Artificial intelligence and machine learning in soil analysis innovations for sustainable agriculture_

~ 869 ~ ISSN Print: 2617-4693 ISSN Online: 2617-4707 IJABR 2024; 8(11): 869-878 www.biochemjournal.com Received: 06-09-2024 Accepted: 09-10-2024 Kuldeep Kumar Faculty of Agriculture (Genetics and Plant Breeding), Maharishi Markandeshwar (Deemed to be University) Mullana Ambala, Haryana, India Avimanyu Palit Ph.D. Research Scholar, Department of Agronomy, Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India Simran Sindhu, M.Sc. Agronomy, Chaudhary Charan Singh Haryana Agriculture University, Hisar, Haryana, India Divya D, Ph.D. Research Scholar, Department of Soil Science, Keladi shivappa Nayaka University of Agricultural and Horticultural Sciences iruvakki shimoga, Karnataka, India Yogita Assistant Professor, Faculty of agriculture, Maharishi Markandeshwar (Deemed to be) University, Mullana, Ambala, Haryana, India Rajeeb Lochan Moharana Assistant Professor, Seed Science and Technology, College of Agriculture, OUAT, Bhawanipatna, Kalahandi, Odisha, India Smriti Hansda Assistant Professor (SWCE), College of Agriculture, Bhawanipatna, Odisha University of Agriculture and Technology, Odisha, India Anil Kumar Assistant Professor, Department of Agronomy, Eklavya University Damoh, Madhya Pradesh, India Corresponding Author: Anil Kumar Assistant Professor, Department of Agronomy, Eklavya University Damoh, Madhya Pradesh, India Artificial intelligence and machine learning in soil analysis innovations for sustainable agriculture: A review Kuldeep Kumar, Avimanyu Palit, Simran Sindhu, Divya D, Yogita, Rajeeb Lochan Moharana, Smriti Hansda and Anil Kumar DOI: https://DOI.org/10.33545/26174693.2024.v8.i11k.2973 Abstract Soil analysis is one important tool in sustainable agriculture since they represent information in terms of soil health and fertility to farmers; hence, there is an optimum resource management. Conventional methods of soil analysis though effective are normally time-consuming, expensive, and often labor- intensive. With the appearance of Artificial Intelligence (AI) and Machine Learning (ML), it has been no more than a transformative innovation in the field of soil analysis. This paper addresses some recent innovations of AI and ML application for soil analysis, focusing on techniques such as remote sensing, spectral analysis, predictive modeling, and precision agriculture, all of which help sustain farming practices. Keywords: Soil analysis, AI tool, machine learning, sustainable agriculture 1. Introduction Soils are one of the determinants in agricultural productivity as their health would dictate yield, quality, and production (Khanna et al. 2019) [44]. Sustainable management of the soil also requires correct analysis to ensure that the soil has fertility to support crop cycles (Bai et al. 2020) [11]. AI and ML-based technologies present a more advanced alternative for the traditional testing methods on soil (Awais et al. 2023) [9], which can provide real-time understanding and predictive capability aiding efficient and sustainable management of the soil (Hosseini et al. 2023) [40]. This is the basic foundation of agriculture; it has a direct relationship with the crop yield and quality in relation to ecological stressors. It is essentially important in the long-term sustainability of soil fertility in sustaining the practices of high-yield farming and ecosystems, and also for food security globally (Panduranga, et al. 2024) [61]. The traditional approaches for soil testing are excellent but usually involve a great deal of time, mainly because they require manual sampling of soils, operations performed in a laboratory, and the use of heavy labor resources, which have significant limitations, such as being relatively unfeasible at large scales or even in resource-poor areas. Two of the most prominent emerging technologies that have given birth to revolutionary solutions for soil health management are AI and ML (Maraveas et al. 2022) [46]. These technologies empower data-driven insights by enabling real-time, accurate, and scalable soil analysis (Babar et al 2024) [10]. They bring actionable insights on nutrient levels, moisture content, pH, and more to farmers and agricultural scientists. Artificial intelligence and machine learning help in predictive capabilities and empower the adoption of proactive soil management practices by farmers for optimal crop cycles and sustainable agriculture (Senoo et al. 2024) [71] and (Mana et al. 2024) [45]. The applications of soil analysis AI and ML facilitate the precision agriculture approach to farming wherein the application of resources is tailored to individual soils in order to avoid the use of chemicals and save natural resources (Li, X. 2024) [35]. It only increases the fertility of the soil but also reduces impact on the environment through fertile agricultural produce that shares coherence with the programs of sustainability (Pandey et al 2024) [60]. This paper reports and discusses the present applications, case studies, challenges, and future International Journal of Advanced Biochemistry Research 2024; 8(11): 869-878.

