Modernizing Agricultural Infrastructure with Machine Learning-Based Remote Sensing Techniques

Abstract

The growing value of renewable agricultural techniques is extended straight to powerful water management in cropping systems. The integration of remote sensing technology with machine learning algorithms is a highly innovative approach for monitoring in real time crop water stress which is indispensable towards achieving maximized water use and crop security. This study is focused on remote sensing data interaction with sophisticated machine learning methods in abutting the crop water stress problem, and, therefore, performing an effective water resources management in the field of civil engineering. Remote sensors provide unique tools that are non-invasive and can evaluate important processes by observing the same area continuously. Day-to-day imagery, mounted constellations of nanosatellites are now fitted with multispectral and thermally sensitive sensors gathering data on indices such as Normalised Difference Vegetation Index (NDVI), soil moisture content of the roots, crop canopy temperature, and many other vital biophysical parameters. These features are of crucial importance in taking of decisions on the water need of the plants. Computational intelligence automates the process, by playing such role, it analyzes and derives meaning from sensory information acquired by remote sensing. Learning a supervised method of machine learning with models using historically observed crop stress level, remote sensing indications and existing data can help in identifying patterns and relations between both indications. Statistical methods of advanced level, i.e. regression analysis, decision trees, random forests, and neural networks are employed to forcibly evaluate water stress. Through remote sensing and machine learning the agricultural drougts evaluation in civil engineering may gain wisdom of numerous feasible options. Firstly, it enables proper and quick decision-making especially in water utilisation where people save water and there is no wastage thus encouraging sustainable water resource utilisation. Precision irrigation permits the crops to get the optimum water amount and as a result, both production and quality are enhanced. Besides, it helps preventing the negative action on the environment that can associated with the irrigation. Also, the use of these innovative approaches contributes to farm monitoring on a large scale, which in developing countries with few sites where there are general equipment to monitor farms becomes more relevant. Our research activity in the project includes remote sensing data series, including Landsat and Sentinel missions satellite images, as well as uncommissioned aerial imagery from drones (UAVs). Before having analysis, the data verifies with the atmospheric distortions rectification, and then the related vegetation indices are to be computed. The dataset consists of accurate data that include measurements of soil moisture and reports on crop health. These become the stage of training and validating the machine learning models. From forecasting agricultural water stress different machine learning methods are introduced to accomplish this target. Vegetation indices and soil moisture levels are regressed via regression models while decision trees and random forests are the classification algorithms optimal for classifying hurdled crops. The neural networks which are being investigating this potential benefit from their capability to handle the complex and non-linear interactions, such as those seen in medical diagnosis. The result of the study deals with the usefulness of merging remote sensing information and machine learning techniques for mapping water stress in croplands. The validation models show a quite accuracy level in detecting crops that are under stress and conveying useful details of periods and locations where water stress is taking a place. These insights about water management is significant for civil engineers and agricultural managers to make policies that save anymore water. Lastly, the study highlighted how the scaling up of this approach could be possible in future. A wide range of covering the state’s agricultural area is successfully accomplished with the aid of remote sensing techniques which, in turn, are an optimal basis for the management of water resources on the regional or even the national scale. Once the machine learning models have been built, they will smoothly be able to contain fresh data so that the system becomes adaptive reacting to shifts in the surroundings with no problems. In the given context, the merger of satellite imagery and machine learning provides a formidable tool to do water stress assessment in the discipline of civil engineering. What is more, it helps to enhance general tendency towards rational water management not only in agriculture but even in other economic sectors as well too. Following that, future studies will be related to strengthening machine learning algorithms by, for example, merging auxiliary data sources as well as developing solutions for combining this technology with irrigation systems for narrowing the gap to achieve the optimum water management in agriculture. This study gives clear evidence of superiority of multidisciplinary methodologies that blend civil engineering concepts with technology of latest standards in solving the most important problems of the area of agricultural water management. As a result, farmers will be able to fully practice sustainable farming adopted to environmental change, the two of which provide an undeniable food security.