Geoinformatics, Precision Farming, and nanotechnology are examples of climate-smart, sustainable agriculture practices that utilize modern tools and techniques to optimize agricultural production, reduce input costs, and minimize environmental impact. These practices advocate for the precise application of inputs at the appropriate time and place, considering soil and crop variability. The development of spectral and hyperspectral sensors, as well as high-resolution images obtained through satellite technology, has accelerated information generation and collection in these areas.
Innovations in nanotechnology, such as nanoscale biosensors and nanoparticles, have also shown promise in enhancing agricultural productivity by improving the use of fertilizers, pesticides, and other inputs. The goal of these practices is to achieve sustainable agriculture under declining resources.
The book covers all the above to cater to the needs of any beginner in the above fields
India’s agrifood industry will have a difficult task in 2050 to feed nearly 1.7 billion people on a limited amount of arable land, water, and energy. At 140 million hectares, our net sown area has all but stagnated, and there is little chance that it will rise. The process of intensifying agriculture has resulted in the degradation of natural resources, specifically soil and water. Our current input application strategy ignores intrafield variability and is based on standard recommendations for a composite sample or visual symptoms of crops. Since the mean values used to make recommendations are rarely observed in a particular field, it indicates either excessive or suboptimal application of inputs.
Introduction A geographical information system (GIS) is a kind of database that has software tools for handling, analyzing, and presenting spatial data and descriptions of phenomena that have a location component. This system can also involve human users and support staff, procedures and workflows, a body of knowledge about relevant concepts and methods, and institutional organizations in a wider sense. GIS technology is a key part of spatial data infrastructure, which the White House describes as “the policies, technologies, standards, human resources, and related activities that are necessary to acquire, process, distribute, use, maintain, and preserve spatial data.”
Introduction Global Positioning System (GPS) is a system of satellites and ground stations that provide location and time information for any place on earth, regardless of the weather. GPS is a type of GNSS, which stands for global navigation satellite systems. GPS has many applications in precision farming, such as farm planning, field mapping, soil sampling, tractor guidance, crop guidance, variable- rate applications (VRAs) and yield mapping. GPS helps farmers work in low- visibility conditions such as rain, dust, fog, and darkness. India has its own GPS system called IRNSS or NavIC, which uses about seven satellites. GPS is owned and operated by the U.S. government, specifically the U.S.
Introduction We use our five senses to perceive the world around us. Some senses, like touch and taste, need contact with the objects. But we get most of our information about the environment through sight and hearing, which do not need close contact with the external objects. In scientific terms, we are doing remote- sensing all the time. Remote- sensing is the process of recording/observing/ perceiving (sensing) objects or events from a distance. Remote- sensing is a way of monitoring and detecting the features, usually physical, of a specific area by measuring its reflected and emitted radiation (usually from satellite or aircraft).
Introduction Spectral signature: A spectral signature is the unique pattern of reflection and emission of a target at different wavelengths. This difference helps to classify remote sensing images based on the spectral signatures of different objects. Electromagnetic (EM) spectrum: The EM spectrum is the range of all types of EM radiation. Some examples are visible light from a lamp, radio waves from a radio station, microwaves, infrared light, ultraviolet light, X-rays, and gamma rays. Spectral response patterns: A spectral response pattern is the amount of energy that an object reflects or emits at different wavelengths. It shows how the object interacts with the EM radiation.
Introduction A thematic layer is defined as a layer with a set of logical feature data placed under single theme such as soil type, pH level, salinity, and water table. The features in these layers determine real world objects and are created using geometries such as points, lines and polygons (Tickoo 2017). Thematic layering is the spatial representation of analyzed data of elements that belong to the same type. Thematic maps are used to create these layers, which show how one or more specific data themes vary across different geographic areas.
Introduction Crop discrimination is the process of using remote-sensing technologies to identify and characterize different types of crops based on their electromagnetic behavior at different wavelengths. Each crop has a unique spectral signature that reflects its response to the incident radiation. However, the spectral signature can also vary depending on the season, the angle of the sensor, crop’s features, illumination intensity, weather phenomenon, and topography among other external factors (Parida et al., 2023).
Introduction Agriculture is a vital sector in the world economy, as it helps to reduce poverty and promote sustainable development, especially in developing countries. However, agriculture also faces challenges such as increasing productivity and reducing environmental impacts. Therefore, new technologies and methods are needed to improve agricultural practices. One of these methods is precision agriculture (PA), which is based on the principle of sustainable agriculture and healthy food production. PA is a science that uses high technology sensors and analysis tools to improve crop yields and support management decisions. PA is a new concept that aims to increase production, reduce labor time, and optimize the management of fertilizers and irrigation processes.
