- June 18, 2024
- Geeta University
Abstract
Agriculture is the foundation of all nations and the main driver of world’s economy. It makes a substantial contribution to India’s gross domestic product (GDP). But the world’s population is projected to increase by 2 billion people by the year 2050 and the arable land is only expected to rise by 4%. So, it is clear that more effective agricultural methods must be developed. Artificial intelligence (AI) and its subsets has emerged as a transformative tool in the agricultural sectors, particular in precision farming. Utilizing artificial intelligence in conjunction with cutting-edge remote sensing technology such as, satellite imagery and drones to obtain high resolution data in real time, helps farmers monitor and manage their fields with unparalleled precision. AI may also provide farmers with real-time decision making for accurate pesticides, fertilizer and irrigation applications as well as identify abnormalities and nutrient deficiency in the soil. it is anticipated that the automation of agricultural will improve resource efficiency, decrease waste and increase food security while also improving the environment. AI in agriculture has the potential to transform the industry and be a major factor in the economic growth of nations. It is essential that the agricultural industry adopt these technology solutions in order to assure its growth and resilience in the face of upcoming possibilities and challenges.
Keywords: Precision agriculture, soil health assessment, crop monitoring, water management, sustainable agriculture.
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1. Introduction
Artificial intelligence (AI) and agriculture may appear to be quite separate fields, but they may complement one another. However, AI focuses on sophisticated computer systems and problem-solving techniques, agriculture is more concerned with managing the land and providing food. AI is associated with science fiction, whereas it has a lot of real time uses. About 50% of young people of India are employed in agriculture, which contributes 18% of the GDP to national economy. Earlier, agriculture is used to only produce crops, but in the last few decades, it has now come to encompass distribution, marketing and processing. The world’s population is expanding at a rapid rate thus, traditional farming practices could not be sufficient to fulfill demand of that population. Farmer can benefit from AI in number of ways, including crop monitoring, irrigation and pest management via sensors and robots. This can boost productivity, reduces resource consumption and enhance both productivity and quality. AI is valuable tool for resolving agricultural issues and assisting professionals around the world in coming up with better solutions. Artificial intelligence (AI) in agriculture refers to the use of advanced computer methods and technology to enhance a variety of farming and related works. Using computer systems, algorithms and machine learning models to analyze data, make predictions and automate processes is necessary to boost productivity, efficiency and sustainability in the agriculture sectors (Sharma et al., 2020). AI in agriculture is a technological advancement that modifies traditional farming practices through automation, data analytics and algorithms (Mishra and Mishra, 2023). The goal is to use intelligent technologies to raise agricultural productivity, sustainability and accuracy (Shaikh et al., 2022).
Importance of Precision Agriculture in Indian Context
Farmers are able to make informed decision: Precisionagriculture allows farmers to optimize the use of resources such as water, fertilizer and pesticides based on the specific needs of each site (Bhakta et al., 2019). This is crucial to reduce waste and increase agricultural productivity. Moreover, majority of farmers work on little areas of land. Precision agricultural technology such as, GPS-guided machinery and sensors can be advantageous for small-scale farmers since they aid in their decision-making about the crop management and resource utilization (Gawande et al., 2023).
Precise administration of inputs:Farmers can enhance crop yields by precisely modifying inputs like irrigation, fertilizers and herbicides to suit the requirement of specific field’s segments.This is particularly important in places where food security is a major issue. Rising temperatures and changed rainfall patterns are two adverse effects of climate change. Precision agriculture help farmers adapt to these changes by equipping them with the knowledge and abilities to choose crops more wisely, manage water more effectively and maintain overall farm resilience (Loureset al., 2020). Precision agriculture requires the collection and analysis of massive amount of data including, soil samples, meteorological data and satellite imaging. With this data-driven approach, farmers are better able to make informed decisions about their farming practices (Steup et al., 2019).
Access to market and rural development: Technologies like precision agriculture can assist farmers in meeting the quality requirements needed to gain market access. Accurate data-driven traceability solutions improve the agricultural supply chain’s accountability and transparency, increasing the competitiveness of Indian produce on the international market. The application of precision agriculture technologies can aid in rural development by increasing farmer production and revenue (Balaufotiset al., 2017). Therefore, in rural areas where a significant portion of the population is employed in agriculture, poverty is reduced (Schleussneret al., 2016). Precision agriculture has the power to fundamentally alter the agricultural industry on the continent by addressing specific problems faced by farmers. By using precision agriculture technologies, farmers may boost production, adapt to changing environmental conditions and promote the sector’s overall growth and sustainability.
