Manisha Aeri
Papers
1
Total Citations
4
H-Index
1
About
Manisha Aeri is a researcher at the forefront of applying deep learning to agricultural automation, with a primary focus on image-based classification and quality assessment. Her most notable contribution is the development of an ensemble method that combines fine-tuned ResNet20 and DenseNet models for the automated classification of pistachios, addressing critical challenges in manual sorting such as human subjectivity, inconsistency, and inefficiency. This work, published in 2024 and already garnering 4 citations, demonstrates her ability to leverage state-of-the-art neural network architectures for practical, real-world agricultural problems. By reducing the reliance on subjective human judgment, Aeri’s research directly enhances the precision, productivity, and overall efficacy of sorting procedures, offering a scalable solution for the food processing industry. Her work exemplifies the growing intersection of computer vision and smart agriculture, positioning her as an emerging voice in applied machine learning.
Research Focus
Key Achievements
Top Papers
- 1