Soil Health Management Using Artificial Intelligence for Smart Agriculture Systems

1. Introduction:

The ever-increasing human population demands higher agricultural production and crop yields. The growing crop and its corresponding yield are directly influenced by the soil, irrigation and climatic conditions that serves as the basis for agricultural production. The world’s human population is expected to grow from 8.1 billion (Year-2024) currently to 9.1 billion by the end of 050 requiring 3 billion tons of grain to feed the human population, which is around 50% more food production requirement [1]. For this to achieve we will need to establish solid grounds for the vegetation to grow, that being grounds, being the soil, thus the study for soil health is important. Soil health management is one of the most important aspects for agriculture and food production. Thus, preserving and enhancing soil health is an important factor for supporting agriculture. Soil management involves assessing and adjusting soil nutrient levels, improving soil structure, texture, monitoring and managing carbon footprints of the soil. Maintaining an equilibrium of nutrients and essentials in the soil can help both agricultural crops and the ecosystem. The breakdown of Smart Agriculture Systems could be based over (i) Soil Health monitoring equipment’s (ii) AI for monitoring the data through these systems. The study begins with an introduction to AI – Artificial Intelligence.

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines. These processes include learning (the acquisition of information and rules for using the information), reasoning (using heuristic or rules to reach nearly or definite accuracy in task), and self-correction mechanism (through reward- reinforcement learning, or statistical methods- Machine Learning). Other fields that being sub-domain for AI includes (but not limited to) deep learning, transfer learning, Image processing, computer vision, and robotics. These approaches enable AI systems to analyze Big Data, derive insights from them, and make autonomous or semi- autonomous decisions without explicit programming for every possible scenario. While various solutions, such as database decision support systems, have been proposed for agricultural issues, artificial intelligence (AI) systems have proven to be the most accurate and reliable [2]. AI has several real-world applications across a range of industries, including agriculture too. Various Software developments have been successfully deployed over this theme of soil health monitoring, one such being Trace Genomics.

The knack of soil to continue functioning as a living ecosystem that nurtures humans, animals, and plants is known as soil health. Soil health management involves practices and strategies aimed at maintaining and improving the quality and fertility of soil for sustainable agricultural production and environmental conservation. Furthermore, soil health management links agriculture and soil research to policy, stakeholder demands, and sustainable supply-chain management [3]. While crop productivity was the primary focus of soil assessments in the past, soil health now encompasses the function that soil plays in water quality, climate change, and human health.

• Principles to Manage Soil for Health:

Research on soil health has provided guidance on managing soil to enhance soil function.

a.

Maximize Presence of Living Roots

b.

Minimize Disturbance

c.

Maximize Soil Cover

d.

Maximize Biodiversity

Figure 1. Principles of soil health [4].

Figure 1. Principles of soil health [4].

Maintaining our soil’s health and productivity is crucial given the growing global population and increasing needs for food production. An increasing number of farmers are enhancing microbial activity and increasing the organic matter content of their soil through the application of soil health concepts and methods, such as cover crops, no-till farming, and diversified rotation. Consequently, farmers are reaping greater earnings and frequently higher yields while also enhancing water infiltration, improving wildlife and pollinator habitat, and sequestering more carbon.

Effective soil health management requires a holistic and integrated approach, combining traditional knowledge with modern scientific techniques to achieve sustainable and productive agricultural systems. The key components for the effective soil health management are: soil testing and monitoring, organic matter management, crop rotation and diversity, conservation tillage, nutrient management, water management, erosion control, soil biota enhancement, pollution prevention, education and training.

Regular soil testing and monitoring are crucial for determining nutrient level, pH, and other parameters, enabling informed decision-making regarding balanced fertilization to avoid nutrient imbalances and environmental pollution. Soil health is majorly determined by the soil structure, organic matter content, nutrient cycling, which can be enhanced by following practices like composting, diversified planting which enhances the soil structure, lower the incidence of soil- borne diseases, and sustain a diversified microbial community, addition of microbial inoculants, organic amendments such as compost, manure, or green manures improves the nutrient availability and increases the soil and plant health. The contamination of soil and water can be minimized by reducing chemical usage and adopting integrated pest management (IPM) strategies, the pest and disease cycle can be disrupted by following practices like, crop rotation, which also improves soil structure and increases nutrient availability in the same area over the course of several seasons. The soil erosion can be prevented by following cover cropping where the crop residues protect the soil surface and increases the nutrient availability. To minimizing the soil disturbance reduced or no tillage is practiced which in turn helps to maintain soil structure, reduce erosion, and increase water infiltration and retention [5]. Mulching protects the soil surface, reduce evaporation, and maintain moderate soil temperature. To optimize fertilizer application, reduce waste and erosion, efficient irrigation methods like drip or sprinkler systems, water conservation practices such as rainwater harvesting, contour farming, and building terraces, contour farming, terracing and precision agriculture techniques are followed. Windbreaks to reduce wind speed and protect soil from wind erosion. Farmer education, research and development to support research, provide training and resources on innovative soil management techniques and their practical applications.