Role of Artificial Intelligence in Agriculture
Data collection and Integration
Automated Data Collection: In many different industries, artificial intelligence is an essential part of data collection and analysis. It provides advanced abilities for extracting significant insights from huge datasets (Sarker, 2022). AI-powered systems have the ability to automated the large-scale collection of data from many sources including sensors, social media, websites and internet of things (IoT) devices (Nagaty, 2023). Data extraction and analysis from unstructured text is accomplished through the use of natural language processing (NLP), an artificial intelligence component. This is useful for sediments analysis, social media monitoring and inferring conclusions from textual sources. AI-based computer vision data such as, images and videos to find patterns, objects and anomalies (Sarker, 2022; Sharma et al., 2020).
Predictive Analytics: Machine learning algorithms provide predictive analytics by identifying patterns and trends in historical data (Mishra and Mishra, 2023). This is helpful, for predicting future outcomes, like stock prices, consumer behaviour and equipment failures. Survey design, distribution and response analysis can all be automated with AI technologies (Javaid et al., 2022). Customer happiness, staff sentiment and public opinion are all determined by sentiment analysis algorithms that assess feedback. Real-time data from the physical world is gathered and analysed using artificial intelligence in conjunction with sensors networks and IoT devices. This is used in smart cities, industrial monitoring and environmental sensing (Zhang et al., 2021).
Pattern recognition: AI algorithms excel in the field of pattern recognition in data. In fields like geography and geology, this ability is utilised to investigate the creation of strata and patterns in the landscape (Srisawasdi and Panjaburee, 2015). Artificial intelligence advances the study of spatial data by its ability to interpret geographic data. It is used in GIS applications to understand spatial patterns, optimise resource allocation and make justifiable decisions in fields such as environmental research, urban planning and agriculture (Ossai and Oliha, 2019). Large-scale dataset labelling is a crucial machine learning stage. By employing AI to automate or support data labelling, the amount of human labour required to prepare datasets for training models can be reduced (Rohet al., 2019).
Satellite Imagery
Crop monitoring and health assessment: Farmers stand to gain much from AI uses for satellite imaging in agriculture. By analysing satellite data, these technologies use computer vision and machine learning to provide insightful information for better agricultural operations (Sheikh et al., 2022). AI systems are capable of analysing satellite images to monitor crop health. By recognising patterns associated with pests, diseases and nutrient deficiencies, farmers may take timely and focused action to protect their crops (Lin et al., 2012). Machine learning algorithms can analyse historical satellite data along with other relevant variables to predict agricultural production. With this knowledge, farmers can plan their harvests and resource management more efficiently. AI enables precision agriculture by analysing satellite images to identify variations in soil types, moisture content and crop health within a field. Farmers can consequently customise their practices, like fertilization and irrigation to specific sites in order to maximize resource consumption (Javaid et al., 2023).
Land use planning:Planning-related satellite data analysis and land cover classification are made possible by AI methods (Javaid et al., 2023). This knowledge is necessary to make the best use of agricultural land allocation and to comprehend changes in land use patterns. AI helps farmers make informed decision by leveraging actionable data from satellite photography (Subundhiet al., 2023). This includes recommendations for crop rotation, planting density and other agronomic strategies to maximise productivity and sustainability (Mardani et al., 2019). According to Sun et al. (2020), AI is capable of analysing satellite data to monitor water supplies and assess drought situations. This information is necessary for planning irrigation schemes and efficiently managing water resources, especially in regions that are susceptible to water scarcity.
Early detection of plant diseases, pests and weather prediction: Machine learning algorithms can be trained to identify the early signs of pest infestations and plant diseases by looking at satellite imagery (Domingues et al., 2022). By acting preventively in the event of early identification, farmers can reduce crop losses (Liang and Shah, 2023). Combining AI with satellite data enables accurate weather pattern prediction (Bibriet al., 2024). Farmers can utilise this knowledge to boost crop resilience by altering planting dates and agricultural practices to adapt for changing climatic circumstances.
Insurance, risk assessment and supply chain optimization:Hazards associated to agricultural performance can be assessed with the use of AI technology (Javaid et al., 2023). Combining satellite images and machine learning algorithms can result in accurate in accurate crop insurance underwriting and claims processing (Sheikh et al., 2022). Satellite image analysis using artificial intelligence provides valuable insights for optimising the agricultural supply chain (Sharma et al., 2020). This comprises monitoring crop growth, estimating harvest dates and improving logistics for transformation and storage. With the aid of AI-analysed satellite images, governments and regulators can keep an eye on agricultural operations, assess land use policies and make plans for sustainable agricultural growth (Burke et al., 2021).
