Artificial Intelligence in Brinjal Phenotyping: A Review of Emerging Tools for Trait Characterization and Crop Improvement

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 9

Year of Publication : 2025

Authors : Eshani Akansha Bisht, Anita Gehlot, Rajesh Singh, Charvi Joshi, Rahul Mahala

10.14445/23488549/IJECE-V12I9P104

How to Cite?

Eshani Akansha Bisht, Anita Gehlot, Rajesh Singh, Charvi Joshi, Rahul Mahala, "Artificial Intelligence in Brinjal Phenotyping: A Review of Emerging Tools for Trait Characterization and Crop Improvement," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 9, pp. 46-62, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I9P104

Abstract:

Over 295 million people in 53 countries experience acute food insecurity due to factors like famine, war, climate change, and conflict zones. Sustainable Development Goal 2: Zero Hunger aims to achieve food security, improve nutrition, end hunger, and promote sustainable agriculture. Balancing farming with environmental protection is crucial, especially in the face of climate change and globalization. Studying plant phenomics, which focuses on how plants grow and react to climate change, can help develop more productive and stronger crops. Advanced technology, such as High-throughput plant phenotyping, can provide detailed data for accurate predictions and better disease control. This article aims to explore the use of AI and machine learning in plant phenotyping, the integration of imaging technologies, IoT, and sensors, and the application of various technologies, including Brinjal, in vegetable phenotyping. Artificial Intelligence, IoT devices, edge computing, computer vision, and advanced sensor technologies are revolutionizing sustainable agriculture. These technologies provide real-time data, early detection of diseases, and improved nutrient, water, and pest management. Auto Machine Learning, Explainable AI, and Deep Learning enhance understanding and optimize breeding cycles. This combination of multi-omics data, machine learning, and smart tools is crucial for smart and sustainable agriculture, promoting farmer-based innovation and cross-sector collaboration.

Keywords:

Phenotype, Genotype, Internet of Things, Artificial Intelligence, Sustainable agriculture, Sensors.

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