Document Type : Original Article
Authors
1
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt
2
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
3
Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt
4
School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
5
Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, 11566, Cairo, Egypt
6
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
7
Department of Financial and Accounting Management Programs, Applied College, Princess Nora bint Abdul Rahman University, Saudi Arabia
Abstract
Agricultural systems are very complicated mechanisms of connections between plants, animals, and the biochemical processes in a way that they provide the main source of crop production, ecosystem stability, and environmental sustainability. Soil is the foundation on which farming rests, with plants growing efficiently and ecosystems functioning. Therefore, there is a need for the assessment of soil quality so as to ensure that agriculture is fruitful, stable, and sustainable. Soil gastric enzymes operate as key catalysts in chemical reactions and nutrient cycling, organic matter decay process, and soil fertility. This survey discusses the two main enzymes, amylase and urease, that play an essential role in nutrient absorption by breaking down starch and improving the nitrogen cycle. Soil physicochemical properties, land use, and weather conditions provide stability of the enzyme activity. Regression analysis techniques like multiple linear regression (MLR) and random forest (RF) machine learning classifiers use large amounts of data to explore enzymatic activity in the soil and investigate its associations with soil properties and management practices. Regression analysis additionally descends over soil enzymology to crop yield forecasting, greenhouse gas emissions accounting, and environmental degradation evaluation, among other things.
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