Sensor Technologies
Remote sensing and drones:AI in sensor technologies is critical to the development of agriculture because it provides real-time data, monitoring and decision support (Singh et al., 2022). A study claims that integrating sensors and AI enhances precision farming, optimises resource use and promotes sustainable agriculture methods. AI-driven sensors enable precision farming by collecting and analysing data on soil temperature, moisture content and nutrient levels (Alahmad et al., 2023; Shaikh et al., 2022). By empowering farmers to make precise decisions regarding irrigation, fertilization and other inputs, this improves agricultural output and resource efficiency. AI systems examine data from drones equipped with diverse sensors and remote sensing technologies. To provide farmers with relevant information for timely action, this involves monitoring crop health, identifying pest infestations and assessing the overall condition of fields (Jung et al., 2021). Farms are equipped with IoT sensors to collect data on many aspects like crop health, soil moisture and weather (Alreshidi, 2019).AI makes use of this data to offer real-time monitoring and data-driven decision-making.
Data analytics for decision support: AI systems analyse sensor data to provide farmers with relevant information. Recommendations for optimal resources allocation, when to plant and harvest and other farm management tips are offered (Mishra and Mishra, 2023). AI enabled sensors assess the condition of the soil by examining factors like nutrient levels and soil composition (Qazi et al., 2022). Utilising this data, farmers may maintain their soil healthier by making well-informed decisions (Singh et al., 2023).
Geographic information system (GIS)
Yield prediction, modelling and climate resilience: AI on geographic information systems is critical to optimising resource management, enhancing decision-making and increasing total agricultural output. AI algorithms examine spatial data from multiple sources including, sensors, satellite imagery and geographic databases (Usigbeet al., 2023). Crop health monitoring is made easier by AI-powered image analysis linked into GIS through the interpretation of drone or satellite imagery. Prompt agricultural protection and increased productivity are made possible by automated disease, pest and nutrient deficiency detection (Jamila, 2023). AI models integrated with GIS are used to forecast crop yields based on historical and present geographic data. This data also aids farmers in more effective planning and managing their agricultural operations (Singh et al., 2023). GIS with AI capabilities supports land use planning by analysing geographic data on climate, soil types and land cover. The two components of sustainable land management i.e. crop rotation and the distribution of agricultural land are supported by this (Shiet al., 2021). The assessment of climate change on agriculture is made easier by GIS and AI. By evaluating regional data on temperature, precipitation and other climatic characteristics farmers and policymakers may design plans to promote climate-resilient agriculture agricultural systems (Srivastav et al., 2021). AI and GIS technologies are used in agriculture to assess and manage water resources (Rao et al., 2019).
Data processing and analysis
Machine learning algorithms
AI-driven machine learning algorithms in agriculture offer innovative solutions to challenges experienced by farmers. Machine learning algorithms use historical data on soil parameters, crop management practices and weather patterns to predict agricultural yields in the future (Singh and Goyal, 2023). With the use of this information, farmers can plan and optimize their agricultural efforts. AI-based machine learning models analyse data from sensors, field observations and satellite imageries to identify signs of diseases and pest infestation (Shaikh et al., 2022). Early diagnosis allows farmers to take timely action, which reduces the impact on crop health. Algorithms trained to differentiate between weeds and crops are said to be proficient in machine learning (Korreset al., 2019). This enables the development of intelligent systems that, by automating weed detection and targeted herbicide application, reduce the demand for broad-spectrum herbicides (Rosle et al., 2021). Machine learning algorithms analyse data from soil moisture sensors, weather forecasts and crop features to enhance irrigation schedules(Katimboet al., 2023; Bwanbale et al., 2022). This ensures efficient water use while addressing the issue of water scarcity.