Ameliorated soil health minimizes the risk of erosion, runoff, and pollution, protecting water bodies and the ecosystem, producing higher and more sustainable yields. Soil health management also helps to tackle climatic changes by sequestering more carbon with the increase of organic matter content of the soil. Healthy soils are better able to withstand extreme weather conditions, such as droughts and floods. The major benefits of soil health management include, enhanced productivity, environmental protection, climate resilience, carbon sequestration.

Overall, soil health management aims to maintain the long-term productivity and sustainability of agricultural systems while minimizing negative environmental impacts such as soil erosion, nutrient runoff, and greenhouse gas emissions.

The intersection of soil health management and artificial intelligence (AI) represents a promising frontier in agricultural innovation, since it offers both the potential to enhance agricultural sustainability and productivity, as well as adapt to the climatic changes or resource constraints. This study is about Soil health management using AI, the foresaid theme involves leveraging advanced technologies to monitor, analyze and optimize soil conditions for sustainable agriculture. Since AI is often data driven, therefor the data from several sources, like as soil sensors, drone images, etc. could be utilized to analyze the current state of the soil and predict future ones. This is soil health management. Harnessing the power of artificial intelligence offers a transformative approach to both soil health management and ecosystem community. As over irrigating field is neither beneficial nor recommendable therefore optimal amount of irrigation and nutrients supply can be done via these AI driven recommender systems combined with robotic machinery from data was prior collected through the above said media. This will improve soil health and increase the chances for crop yield enhancement. AI technologies, including machine learning algorithms and data analytics, enable farmers and agronomists to make data-driven decisions at a scale and precision previously unattainable. By integrating AI into soil health management practices, stakeholders can optimize resource allocation, mitigate risks, and enhance agricultural productivity while minimizing environmental impacts. Overall, AI has the potential to revolutionize soil health management, making it more precise, efficient, and sustainable, which can lead to increase in crop yields, improved food security, and reduce environmental impact. Assuring the long-term growth of agriculture and the wellbeing of the environment requires agricultural practitioners to place a high priority on soil management and take action to safeguard and enhance the soil health.

Figure 2. Soil Attributes [6].

Figure 2. Soil Attributes [6].

However, AI in soil health management faces certain challenges related to cost, accessibility, expertise, data quality, interpretability and system integration [7]. Additionally, managing, storing, and securing the data needed for AI analysis can conflict with data privacy laws, necessitating careful consideration of legal and regulatory aspects. Ensuring the quality of data, interpretability of results and seamless integration of AI models into existing soil health management practices are crucial for the successful implementation of AI-based soil analysis.

With the advancement in technology, the use of artificial intelligence (AI) in agriculture is becoming more prevalent, particularly in soil management. Conventional methods and experience are often the foundation of traditional soil management approaches, which frequently fall short to discover the full potential of soil and are ill-equipped to handle the demands of modern agriculture. With the goal of promoting sustainable agricultural development and offering more intelligent and scientific solutions for agricultural production, this paper will explore the use of AI in soil health management and its role in the growth of smart agriculture systems. It delves into the key components of AI-driven soil health management, including data collection methodologies, predictive modeling techniques, and decision support systems. Additionally, it examines case studies and real-world applications demonstrating the efficacy of AI in optimizing soil health and fostering sustainable agricultural practices.

2. Application of AI in Soil Health Management

Artificial intelligence (AI) offers various applications in soil health management that can revolutionize agricultural practices. They are [8]:

a.

Soil Monitoring and Analysis

AI algorithms can analyze data from various sources such as satellite imagery, soil sensors, and historical data to monitor soil health parameters like nutrient levels, moisture content, and pH. This data-driven approach allows for real-time monitoring and timely interventions to optimize soil health. Utilizing machine learning algorithms to analyze soil properties and predict nutrient levels is a significant aspect of intelligent soil management in modern agriculture.

b.

