Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

Nanotechnology is a research area that has experienced tremendous development given the enormous potential of nanoparticles (NPs) to influence almost all industries and conventional processes. NPs have been extensively used in agriculture to improve plant physiology, production, and nutritional values of plant-based products. The large surface area and small size are some of the desired attributes for NPs that can substantially ameliorate plants' physiological processes, thereby improving crop production. Nevertheless, the results derived from such research have not always been positive as NPs have been shown, in some cases, to negatively affect plants due to their potentially toxic nature. These toxic effects depend upon the size, concentration, nature, zeta potential, and shape of nanoparticles, as well as the used plant species. The most common response of plants under NPs toxicity is the activation of antioxidant systems and the production of secondary metabolites. The mitigation of such NPs-induced stress highly varies depending on the amount of NPs applied to the plant growth stage and the environmental conditions. On the contrary, higher photosynthetic rates, higher chlorophyll, and proline content, improved homeostasis, hormonal balance, and nutrient assimilation are the favorable physiological changes after NPs applications. Alternatively, NPs do not always exhibit positive or negative impacts on plants, and no physiological influences are sometimes observed. Considering such diversity of responses after the use of NPs on plants, this review summarizes the progress made in nanotechnology on the influence of different NPs in plant physiology through the use of indexes like seed germination, root and shoot morphology, photosynthesis, and their impact when used as carriers of cell signaling molecules such as nitric oxide (NO). Understanding the intimate dynamics of nanoparticle toxicity in plants can prove to be fruitful for the development of areas like agronomy, horticulture, plant pathology, plant physiology, etc. That, in return, can assist to ensure agricultural sustainability. Similarly, this may also help to pave the way to combat the drastic climate change and satisfy growing food demands for the ever-increasing world population. Further studies on molecular and genetic levels can certainly broaden the current understanding of NPs-plant interactions and devise the respective mitigation strategies for environmental safety.

Enhanced crop growth and yield are the recurring concerns in agricultural field, considering the soaring world population and climate change. Abiotic stresses are one of the major limiting factors for constraining crop production, for several economically important horticultural crops, and contribute to almost 70% of yield gap. Salt stress is one of these unsought abiotic stresses that has become a consistent problem in agriculture over the past few years. Salinity further induces ionic, osmotic, and oxidative stress that result in various metabolic perturbations (including the generation of reactive oxygen, carbonyl, and nitrogen species), reduction in water potential (ψ_w), distorted membrane potential, membrane injury, altered rates of photosynthesis, leaf senescence, and reduced nitrogen assimilation, among others); thereby provoking a drastic reduction in crop growth and yield. One of the strategies to mitigate salt stress is the use of natural plant extracts (PEs) instead of chemical fertilizers, thus limiting water, soil, and environmental pollution. PEs mainly consist of seeds, roots, shoots, fruits, flowers, and leaves concentrates employed either individually or in mixtures. Since PEs are usually rich in bioactive compounds (e.g., carotenoids, flavonoids, phenolics, etc.), therefore they are effective in regulating redox metabolism, thereby promoting plant growth and yield. However, various factors like plant growth stage, doses applied, application method, soil, and environmental conditions may greatly influence their impact on plants. PEs have been reported to enhance salt tolerance in plants primarily through modulation of signaling signatures and pathways (e.g., Na⁺, ANNA4, GIPC, SOS3, and SCaBP8 Ca²⁺ sensors, etc.), and regulation of redox machinery [e.g., superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), non-specific peroxidase (POX), glutathione peroxidase (GPX), peroxiredoxin (Prx), ascorbic acid (AsA), glutathione (GSH), α-tocopherol, etc.]. The current study highlights the role of PEs in terms of their sources, methods of preparation, and mode of action with subsequent physiological changes induced in plants against salinity. However, an explicit mode of action of PEs remains nebulous, which might be explicated utilizing transcriptomics, proteomics, metabolomics, and bioinformatics approaches. Being ecological and economical, PEs might pave the way for ensuring the food security in this challenging era of climate change.

The current COVID-19 global pandemic has amplified the pressure on the agriculture sector, inciting the need for sustainable agriculture more than ever. Thus, in this review, a sustainable perspective of the use of remotely piloted aircraft (RPA) or drone technology in the agriculture sector is discussed. Similarly, the types of cameras (multispectral, thermal, and visible), sensors, software, and platforms frequently deployed for ensuring precision agriculture for crop monitoring, disease detection, or even yield estimation are briefly discoursed. In this regard, vegetation indices (VIs) embrace an imperative prominence as they provide vital information for crop monitoring and decision-making, thus a summary of most commonly used VIs is also furnished and serves as a guide while planning to collect specific crop data. Furthermore, the establishment of significant applications of RPAs in livestock, forestry, crop monitoring, disease surveillance, irrigation, soil analysis, fertilization, crop harvest, weed management, mechanical pollination, crop insurance and tree plantation are cited in the light of currently available literature in this domain. RPA technology efficiency, cost and limitations are also considered based on the previous studies that may help to devise policies, technology adoption, investment, and research activities in this sphere.

Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R² of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.