Machine learning models analyse historical climate data in order to predict future trends of climate. Using this knowledge farmers can adjust planting schedules and choose crop varieties resistant to climate change. Machine learning algorithms analyse soil nutrient data and historical crop performance data to provide precise fertilizer application rates. This improves nutrient management and reduces the environmental impact (Musanaseet al., 2023). AI-driven machine learning algorithms are used to track the health and behaviour of livestock by analysing data from sensors and image technology (Neethirajan, 2023). Predictive models improve cattle management by predicting and preventing infections (Akhigbe et al., 2021). A study revealed that machine learning algorithms improve inventory control, estimate market demand and optimize logistics to help with supply chain optimization (Sharma et al., 2020). Farmers benefit from this since, it ensures the efficient and timely delivery of agricultural goods (Malik et al., 2022). Machine learning techniques analyse satellite images to monitor changes in vegetation, crop health and land cover (Feizizadehet al., 2023). This information facilitates data-driven decision making and helps assess the overall condition of agricultural landscapes.
Machine learning algorithms help farmers maximise their human resource utilization by automating tasks and forecasting heavy workloads (Shaikh et al., 2022). This will be especially helpful to smallholder farmers who might not have as much labour available (Javaid et al., 2023). Machine learning algorithms analyse a range of data sources including, weather patterns and satellite data to forecast drought conditions (Benos et al., 2021). Farmers can use adaptive strategies to decrease the effects on crops and animals (Cravero et al., 2022). Machine learning powered decision support systems provide legislators, extension offices and farmers with pertinent data (Srivastav et al., 2021). Pandey and Pandey (2023) concluded from a study that these systems facilitate the process of making informed decisions on crop management, resource allocation and risk mitigation. AI- driven machine learning algorithm are revolutionizing agriculture by providing data-driven insights and solutions to challenges faced by farmers. These technologies enable more productive and sustainable farming practices, gradually raise the standard of life and food security in the surrounding region (Mishra and Mishra, 2023).
Deep learning techniques
Artificial intelligence and deep learning techniques have the potential to revolutionize agriculture by providing innovative solutions for difficult problems (Sampenaet al., 2022). A subclass of machine learning called deep neural networks that comprises multiple layers, can learn complex patterns and interpretations from massive datasets (Aggarwal, 2018). Convolutional neural network is a type of deep learning model that uses drones or satellite data to assess crop health. By recognizing diseases, pests and nutritional deficiencies these models allow timely management (Shaikh et al., 2022; Bouguettayaet al., 2022).
A study conducted by Kores et al. (2019) claimed that deep learning algorithms are trained to recognize and differentiate between unwanted plants commonly,referred to as weeds or pests and crops. This offers up the prospects to the development of automated pest management and targeted weed control technologies. Plant images are analysed by deep learning algorithms to identify symptoms of sickness. By using patterns these models could learnt from vast datasetsthat help farmers to take preventive measures to protect their crops and detect diseases early (Sambasivam and Opiyo, 2021). Behera et al. (2020) concluded that computer vision systems use deep learning to automate the sorting and grading of fruits and vegetables based on factors including size, colour and quality. This increases the effectiveness of the post-harvest processes.
Deep learning techniques handle remote sensing data including, satellite imaging and multispectral data enabling precision agriculture applications like soil condition monitoring, crop yield predictions and resource optimization (Sishodiaet al., 2020; Sharma et al., 2020). Deep learning models analyse data from soil moisture sensors and climatic conditions to optimize irrigation schedules that help conserve water and boost the effectiveness of irrigation practices (Ahmed et al., 2023; Bwambale et al., 2022). Deep learning models are able to assess data to monitor the health and behaviour of animals identifying psychological abnormalities as well as illness or pain (Neethirajan, 2023). Deep learning is used to assess the quality of agricultural products like, fruits, vegetables and grains in order to identify defects, determine ripeness and ensure product quality along the supply chain (Singh et al., 2022). These methods investigate past climate data to predict future climatic patterns. This knowledge is beneficial to climate-resilient agriculture because it enables farmers to modify their practices in response to changing climatic conditions.
Predictive modelling
Elufioyeet al. (2024) declared that AI-powered predictive modelling holds significant potential for the agricultural industry due to its improved capabilities in outcome forecasting, resource allocation optimization and decision-making. AI-driven predictive algorithms analyse historical data on weather patterns, soil properties and farming practices to predict crop yields. Farmers can more effectively plan and manage their production plans by using this information (Javaid et al., 2023; Shaikh et al., 2022). AI algorithms examine meteorological data to predict how climate change would impact agriculture in particular regions. This involves predicting variations in temperature, precipitation patterns and occurrence of extreme weather events to assist farmers in adapting to challenges associated to climate changes (Akpotiet al., 2019).
