Research area 03

Smart Agriculture

Early detection of crop and leaf disease from images, for farms with limited data and limited compute.

Scope

Plant and leaf disease detection, crop monitoring and data-efficient learning for agriculture: small datasets, data augmentation, few-shot and federated learning across farms that cannot share raw data.

Our approach

[PLACEHOLDER: how Applied Perception approaches smart agriculture, and two or three open questions, written by Dr. Al Farid]

Publications

Journal article

Bridging the performance–efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection

Rittik Chandra Das Turjy, Fahmid Al Farid, Md. Hasan Imam Bijoy, Sarbajit Paul Bappy, Amir Sohel, Jia Uddin, Hezerul Bin Abdul Karim

Frontiers in Artificial Intelligence, 2026

DOI

Journal article

Deep Learning for Medicinal Plant Classification: A Comprehensive Review of Recent Advances and Challenges

Monir Hossain, Fahmid Al Farid, Momotaz Begum, Jia Uddin, Hezerul Bin Abdul Karim

Journal of Computational and Cognitive Engineering, 2026

DOI

Journal article

FICNet: A Deep Learning Framework for Intrusion Detection in Agricultural Internet of Things

Md. Fahmid-Ul-Alam Juboraj, Fahmid Al Farid, Mahe Zabin, Jia Uddin, Muhammad Iqbal Hossain, Sarina Mansor

Computer Modeling in Engineering & Sciences, 2026

DOI

Conference paper

Deep Learning-Based Classification of Tomato Leaf Diseases for Precision Agriculture

Fahmid Al Farid, Md Roman Bhuiyan, Farshad Badie, Duraisamy Balaganesh, Md. Mahbubur Rahman Tusher, Hezerul Abdul Karim

2025 Multimedia University Engineering Conference (MECON), 2025

DOI

Journal article

Federated learning meets few-shot learning: A voting ensemble based combined approach to cauliflower leaf disease classification across non-iid data distributions

MD. Zahin Muntaqim, Tangin Amir Smrity, Hasan Muhammad Kafi, Abu Saleh Musa Miah, Fahmid Al Farid, Hezerul Abdul Karim, Anichur Rahman

Array, 2025

DOI

Journal article

Leafsightx: an explainable attention-enhanced cnn fusion model for apple leaf disease identification

Md. Ehsanul Haque, Fahmid Al Farid, Md. Kamrul Siam, Md. Nurul Absur, Jia Uddin, Hezerul Abdul Karim

Frontiers in Artificial Intelligence, 2025

DOI

Journal article

Leafsightx: Multi-head attention-driven densenet201 and inceptionv3 feature fusion for enhanced accuracy and transparent apple leaf disease detection

ME Haque, F Al Farid, MK Siam, MN Absur, J Uddin, HA Karim

Frontiers in Artificial Intelligence 8, 1689865, 2025

Paper

Book chapter

A Deep Learning Approach to Detect Adulterated Milled Rice Kernels of Bangladeshi Varieties

Md. Monirul Islam Akash, Machbah Uddin, Md. Rakib Hassan, Muhammad Mustagis Billah, Fahmid Al Farid

Lecture Notes in Networks and Systems, 2024

DOI

Conference paper

A novel approach for tomato leaf disease detection and classification using deep learning

MN Uddin, TC Sarker, SI Sefat, S Akter, F Al Farid, MT Sarker, HA Karim, ...

2024 Multimedia University Engineering Conference (MECON), 1-6, 2024

Paper

Journal article

Improving wheat leaf disease classification: Evaluating augmentation strategies and cnn-based models with limited dataset

Syed Taha Yeasin Ramadan, Tanjim Sakib, Fahmid Al Farid, Md. Shofiqul Islam, Junaidi Bin Abdullah, Md Roman Bhuiyan, Sarina Mansor, Hezerul Bin Abdul Karim

IEEE Access, 2024

DOI

Journal article

Optimized crop disease identification in Bangladesh: A deep learning and SVM hybrid model for rice, potato, and corn

Shohag Barman, Fahmid Al Farid, Jaohar Raihan, Niaz Ashraf Khan, Md. Ferdous Bin Hafiz, Aditi Bhattacharya, Zaeed Mahmud, Sadia Afrin Ridita, Md Tanjil Sarker, Hezerul Abdul Karim, Sarina Mansor

Journal of Imaging, 2024

DOI

Journal article

Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classification

Md Shofiqul Islam, Sunjida Sultana, Fahmid Al Farid, Md Nahidul Islam, Mamunur Rashid, Bifta Sama Bari, Noramiza Hashim, Mohd Nizam Husen

Sensors, 2022

DOI

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