[Audio] Our presentation today focuses on the utilization of AI and ML technologies for soil analysis in sustainable agriculture. In order to address the challenges of limited time and resources, it is crucial to incorporate innovative and efficient methods for soil analysis. This is where AI and ML come into play. According to various studies, remote sensing and hyperspectral imaging have been identified as crucial tools for analyzing soil composition. One such study published in the International Journal of Advanced Biochemistry Research discusses the use of these technologies for soil composition analysis (Attri et al. 2023). But how exactly do AI and ML aid in soil analysis? Through advanced deep learning methods like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we are able to predict nutrient levels and moisture content from unstructured soil data, including images and remote sensing data (Hassan et al. 2023). CNNs, in particular, are effective in spectral analysis of soil as they can detect and quantify micronutrient and organic matter content based on spectral attributes. On the other hand, RNNs can identify seasonal patterns of soil moisture and nutrient depletion through time-series monitoring, using historical data to facilitate proactive soil management (Han et al. 2024) and (Chintamaneni et al. 2024). The usefulness of AI and ML in soil analysis goes beyond these aspects. These technologies also have the potential to create predictive models for crop yield and soil husbandry. By analyzing historical data, AI prediction models can forecast crop yields in real-time, allowing for informed decision-making on factors such as fertilization, irrigation, and crop rotation for sustainable crop production. To conclude, the use of AI and ML in soil analysis offers endless possibilities and presents great potential for effective and sustainable agricultural practices. Thank you for attending slide 2 of our presentation on this important topic. Please continue to follow the remaining slides for further insights..

[Audio] Technology is rapidly evolving and impacting various industries, including the field of agriculture. The third topic of discussion is the application of AI and ML technologies in soil analysis for sustainable agriculture. Traditional methods are time-consuming, but with the convergence of remote sensing and AI, soil health monitoring can now be integrated into large-scale applications. AI algorithms can analyze photographs from satellites and drones to provide valuable information about soil moisture, organic matter, and nutrient distribution. Real-time monitoring is also possible through spectral analysis and ML models, reducing the need for manual sampling. This results in a more efficient and accurate way of analyzing soil health over large areas without physical labor. One important application of AI and ML in soil analysis is soil classification and mapping. By using ML models, farmers can classify and map different types of soil and their spatial variability, allowing for a site-specific management approach. K-Means Clustering and Hierarchical Clustering techniques are used to divide fields into homogenous zones, providing a better understanding of areas that require specific treatment. Through K-Means Clustering, fields can be divided into zones based on soil properties, allowing for variable fertilization to be applied and minimizing waste of resources. By using these technologies, farmers can make informed decisions on optimal levels of fertilizers and timing of irrigation based on projected yield outcomes. In conclusion, the use of AI and ML technologies in soil analysis is revolutionizing agriculture by providing efficient, accurate, and real-time monitoring of soil health. Through soil classification and mapping, farmers can adopt a site-specific approach to maximize yields and minimize waste of resources..

[Audio] Technology is constantly evolving in the modern agricultural landscape to provide more efficient and sustainable solutions for farmers. One of the latest advancements in this field is the use of AI and ML technologies in soil analysis, which is more efficient and precise compared to traditional methods. This allows for personalized recommendations for each farmer's individual needs. An article published in the International Journal of Advanced Biochemistry Research discusses the benefits of AI and ML in soil analysis, such as optimal planting periods, crop rotation, and fertilizer application. The major advantage of AI-based soil mapping is its ability to maximize input usage, reduce environmental impact, and increase yield. By analyzing soil variability at a granular level, farmers can make well-informed and sustainable decisions for their crops. Additionally, the combination of AI and IoT has enabled real-time soil monitoring through sensors that measure temperature, pH, moisture, and salinity. This data is then used by AI models to provide actionable information for soil management. The integration of IoT with AI-based analytics also allows for continuous real-time monitoring of soil parameters, allowing farmers to receive instant recommendations and make necessary adjustments, such as altering irrigation during drought periods or adding nutrients in real-time. Overall, the use of AI and ML in soil analysis is revolutionizing sustainable agriculture by providing site-specific recommendations and real-time monitoring, leading to better yields and a healthier environment..