AI algorithms use a range of data sources including, historical disease and pest evidence, meteorological data and satellite imagery to estimate the livelihood of disease or pest outbreaks (Toscano-Miranda et al. 2022). Early detection in these can help farmers to avoid problems before they emerge. AI driven forecasting models predict soil moisture content and future weather trends. This information helps optimize irrigation schedules and conserve resources by ensuring that crops receive the appropriate quantity of water (Ahmed et al., 2023; Bwambale et al., 2022). Models powered by artificial intelligence assess crop needs, soil nutrient information and historical performance to provide precise recommendation for fertilizer application. This helps farmers control nutrient more effectively and reduce their adverse effect on the environment (Kumar et al., 2023). Predictive modelling can assist in crop rotation planning by utilising previous data on crop performance, soil health and pest prevalence. This promotes sustainable agricultural practices and lessen the possibility of soil degradation (Reynolds et al., 2018).
Predictive models examine historical pricing records, market trends and external factors to forecast future demand and prices for agricultural products (Carta et al., 2018). This provides expert guidance to farmers in crop selection and market scheduling. AI-based prediction modelling assesses future water availability by analysing data on precipitation, river flow and groundwater level (Hanoon et al., 2021). This information makes it easier to plan water-efficient farming operations, particularly in dry locations. AI predictive models aid in land use planning by examining data on crop performance, climate and soil properties (Folorunso et al., 2023). It assists in the most effective distribution of land for different crops and land management strategies. Artificial intelligence (AI) powered predictive models assess a variety of risks including ones caused by insect outbreaks, market volatility and climate change. The previously described information helps farmers and policymakers develop risk reduction strategies. Through the use of artificial intelligence, agriculture may obtain more accurate and data-driven insights that will boost productivity, sustainability and resilience to shifting market and environmental conditions.
Success stories of India utilising artificial intelligence in precision agriculture
Success stories of India utilising artificial intelligence in precision agriculture
Using technology to collect and evaluate data in order to make better informed judgements regarding farming operations is known as precision agriculture. This entails keeping a check on crop health, managing resources more effectively and raising productivity.
Digital soil-health card: The government of India’s initiative to map soil composition and quality at the farmer level has been instrumental. With the aid of these digital cards, farmers can better assess the condition of their soil and apply the appropriate amount of fertilizer to maximise yield while lowering expenses. This initiative has been supported by AI tools that analyse soil data and recommend specific actions.
CropIn Technology solutions: CropIn is an agritech startup that uses artificial intelligence for providing digital farm management solutions. Their platform aids farmers in forecasting yields, keeping an eye on crop health and providing resource management. This has helped thousands of farmers in India make better decisions and be more productive.
AI for weather forecasting and advisory services: The “SaaguBaahu” project in Andhra Pradesh uses AI to provide farmers, farming advises and weather forecasts. This programme, is a part of World Economic Forum’s AI for Agriculture Innovation (AI4AI) initiative, assists farmers in making knowledgeable decisions regarding irrigation and planting, greatly increasing crop yield and lowering losses from unanticipated weather events.
eNAM (National Agriculture Market): This electronic trading platform connects farmers with buyers across India, guaranteeing better prices via open auctions. AI helps farmers choose when and where to sell their produce by analysing market patterns and forecasting prices. Farmers are now receiving fair prices and are less dependent on regional middlemen.
Ninjacart: This Agtech business optimises the supply chain from farm to retail using AI. Ninjacart minimizes waste and guarantees that farmers receive prompt payments and higher prices for their produce by forecasting demand and streamlining logistics. This AI-driven strategy has greatly increased agriculture supply chain’s efficiency and profitability.
Conclusion
Artificial intelligence has the potential to revolutionize farming techniques through its application in precision agriculture. As we navigate the challenges of agricultural development across the India, artificial intelligence becomes increasingly important offering revolutionary approaches for enhancing efficiency, sustainability and productivity. However, collaborative efforts and deliberate actions are required to fully utilise AI in agriculture. In order to bridge the technology gap and provide broad access to AI technologies, it is imperative to allocate resources towards both digital and physical infrastructure. A comprehensive strategy to promote AI use in agriculture must include capacity-building programmes, frameworks for data governance policies and incentives for technical adoption. Therefore, rather than being merely a technological advancement, AI’s application to precision agriculture is a driver for sustainable development. It has the potential to take agriculture to unprecedented levels, ensuring food security, promoting environmental responsibility and boosting the nation’s economy. With collaboration, innovation and investments in AI-driven agriculture, stakeholders in India have the ability to take the country on a revolutionary journey towards an agricultural landscape that is resilient, efficient and technologically empowered.
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