Predictive Modelling

Integrating sensor technology for real-time monitoring of soil moisture and pH value is an important intelligent soil management method in modern agriculture. AI models, such as machine learning algorithms, can predict soil properties and health indicators based on historical data. This can help farmers make informed decisions about crop selection, fertilizer application, and irrigation strategies to improve soil health and productivity.

c.

Precision Agriculture

Deploying AI-driven UAVs (Unmanned Aerial Vehicles) and satellites for mapping soil variability is an advanced soil management method in modern agriculture. AI-powered precision agriculture technologies enable farmers to apply inputs like water, fertilizers, and pesticides with precision, reducing waste and environmental impact. By analyzing soil data and crop performance indicators, AI can optimize input usage for sustainable soil management.

d.

Disease and Pest Detection

Advanced plant protection technology utilizes AI-driven image recognition systems to swiftly detect and identify early symptoms of plant diseases, using image recognition algorithms and machine learning models. This enables for the timely and efficient application of control measures. AI-based image recognition systems can analyze images of crops and soil to detect signs of diseases, pests, or nutrient deficiencies. Early detection allows for targeted interventions, reducing the need for broad-spectrum treatments and minimizing crop damage.

e.

Recommendation Systems

AI-driven recommendation systems can provide personalized advice to farmers on soil management practices based on individual soil conditions, crop types, and environmental factors. These recommendations can optimize soil health, crop yield, and resource efficiency.

f.

Autonomous Farming Equipment

Deploying autonomous robots for preventive and control measures, such as field management and crop coverage, is a modern, efficient agricultural management method. AI-powered autonomous tractors and drones can perform soil health monitoring, precision planting, and other tasks with minimal human intervention. These technologies streamline agricultural operations and enable efficient soil management practices.

Overall, the application of AI in soil health management holds great potential for enhancing agricultural sustainability, productivity, and resilience. By leveraging AI technologies, farmers and land managers can make data-driven decisions to improve soil health, conserve resources, and ensure long-term food security.

3. Literature Review

In recent years, the advanced technologies, such as artificial intelligence (AI), has provided new opportunities to revolutionize soil health management practices. AI techniques, including machine learning, deep learning, and data analytics, offer the potential to analyze vast amounts of soil-related data, extract meaningful insights, and guide decision-making processes in real time. By leveraging AI-enabled smart agriculture systems, farmers can enhance soil quality, optimize nutrient management, minimize environmental impact, and increase agricultural productivity in a sustainable manner. This section reviews the work done to provide a comprehensive overview of recent research and development in soil health management using AI for smart agriculture systems. By synthesizing existing knowledge and identifying emerging trends, this review seeks to elucidate the potential benefits, challenges, and opportunities associated with integrating AI technologies into soil management practices, aiming to highlight key findings, gaps in knowledge, and future research directions in this rapidly evolving field.

Reference	Technique	Strength	Limitations
Plant, et.al. [9]- 1989	CALEX	Prepares scheduling guidelines for crop management	Time consuming
Gholami, et.al. [10]-2017	ANN (Artificial Neural Network)	To estimate soil erosion, high calculation speed, high accuracy	Plots required to monitor rill erosion
Zhao, et.al. [11]-2007	ANN	High-resolution soil texture maps generated using coarse resolution soil texture map	Low accuracy
Mosaffaei, et.al. [12]-2020	ANN	Predict degradation in national park management plan	Adaptation challenge with new data.
Shao, et.al. [13]-2021	BP-ANN (Back Propagation- Artificial Neural Network)	Classify and evaluate soil quality, where soil nutrients contaminated with heavy metal contamination in the arid area	Expensive
Dahmardeh, et.al. [14]-2017	ANN, ANFIS (Adaptive Neuro-fuzzy Inference System)	The effects of tillage type, temperature, sodium are evaluated based on type of intercropping to carbon-nitrogen ratio	Measures only two chemical properties
Pellegrini, et.al. [15]-2021	ANN	Predict Soil microbial-biomass from soil physical and chemical properties	Only a few cases were studied.
Jalal, et.al. [16]-2021	ANN, ANFIS, GEP (Gene Expression Programming)	Prediction models developed to evaluate swell pressure and unconfined compression strength of expansive soils	Internet dependent
Kim, et.al. [17]-2008	ANN	Estimates soil erosion, NH4–N concentrations and dissolved P of runoff	Not accurate for higher erosion values
Arsoy, et.al. [18]-2013	ANN	Soil water content determination based on dielectric permittivity measurement	Time consuming
Liu, et.al. [19]- 2015	SVM (Support Vector Machine)	Classification and assessment of urban soil quality	sensitive to outliers
Guan, et.al. [20]-2011	SVM	Soil salinity prediction for irrigation water management in irrigation districts	Prior Knowledge of EC value required
Mustafa, et.al. [21]-2018	SVM	Geospatial prediction of soil erosion	High Complexity
Wijitdechakul, et.al. [22]-2016	UAV (Unmanned Aerial Vehicle)	Interpret the plant health conditions for user.	Expensive
Pluer, et.al. [23]-2020	UAV	To test field scale variation in soil characteristics	High complexity
Krenz, et.al. [24]-2019	UAV	To identify the degradation status of soils	Tussocks or exposed shrub roots cannot be detected
Falco, et.al. [25]-2018	UAV	To estimate sprout density and plant vigor throughout the growing season	High complexity
Rosa, et.al. [26]-1999	ImpelERO	To evaluate soil erosion	Time consuming
Kaufamann, et.al. [27]-2009	Fuzzy logic expert system	To evaluate the plant productivity of restored soils	Internet dependent.
Ahsanuzzaman, et.al. [28]-2004	Expert system	To evaluate groundwater pollution from application of manure to soil	Internet-based.

