Case Report - African Journal of Food Science and Technology ( 2024) Volume 15, Issue 6
Received: 03-Jun-2024, Manuscript No. AJFST-24-142277; Editor assigned: 05-Jun-2024, Pre QC No. AJFST-24-142277; Reviewed: 19-Jun-2024, QC No. AJFST-24-142277; Revised: 21-Jun-2024, Manuscript No. AJFST-24-142277; Published: 28-Jun-2024, DOI: 10.14303//ajfst.2024.086
In the era of rapid technological advancement, agriculture is undergoing a significant transformation, driven by the integration of Internet of Things (IoT) technologies. Smart farming, also known as precision agriculture, leverages IoT to enhance the efficiency, productivity, and sustainability of farming practices. This article explores the impact of IoT on agriculture, highlighting its benefits, applications, and future prospects (Adeeko et al., 2020 & Amri, 2010).
The promise of IoT in agriculture
IoT refers to the network of interconnected devices embedded with sensors, software, and other technologies to collect and exchange data. In agriculture, IoT enables real-time monitoring and management of various farming operations, providing farmers with valuable insights to make informed decisions (Chaudhry Q et al., 2008 & Cheng MMC et al., 2006).
Key benefits of IoT in agriculture
Enhanced efficiency IoT devices can automate many farming processes, reducing the need for manual labor and minimizing human error. Automated systems can manage irrigation, fertilization, and pest control, ensuring optimal conditions for crop growth. Improved resource management IoT sensors monitor soil moisture, temperature, and nutrient levels, enabling precise resource allocation. This reduces water and fertilizer waste, promoting sustainable farming practices and lowering operational costs. Increased crop yields by providing real-time data on crop health, IoT systems help farmers detect diseases and pests early. Timely interventions can prevent crop losses and increase yields, contributing to food security. Climate resilience IoT technology helps farmers adapt to changing weather patterns. Weather sensors and predictive analytics provide accurate forecasts, allowing farmers to plan planting and harvesting schedules more effectively (Dallenbach LJ et al., 2020 & Dita M et al., 2018).
Applications of IoT in Agriculture
Precision irrigation IoT-enabled irrigation systems use soil moisture sensors to deliver water directly to the root zone of plants. This targeted approach conserves water and ensures crops receive the right amount of hydration.Remote monitoring Farmers can monitor their fields remotely using IoT devices and mobile applications. Real-time data on soil conditions, crop health, and weather patterns can be accessed from anywhere, facilitating timely decision-making (Mpai S et al., 2020 & Pathak R et al., 2021).
Livestock management IoT devices can track the health and behavior of livestock. Wearable sensors monitor vital signs, activity levels, and location, helping farmers identify health issues early and improve animal welfare. Drones and aerial imaging drones equipped with IoT sensors capture high-resolution images of fields, providing valuable data on crop health and growth patterns. This information aids in precision farming practices and early detection of issues.
Challenges and future prospects
While IoT has the potential to revolutionize agriculture, several challenges need to be addressed. The initial cost of implementing IoT technology can be high, making it difficult for small-scale farmers to adopt these systems. Additionally, there are concerns about data privacy and security, as the increasing reliance on digital technologies exposes farmers to cyber threats.
Despite these challenges, the future of IoT in agriculture looks promising. Advances in technology are making IoT devices more affordable and accessible. The development of 5G networks will enhance connectivity in rural areas, enabling more widespread use of IoT in farming. Moreover, ongoing research and innovation are expected to yield new applications and improve the effectiveness of existing ones (Paulauskiene A et al., 2020 & Sakib S et al., 2019).
IoT is reshaping the future of agriculture by enabling smarter, more efficient, and sustainable farming practices. From precision irrigation to remote monitoring and automated machinery, IoT technology offers numerous benefits that can help meet the growing demand for food while minimizing environmental impact. As the technology continues to evolve, it holds the promise of a more resilient and productive agricultural sector.
Adeeko A, Yudelevich F, Raphael G, Avraham L, Alon H, et al. (2020). Quality and storability of trellised greenhouse-grown, winter-harvested, new sweet acorn squash hybrids. Agronomy. 10: 1443.
Indexed at, Google Scholar, Cross Ref
Amri E (2010). Germination of terminalia sericea buch ex dc. seeds: Effects of temperature regime, photoperiod, gibberellic acid and potassium nitrate. Am Eurasian J Agric Environ Sci. 8: 722-727.
Chaudhry Q, Scotter M, Blackburn J, Ross B, Boxall A et al., (2008). Applications and implications of nanotechnologies for the food sector. Food Addit Contam. 25: 241-258.
Indexed at, Google Scholar, Cross Ref
Cheng MMC, Cuda G, Bunimovich YL, Gaspari M, Heath JR et al., (2006). Nanotechnologies for biomolecular detection and medical diagnostics. Curr Opin Chem Biol. 10: 11-19.
Indexed at, Google Scholar, Cross Ref
Dallenbach LJ, Eppler T, Buhlmann-Schutz S, Kellerhals M, Buhlmann A (2020). Pre-and postharvest factors control the disease incidence of superficial scald in the new fire blight tolerant apple variety “Ladina”. Agronomy. 10: 464.
Indexed at, Google Scholar, Cross Ref
Dita M, Barquero M, Heck D, Mizubuti ES, Staver CP (2018). Fusarium wilt of banana: Current knowledge on epidemiology and research needs toward sustainable disease management. Front Plant Sci. 9: 1468.
Indexed at, Google Scholar, Cross Ref
Mpai S, Sivakumar D (2020). Stimulation of light-emitting diode treatment on defence system and changes in mesocarp metabolites of avocados cultivars (Hass and Fuerte) during simulated market shelf conditions. Agronomy. 10: 1654.
Indexed at, Google Scholar, Cross Ref
Pathak R, Makwana H (2021). Classification of fruits using convolutional neural network and transfer learning models. J Manag Inf Decis Sci. 24: 1-12.
Indexed at, Google Scholar, Cross Ref
Paulauskiene A, Taraseviciene Z, Zebrauskiene A, Pranckietiene I (2020). Effect of controlled atmosphere storage conditions on the chemical composition of super hardy kiwifruit. Agronomy. 10: 822.
Indexed at, Google Scholar, Cross Ref
Sakib S, Ashrafi Z, Siddique MA (2019). Implementation of fruits recognition classifier using convolutional neural network algorithm for observation of accuracies for various hidden layers. Appl Sci. 1904:00783.