Introduction Site-specific nutrient management (SSNM) is the dynamic, field-specific management of nutrients in a particular cropping season to optimize the supply and demand of nutrients according to their differences in cycling throughsoil–plantsystems(Sarkar etal.,2017).Thisnewmethodofnutrient recommendationsisprimarilybasedontheinitialnutrientstatus(INS)from thesoil andthecrop’sneedfornutrients toachieve thedesiredyield.
Introduction To address the escalating food demands of the global population, a transformation in the agricultural sector is imperative (Velten et al., 2015; Lampridi et al., 2019). The current overdependence on finite resources is leading to their overexploitation, largely due to irresponsible usage (Velten et al., 2015; Lampridi et al., 2019). By adopting a site-specific approach, farmers can apply inputs only where they are necessary in the field (Velten et al., 2015; Lampridi et al., 2019). This method can enhance output, decrease labor costs, and mitigate the environmental damage caused by excessive input use (Velten et al., 2015; Lampridi et al., 2019).
Introduction In order to cater to the nutritional needs of a rapidly growing population, it is imperative to increase food grain production on the same land while maintaining the soil’s productive capacity. This necessitates comprehensive research to establish a scientific foundation for enhancing and sustaining food production and soil productivity with minimal environmental impact (Rathore et al., 2010). Balanced nutrition extends beyond the application of nitrogen, phosphorous, and potassium in specific quantities through fertilizers. It entails ensuring that the soil contains the right proportions of available nutrients to meet the crop’s requirements for optimal yield (Rathore et al., 2010).
Introduction Precision farming, also known as precision agriculture, prescription farming, and site-specific farming, is grounded on the principle of managing fields based on their heterogeneity (Nowak 2021). In the past, crop management decisions were made for the entire field, treating it as a homogenous unit, despite significant variations in soil characteristics, weed infestations, and plant growth within a field (Nowak 2021). Precision agriculture integrates modern technology with past experience to manage farming on a grid-by-grid basis (Nowak 2021).
Introduction Nanotechnology is a scientific and technological field that manipulates matter at the nanoscale to create, design, characterize, and use structures, systems, and devices with novel properties (Sattler 2016). Nanoparticles are one of the most important products of nanotechnology, as they exhibit different features from their bulk counterparts due to their size and shape (Sabir et al., 2014). Nanoparticles have applications in various disciplines, such as materials science, medicine, agriculture, physics, and chemistry.
Introduction Nanofertilizers are products of nanotechnology that are created or modified from conventional fertilizers, bulk fertilizer materials, or plant extracts. They use different methods such as chemical, physical, mechanical, or biological processes to produce nanosized particles that can enhance soil fertility, crop productivity, and quality (KhetiGaadi 2022). Nanofertilizers have a size range of 0.1–100 nm, which is similar to the size of plant cell wall (20 nm), making them easier to be absorbed by plants (Tiwari 2022).
Introduction Nanopesticides are pesticides that use nanomaterials or nanotechnology to improve their effectiveness and safety in agriculture. Nanomaterials are materials that have at least one dimension between 1 and 200 nm. Nanomaterials have different properties than their bulk counterparts, such as higher surface area, reactivity, and mobility. Nanopesticides can be formulated in various ways, such as nanosized active ingredients, molecular active ingredient nanocarrier complexes, or active ingredients encapsulated or coated by nanomaterials (Deka et al., 2021; Xu et al., 2022).
Introduction Crop models can be broadly classified into two categories: dynamic or process-based crop simulation models (CSMs), and reduced form statistical models. Since simulation models are based on various plant-related processes, they develop or alter in tandem with plant growth (Chetty 2009); as a result, they are inherently nonlinear (Olmstead 2009). To run, calibrate, and validate, they require a significant amount of date input. However, statistical models are helpful in situations where there is a lack of data because they describe the relationships between a subset of variables while keeping the others constant. (Chetty 2009).
Augmentation 19, 20 Bottom-up 139, 146, 151 Characterization 51, 133, 134 Crop classification 63, 67 Crop discrimination 61 Dynamic light scattering 135, 136 Electromagnetic (EM) spectrum 43 Geographical Information System 1, 11, 16, 17, 108 Augmentation 19, 20 Bottom-up 139, 146, 151 Characterization 51, 133, 134 Crop classification 63, 67 Crop discrimination 61 Dynamic light scattering 135, 136 Electromagnetic (EM) spectrum 43 Geographical Information System 1, 11, 16, 17, 108