4. Case Study

• This section addresses the application of Artificial Intelligence for soil health management, providing a detailed view of data input, algorithms used, features, characteristics and the optimal results obtained.

REFERENCE	DATA INPUT	ALGORITHM	FEATURES	CHARACTERISTICS	OPTIMAL RESULTS
Fernandes, et.al. [29]-2019	8556 Samples	ANN	Estimates soil organic matter content from soil chemical attributes	Soil Organic matter	R2=0.76, RMSE=1.98g Kg-1
Mirzaee, et.al. [30]-2016	100 soil samples	ANNSK (Artificial Neural Network Simple Kriging)	To predict soil organic matter content	Soil Organic matter	R2=0.633, RMSE=0.271
Somaratne, et.al. [31]-2005	240 soil samples	ANN, MLR (Multivariate Linear Regression)	to predict SOC contents across different land use patterns	Soil Organic matter	1. ANN: Ci(R2=0.92), Ce(R2=0.83) 2. MLR: Ci(R2=0.73), Ce(R2=0.82)
Bouasria, et.al. [32]-2020	369 soil samples	DT (Decision Tree), K-NN (K- Nearest Neighbour), ANN	To predict soil organic matter content	Soil Organic matter	ANN:(MS image: R2=0.6553, PAN image: R2=0.6985)
Huang, et.al. [33]- 2020	102 soil samples	BPNN, SVR (Support Vector Regression), PLSR (Partial Least Square Regression)	To predict soil organic matter concentration	Soil Organic matter	1. BPNN: (R2=0.880, RMSE=2.679) 2. SVR: (R2=0.895, RMSE=2.531) 3. PLRS: (R2=0.808, RMSE=3.393)
Swetha, et.al. [34]- 2020	90 soil samples	RF (Random Forest), CNN (Convolution Neural Network)	a smartphone application for predicting soil texture	Soil Texture	Clay (R2=0.97-0.98), Sand (R2=0.96– 0.98), Silt (R2=0.62– 4. 0.75)
Zhao, et.al. [35]- 2009	450 sampling points	ANN	To predict soil texture based on soil attributes obtained from existing coarse resolution soil maps	Soil Texture	LM:(RMSE- Clay:7.9, Sand:16.6), RP:(RMSE- Clay:8.5, Sand:14.9)
Penghui, et.al. [36]-2020	Various types of variables	ANFIS- GOA, ANFIS-SSA, ANFIS- GWO, ANFIS-PSO, ANFIS-GA, ANFIS-DA	To predict soil temperature	Soil Temperature	ANFIS-mSG was found to be efficient
Sattari, et.al. [37]- 2020	3995 Records	DT-GBT (Decision Tree-Gradient Boosted Tree)	To predict the soil temperature at	Soil Temperature	NS:0.9446–0.9942, KGE:0.857–0.995, R:0.9793–0.9971
Behmanesh, et.al. [38]-2017	Soil temperature dataset (1997- 2008)	GEP, ANN, MLR	To estimate the soil temperature at different depths	Soil Temperature	ANN performed efficiently
John, et.al. [39]- 2020	60 soil samples	ANN, SVM, RF, MLR	Estimation of soil organic content and soil nutrient indicators	Soil Organic Content Soil Nutrient	RF:R2=0.68, SVM:R2=0.36, ANN:R2=0.36, MLR:R2=0.17
Pathumuthusabana, et.al. [40]-2021	1700 soil sample images	CNN, Lenet, AlexNet, Vgg16	classification of SOC and soil macronutrients	Soil Organic Content, Soil Macronutrients	Accuracy: Lenet:77.4%, AlexNet:85.31%, Vgg16:87.38%
Rajamanickam, et.al. [41]-2021	1000 Samples	DT, KNN, SVM	Predicts soil fertility based on macro and micro nutrients status	Soil Fertility	MSE (DT:0.01, KNN:0.6897, SVM_linear:0.6552 SVM_rbf:0.559
Zhang, et.al. [42]- 2021	Various types of variables	DT, RF	Predicts soil fertility	Soil Fertility	RF and DT are the most accurate methods
Hassan-Esfahani, et.al. [43]-2015	Various types of variables	ANN, UAV	Estimates surface soil moisture	Soil Moisture	RMSE:2.0, MAE:1.3, R2:0.77
Gill, et.al. [44]- 2006	Various types of variables	SVM, ANN	Predicts soil moisture	Soil Moisture	SVM performed efficiently
Prakash, et.al. [45]-2020	Various types of variables	MLR, SVM, RNN (Recurrent Neural Network)	Predicts Soil Moisture	Soil Moisture	MLR performed efficiently
Sarmadian, et.al. [46]-2008	125 soil samples	MLR, ANN	Predicts soil parameters	Soil Properties	ANN performed efficiently
Kurnaz, et.al. [47]- 2015	Various types of variables	ANN	Predicts compression and recompression index of soil	Soil Properties	Compression index(R2=0.8973), Recompression Index(R2=0.3600)
Mohanty, et.al. [48]-2015	721 soil samples	ANN	Evaluates Pedotransfer function of Field Capacity and Permanent Wilting Point	Soil Properties	ANN indicated unbiased and higher predictability