[Audio] Slide number five of our presentation focuses on the use of AI and ML technologies in soil analysis for sustainable agriculture. Traditional methods of soil analysis tend to be time-consuming and impractical in resource-poor areas. However, with advancements in technology, more efficient and effective solutions have become available. One such solution is the use of AI and ML technologies, as discussed in the International Journal of Advanced Biochemistry Research. These advanced technologies, combined with the use of drones, have shown a significant improvement in soil fertility and productivity. Studies have even shown an increase of up to 25% in yield per hectare. One notable case study is the use of drone-based soil analysis in Brazil's agribusiness sector. With one of the largest agribusiness sectors in the world, Brazil has embraced the use of AI and ML in precision agriculture. Drones equipped with spectral sensors collect data on soil characteristics, which is then analyzed by AI algorithms to identify any nutrient deficiencies, compaction, or erosion. This allows for precise resource allocation and has not only improved yields, but also promoted sustainable farming practices by reducing overuse of resources and minimizing environmental impact. In fact, studies have shown a 15% increase in yields and a 20% decrease in the use of fertilizers and water, thanks to the precision fertilization and irrigation provided by AI models based on drone-collected data. In conclusion, the use of AI and ML technologies in soil analysis has brought significant improvements to the agricultural industry. It not only provides more efficient and accurate analysis, but also promotes sustainable and environmentally-friendly farming practices..

[Audio] Our presentation will focus on the use of AI and ML technologies in soil analysis for sustainable agriculture. These technologies offer a potential solution to the time-consuming traditional methods of soil analysis. Our research has found that the International Journal of Advanced Biochemistry Research has published an article on this topic by Araújo et al. (2023) and Sharma et al. (2020). Challenges in data collection and quality are important considerations when utilizing AI and ML models for soil analysis. One major drawback is the need for high-quality data to feed these models. However, the data on soil can vary greatly in terms of inconsistency, regional differences, and applicability, as pointed out by Munappy et al. (2022) and Deekshith, A. (2021). The primary challenge of using AI-driven soil analysis is the quality and variability of data. Soil data can differ between geographical regions due to varying soil types, climates, and farming techniques. This can make it costly to collect accurate data using sensors, drones, or remote sensing technologies, as highlighted by Barrena et al. (2023). Data scarcity is also a concern, particularly in developing regions where consistent soil health data is limited or non-existent. This can hinder the ability to train models specific to the region, resulting in less accurate predictions and generalized recommendations, as explained by Ahad et al. (2020). In addition to data scarcity, standardization issues can arise due to different measurement practices and reporting structures. This makes it challenging to aggregate data from various sources, as the metrics for soil health may vary. Unstandardized protocols can decrease the success rate of models trained on diverse data. In conclusion, the use of AI and ML technologies in soil analysis for sustainable agriculture has the potential to provide a faster and more efficient method compared to traditional practices. However, it is important to address the challenges of data collection and standardization in order to maximize the effectiveness and accuracy of these technologies..

[Audio] The use of AI and ML technologies in soil analysis has great potential for sustainable agriculture. Traditional methods are often time-consuming and can have negative environmental impacts. The International Journal of Advanced Biochemistry Research has reported that unsustainable sources are erasing any environmental benefits in sustainable agriculture practices (Awais et al. 2023). To address this issue, it is important to turn towards using AI and ML technologies for soil analysis. One solution is to implement clear data privacy policies that establish ownership of soil data by farmers. This can encourage data sharing while still protecting the rights of the farmers. Additionally, investing in energy-friendly AI tools, such as solar sensors, can greatly reduce the environmental impact of these technologies in agriculture. Looking towards the future, the advancement of AI and ML technologies may lead to more accurate, accessible, and sustainable soil analysis. This can be achieved through hybrid approaches that combine traditional soil science with AI methods. By incorporating these hybrid models, we can enhance accuracy and adaptability by combining physical properties of soils with predictions from AI. For example, physics-informed neural networks, based on the principles of both soil and environmental science, can provide substantial insight into soil assessment. This will allow farmers to make better choices that boost crop productivity through optimum resource usage and minimal environmental impact. From smallholder farms to large-scale agribusiness, AI-based tools can support more precise soil management, reducing dependency on chemical inputs and promoting regenerative practices that will build up soil vitality over time. However, data scarcity, high costs, and accessibility barriers still limit the widespread usage of these technologies. Therefore, it is important to address these obstacles and challenges in the development of cost-effective solutions and models that can be applied in various regions and agricultural practices..

