Authors : Tarun Badiwal; Manish Jain; Sandeep Jayswal; Suresh Meena

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/8stawdxh

DOI : https://doi.org/10.38124/ijisrt/26aug1195

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Agriculture is increasingly dependent on technologies that can improve productivity while reducing the consumption of water, fertilizers, pesticides, herbicides, labour and other resources. The five source chapters reviewed for this paper collectively describe the role of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), computer vision, cloud computing and embedded systems in smart farming. The summarized work focuses on continuous sensing of soil and environmental parameters, automated irrigation, crop and weed monitoring, disease identification, yield prediction and decision support. The sources identify Artificial Neural Networks (ANNs), Deep Learning, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as important approaches. A prototype architecture is also described using Arduino Mega 2560, Raspberry Pi, multiple sensors, Firebase and an Apache web server. In the disease-detection prototype, 295 leaf images were divided into training, validation and testing groups, and a CNN-based mobile application produced a reported confidence score of 0.97 for an example operation with an execution time of approximately 0.88 seconds. Overall, the reviewed material indicates that integrating IoT sensing with AI/ML can support real-time monitoring, resource optimization and faster agricultural decisions. However, Internet dependence, cybersecurity, system complexity, adoption cost, limited datasets and reduced accuracy for visually similar crop diseases remain important challenges.

Keywords : Smart Agriculture; Artificial Intelligence; Internet of Things; Machine Learning; Deep Learning; CNN; ANN; Precision Agriculture; Automated Irrigation; Crop Disease Detection.

References :

1. Uploaded Source Chapter 1, “Introduction to Smart Farming in Agriculture,” Vivekananda Global University, Jaipur, supplied as chapter 1.pdf.
2. Uploaded Source Chapter 2, “A Study on Agriculture Using Artificial Intelligence,” Vivekananda Global University, Jaipur, supplied as chapter 2.pdf.
3. Uploaded Source Chapter 3, “Study of Machine Learning in IoT Based Agriculture,” Vivekananda Global University, Jaipur, supplied as chapter 3.pdf.
4. Uploaded Source Chapter 4, “An Intelligent Prototype Model for Smart Agriculture,” Vivekananda Global University, Jaipur, supplied as chapter 4.pdf.
5. Uploaded Source Chapter 5, “Outcomes & Limitations,” Vivekananda Global University, Jaipur, supplied as chapter 5.pdf.