Skip to content
Research Article Open access CC BY 4.0

Data-driven Agriculture: Software Innovations for Enhanced Soil Health, Crop Nutrients, Disease Detection, Weather Forecasting and Fertilizer Optimization in Agriculture

Hridesh Harsha Sarma, Bikash Chandra Das, Tridisha Deka, Sofior Rahman, Marjana Medhi, Mriganko Kakoti

Journal of Advances in Biology & Biotechnology · pp. 878–896 · Published 2 Aug 2024

10.9734/jabb/2024/v27i81209

Abstract

In the evolving landscape of modern agriculture, the integration of software technologies has become increasingly indispensable. These tools offer transformative capabilities that address the complexities and challenges faced by today's farmers. By harnessing the power of software applications, farmers gain access to sophisticated tools for precise planning, decision-making, and operational management. These applications facilitate precise management of farm operations, including crop planning, irrigation scheduling, and fertilization regimes, thereby optimizing resource use and enhancing productivity. Moreover, these tools provide real-time access to weather forecasts, pest and disease alerts, and market prices, enabling farmers to make informed decisions and mitigate risks effectively. Some of the common Softwares used by farmers are Meghdoot, Mausam, Plantix, FarmRise, Agrisetu, KisanSuvidha, Umang Pusha Krishi, Kisan Girdawari etc. Moreover, in an era where climate variability and market dynamics pose significant uncertainties, these technologies provide critical insights and support systems that empower farmers to navigate challenges effectively and seize opportunities for growth. As such, the adoption of agricultural software is not just a modernization effort but a strategic imperative to empower farmers and ensure the resilience and sustainability of global food systems.

Agriculture crop planning Kisan Suvidha Meghdoot Pusha Krishi weather forecasts

References (6)

  1. 1 Decision support tools for agriculture: Towards effective design and delivery [DOI]
  2. 2 Farm management information systems: Current situation and future perspectives [DOI]
  3. 3 Providing Smart Agricultural solutions to farmers for better yielding using IoT [DOI]
  4. 4 Robust Indoor Human Activity Recognition Using Wireless Signals [DOI]
  5. 5 County-Scale Spatial Distribution of Soil Nutrients and Driving Factors in Semiarid Loess Plateau Farmland, China [DOI]
  6. 6 4M — Software for Modelling and Analysing Cropping Systems [DOI]

Cited by 30

Advancing SDG 2 Through Smart Agriculture: Modeling Technology Adoption Among Indonesian Farmers Using the UTAUT2 Framework

Ari Basuki, Andharini Dwi Cahyani, Yudha Putra Dwi Negara · Journal of Lifestyle and SDGs Review · 2025

Enhancing plant productivity and sustainability under diverse plant-environment interactions

Mehrdad ALIZADEH, Mohsen ABBOD, Fatemeh GRAILY MORADI · Notulae Scientia Biologicae · 2025

Crop Health Monitoring Through AI-Based Crop Leaf Disease Detection Systems

Parag Bhuyan, Pranav Kumar Singh, Sujit Kumar Das · Advances in Environmental Engineering and Green Technologies · 2025

A review on mobile apps in agriculture: Transforming farming practices in India through innovation and technology

Dr. Parvez Mallick, Dr. Subhadip Pal, Dr. Agniswar Jha Chakrabarty · International Journal of Agriculture Extension and Social Development · 2026

A Comprehensive Review of Vision and IoT Based Smart Soil Nutrient and Crop Monitoring: Challenges and Opportunities

Lakshmi Khetawat, Jetendra Joshi · 2025 International Symposium on Electrical and Electronics Engineering (ISEE) · 2025

Paddycrops Disease Prediction Using Image Processing by Using 3DCNN

Chandru R, S. Lakshmanan · 2025 International Conference on Inventive Computation Technologies (ICICT) · 2025

Thermal canopy segmentation in tomato plants: A novel approach with integration of YOLOv8-C and FastSAM

Hemamalini P, Chandraprakash MK, Laxman RH · Smart Agricultural Technology · 2025

IoT-Enabled Smart Farming for Real-Time Weather Forecasting and Crop Health Monitoring Using Hybrid GRU-CNN Models

Raj Kumar Gupta, S.Nandhini Devi, Shahin Athavani · 2025 6th International Conference for Emerging Technology (INCET) · 2025

Showing 9 of 30 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

30

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.