[Audio] Slide 8 focuses on the use of AI and ML technologies for sustainable agriculture. With the growing need for more efficient and effective methods of soil analysis, traditional approaches are proving to be time-consuming, expensive, and require specialized equipment and training. This is where AI and ML come in. These advanced tools have the ability to analyze large amounts of data, providing accurate and timely results for farmers and researchers. They can also identify patterns and trends in soil quality, leading to targeted interventions and improved practices. In fact, recent research has shown that the use of AI and ML in soil analysis has the potential to greatly improve precision irrigation and fertilization in orchards, resulting in increased yields and reduced environmental impact. These technologies not only benefit agriculture, but also have a significant impact on our environment. By using AI and ML, we can better understand and address issues such as soil nutrient deficiencies, soil property patterns, and overall soil health. Promising research has also revealed the potential for AI and ML-driven crop yield predictions, allowing for more effective and sustainable farming practices. As we move towards Industry 4.0, the use of AI and ML in agriculture becomes increasingly crucial. However, it is not just about technology, it is about creating a more sustainable future for our planet and its resources. Thank you for joining us in this discussion on the potential of AI and ML in soil analysis for sustainable agriculture. Let's continue to explore and embrace the possibilities for a better tomorrow..

[Audio] This article focuses on the use of AI and ML technologies in soil analysis for sustainable agriculture, as an alternative to traditional, time-consuming methods. The International Journal of Advanced Biochemistry Research, found at biochemjournal.com, features recent studies and advancements in this field. The ninth slide out of ten in particular highlights notable research and developments using AI and ML in soil analysis and management. The first study, conducted by Hosseini and colleagues, compares the accuracy and efficiency of spectral reflectance methodology to traditional soil analysis. The results show potential for the use of AI and ML in improving soil analysis, specifically in precision fertilization and irrigation. Next, Malashin and his team's review paper explores the potential of variable-rate fertilization and delineation of soil management zones using AI and ML tools. The third study, led by Singh and his team, focuses on the use of multivariate spatial clustering models to delineate site-specific management zones based on soil properties. Khanna and Kaur's paper discusses the significant impact of Internet of Things (IoT) in the field of Precision Agriculture, which has the potential to revolutionize farming practices and promote sustainable agriculture. Additionally, two studies by Pandey and Mishra, and by Mana and colleagues, examine how AI and ML can be utilized for global food security and sustainable agriculture. These advanced tools can aid in predicting sustainable crop yields and managing rotational cropping systems. The results of these studies highlight the potential for AI and ML in transforming soil analysis and management, paving the way for a more sustainable and efficient approach to agriculture..

[Audio] Our presentation concludes with a discussion on the use of AI and ML technologies in soil analysis for sustainable agriculture. Traditional methods of soil analysis can be time-consuming and inefficient, making the integration of artificial intelligence and machine learning a more efficient and accurate approach. In this last slide, we will explore the potential of AI and ML in soil health management, yield prediction, and precision agriculture, supported by reputable sources such as the International Journal of Advanced Biochemistry Research and IEEE Access. These studies have shown the effectiveness of using hyperspectral imaging and machine learning techniques in soil analysis. Additionally, a systematic literature review has demonstrated the benefits of utilizing machine learning in sustainable agriculture supply chain performance, while also showcasing its effectiveness in modeling the relationship between sorghum yield and soil properties. We will also touch upon an interesting thesis from Louisiana State University, which delves into the use of machine learning in deep foundation and soil classification. Lastly, we will review the importance of staying updated with remote sensing methods in monitoring agricultural soil degradation. Thank you for joining us throughout this presentation. We hope you found it informative and insightful. As we move towards a more sustainable future, it is crucial to embrace cutting-edge technologies like AI and ML in improving soil analysis for agriculture. Thank you for your attention..