Artificial intelligence driven smart agricultural applica

Introduction

Agriculture stands as a cornerstone of the globaleconomy, fulfilling a fundamental human necessity:nourishment. Across numerous nations, it serves asthe primary source of employment. Even in countrieslike India, where traditional farming methods persist,many farmers hesitate to embrace advancedtechnologies due to factors like limited knowledge,high costs, or simply unawareness of the benefits thatthese innovations can bring. Insufficientunderstanding of soil types, crop yields, weatherpatterns, improper pesticide use, irrigationchallenges, faulty harvesting practices, and a lack ofmarket trend information contribute to financiallosses for farmers and amplify operational costs(Khanna et al, 2019; Durra et al., 2021). Each stage ofagriculture, if not well-informed, generates newproblems or exacerbates existing ones, adding to theoverall expense of farming. Furthermore, theburgeoning global population places increasingdemands on the agriculture sector. 

The United Nations Development Programme's 2021report on "Leveraging Digital Technology forSustainable Agriculture" underscores the necessity ofboosting global food production by a staggering 98percent to adequately feed a projected population of9.9 billion by 2050 (Durra et al., 2021). Thisimperative goal can only be realized through theefficient utilization of available resources,encompassing land, labour, capital, and technology.Presently, precision agriculture strives to establish adecision support system for farm management,optimizing output while conserving essentialresources. In this context, the emerging trend of foodsecurity must be met with data-driven farmingpractices to enhance productivity, efficiency, andprofitability. Pervasive challenges such as rising fooddemand, labour shortages, water scarcity, climatefluctuations, and escalating energy requirementsunderscore the pressing need for technologicalintervention. Smart agriculture, which encompassesprecision farming, digital agricultural techniques, andmodern practices, holds tremendous promise andwarrants substantial validation.

Smart agriculture predominantly relies on threefundamental pillars: science, innovation, and ICTInformation and Communication Technology(Simonyan et al., 2014; Khanna et al., 2019; Dhanyaet al., 2022). The conventional methods ofinformation and knowledge management employedin gathering and overseeing agricultural data arearduous, time-intensive, and susceptible to errors.Consequently, it is imperative to harnesstechnological advancements in remote sensing, digitalapplications, sensor technologies, advanced imagingsystems, cloud-based data storage, and intelligentdata analysis through decision support systems tousher in a more promising era for the farming sector(Islam et al., 2017; Khamparia et al., 2019; Bania,2023). In Fig. 1, components of intelligentagricultural solutions are shown.

Smart agriculture has the potential to harness cuttingedge technologies such as the Internet of Things (IoT),ML, Cloud Computing, Blockchain, and more, reapingsubstantial benefits for enhancing food production andtackling the emerging challenges within theagricultural sector (Simonyan et al., 2014; Zhao et al.,2021; Dhanya et al., 2022). Notably, the widespreadadoption of computer and mobile technology, even inthe remotest rural areas, has created an unparalleledopportunity for connecting rural producers with urbanconsumers and international investors. Thisconnectivity, in turn, facilitates more robustinvestments and knowledge transfer in agriculture. AIemerges as a transformative technology, boasting atrack record of success across various industries,agriculture included. Machine learning and deeplearning, two subsets of AI, have garnered extensiveattention from researchers due to their capacity to  deliver innovative solutions for modelling intricaterelationships and making accurate predictions basedon agricultural data.

Computer vision, a subfield of artificial intelligence,equips machines with the ability to "see" byharnessing contemporary technologies thatincorporate cameras and computers, thus negatingthe need for human imagination (Dhanya et al., 2022;Bania, 2023). This capability empowers artificialintelligence systems with extensive automationcapabilities. Computer vision collects essential visualdata pertinent to crops, livestock, farms, or gardens,facilitating the identification, detection, and trackingof specific objects through visual elements. Moreover,it enables the comprehension of complex visual datato execute various automated tasks. The realm ofcomputer vision technology encompasses a broadspectrum of solutions for farmers, including small AIpowered mobile applications for decision support, infield imaging sensors, remote sensing technologies fordata acquisition, and the deployment of drones androbots to automate a variety of agricultural processes.

The motivation and goals of this research cantered oninvestigating the substantial contributions that AIand ML models can make in empowering farmers toenhance their decision-making processes in diverseagricultural domains and applications, including soilmanagement, weed control, crop management,livestock husbandry, water resource management,and more. The results of this study highlight theimpressive outcomes attained through the applicationof ML algorithms and models to address agriculturalchallenges