathways are discussed.

Although trace elements are essential for life, environmental contamination due to metal accumulation and overuse in various sectors, such as healthcare, agriculture, industry, and cosmetics, poses significant health concerns. Exposure of plants to heavy metals leads to the overproduction of reactive oxygen species (ROS) due to their ability to change mitochondrial membrane permeability and restrict the action of ROS clearance enzymes in the cellular antioxidant system. The interaction of ROS with cellular membranes, heavy-metal-induced interactions directly or indirectly with different macromolecules, and signaling pathways leads to the accumulation of environmental pollutants and oxidative stress in exposed organisms. The heavy metal–ROS–cell signaling axis affects various pathological processes such as ATP depletion, excess ROS production, mitochondrial respiratory chain damage, decoupling of oxidative phosphorylation, and mitochondrial death. This review focuses on discussing the toxic effects of different heavy metals on plants, with particular emphasis on oxidative stress, its consequences, and mitigation strategies.

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

Plants are vulnerable to many environmental constraints which include drought, salinity, extreme temperatures, heavy metals, etc., thereby posing damage to the development and yield of major crops. Of these stresses, drought represents a severe environmental threat to plant productivity in agriculture. Drought stress reduces the yield by affecting the key plant metabolic pathways. The capability of plants to switch on or off a series of genes result in alterations of the physiological and morphological attributes, thereby allowing plants to escape, tolerate or avoid drought stress. Different genes, transcription factors and signal transduction pathways are induced by drought stress. Advances in genome editing technologies have revolutionized the agriculture sector by using more accurate genome engineering techniques for targeted crop traits. Clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) technology is a robust and efficient approach for developing plant/crop varieties tolerant to different climatic changes. The use of new genome editing technologies like CRISPR facilitates the creation of plant species with improved drought tolerance. Thus this review aims to elucidate the basic plant responses to stress and the mechanisms for the adaptation and attainment of tolerance.