Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1

Applications of Emerging Smart Technologies in Farming Systems: A Review © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 58 ( ) Year 2023 G slope and surface texture for a site-specific application. Rather than using expensive grid sampling of soil chemical and physical attributes, fuzzy cluster analysis highlighted the possibility of readily available spatial yield or soil to establish management zones for applying nitrification inhibitors. Finally, in precision agriculture, ( Ortiz et al. 2011 ) used fuzzy clustering of elevation and slope of the terrain to delineate root-knot nematode (RKN) risk zones for a comparison test of two nematicide application rates on nematode population density and cotton lint yield. ii. Artificial Neural Networks in Agriculture Artificial neural network (ANN) has emerged as a new technology that offers a variety of solutions to complicated challenges in agricultural research. Since it can address a wide range of issues that a linear system cannot. Modern agriculture requires a high level of production efficiency as well as a high level of product quality in agricultural and livestock production. ( Russian Federation: Gazprom Neft 2020, Sustainable Development Report ).ANN tools are extensively employed in a variety of classification and prediction tasks. Their applications vary from crop quality classification to disease and pest verification, predicting the impacts on production based on multiple independent parameters and intelligent weed management. ANN techniques improve agricultural decision-making processes, assist in optimizing storage and transportation procedures, and enable the forecasting of expenses. Machine learning methods in the "life cycle of a farm" requires handling large amounts of data collected during the growing season and having the appropriate software to analyze it. The evident growth of digital agriculture and precision farming leads many farms to switch to ANN intelligence-based products. ( AbderahmanRejeb et al. 2022 ) iii. Genetic Algorithm Applications Genetic algorithms are similar to natural systems, created through gene reproduction, cross- over, and mutation to provide better environmental adaptation. It works on the principle of pheromones that are substances which are secreted to the outside by an individual and received by a second individual of the same species. A complex structure could be built using its more fundamental constituents. (LiuLingxiao 2022 ). Genetic algorithms determine the best cropping pattern while considering various constraints and complexities using search and optimization technique. Genetic algorithms have also been used to assist with modelling and Prediction. Genetic algorithm approach can address the issues in Crop management, water management, food quality and safety, food processing, precision agriculture, agricultural biology, agricultural machinery, agricultural facilities, animal behavior, and forecasting agricultural commodity prices. The standard genetic algorithm's steps can be summarised as follows. 1. The population of individuals is initialized. This can be done by randomly generating a definite number of people represented by fixed-length character strings. The following stages (2–4) are repeated until the halting requirement is reached. 2. Every individual in the population has a chance of experiencing a mutation. In other words, the given individual can be randomly modified 3. In some unexpected way, the (possibly modified) individuals split and interchange these splits in pairs, creating new individuals (cross-over). As a result of Steps 2 and 3, the population modifies. 4. The fitness of each individual in the newly obtained population is assessed. As a result, only a subset of all individuals is advanced to the next stage. (i.e., Step 2) or, if some individuals obtain satisfactory fitness. The procedure is stopped. In this way, a solution—or an approximate solution—to the problem can be found,