5. Limitations of Artificial Intelligence in Soil Health Management

Artificial intelligence in soil health management might have major benefits but it also comes with several challenges. They are:

• Data Quality and Quantity: AI models require a significant amount of high-quality data to effectively analyze and predict soil health. Obtaining comprehensive and accurate soil data can be a challenge, especially in remote or under-studied regions.
• Model Interpretability: Some AI models, such as deep learning neural networks, can be complex and difficult to interpret. Understanding how the AI reaches its conclusions about soil health can be crucial for gaining trust from users and stakeholders.
• Integration with Traditional Practices: Integrating AI technologies with existing soil management practices and workflows can be challenging. Ensuring that AI recommendations align with local knowledge and practices is essential for successful adoption.
• Cost: Implementing AI solutions for soil health management can require significant financial resources, especially for collecting data, developing models, and deploying technology in the field. Cost can be a barrier for small-scale farmers or resource- constrained agricultural organizations.
• Regulatory and Ethical Concerns: There may be regulatory challenges around data ownership, privacy, and ethical considerations when using AI for soil health management. Ensuring compliance with relevant laws and regulations is essential to avoid legal issues.

Overcoming these challenges often requires collaboration among farmers, researchers, technology developers, policymakers, and other stakeholders to develop tailored AI solutions that address specific soil health management needs while considering the broader social, economic, and environmental context.

6. Conclusion

According to recent research, the Indian economy’s most important industry is agriculture, which employs more than 60% of the workforce and accounts for over 17% of GDP. In order to obtain soil fertility and crop health status on a regular basis, farming must be revolutionized by timely soil testing and crop disease detection employing machine learning algorithms and AI techniques effectively on real-time datasets. Artificial Intelligence has been instrumental in revolutionizing soil management practices. AI analyzes soil properties, forecasts nutrient levels, maps soil variations, applies precision irrigation and fertilization, identifies early signs of plant diseases, guides targeted treatments, forecasts soil erosion risks, and deploys autonomous robots for control measures through machine learning algorithms. Anticipating the future, AI will propel smart agriculture into the norm. As artificial intelligence (AI) technology advances and gains traction, it will enable intelligent, more effective, and sustainable agriculture. This will play a major role in conserving the environment, fostering rural revival, and resolving the problem of food security.