Modeling PPFD propagation and transmittance in ePAR spectrum for canopy and intra-canopy adaptive greenhouse lighting - Cristian Bua; Yibin Wang; Mike O. Ojo; Al Bashir; Stefano Giordano; Azlan Zahid (Computers and Electronics in Agriculture, 2026)

Deep learning-based classification of nitrate and nitrite concentrations from water samples using colorimetric test strip images - Muhammad Roman; Mazhar Sher; Chamika Kuruppuarachchi; Arshid Ali; Chulwoo Pack; Azlan Zahid; Ali Mirzakhani Nafchi (Environmental Monitoring and Assessment, 2026)
Accurate monitoring of nitrate and nitrite concentrations in water is essential for sustainable agriculture, safeguarding public health, and protecting aquatic ecosystems from nutrient pollution. Traditional methods for detecting nitrate and nitrite in water samples are precise but costly, complex, and time-consuming, limiting their practicality for frequent on-site testing. This research proposes deep learning-based computer vision techniques to classify nitrate and nitrite concentrations using images of colorimetric test strips. An RGB IMX219 camera was used to acquire images of colorimetric test strips under standardized, controlled illumination conditions to ensure consistent image quality. A total of 1938 nitrate images and 1190 nitrite images were collected before augmentation. After preprocessing and training-only data augmentation, both classical machine learning baselines based on hand-crafted color and texture features and deep learning models—including a multilayer perceptron (MLP) and convolutional neural networks (AlexNet, VGG16, ResNet18, and GoogLeNet)—were trained and evaluated using an independent test set and stratified fivefold cross-validation. For nitrate classification, ResNet18 and GoogLeNet achieved near-perfect 100% test accuracy, with mean cross-validation accuracy of 99.97% ± 0.04%, substantially outperforming classical baseline models based on hand-crafted color and texture features, which achieved at most 83.5% test accuracy. For nitrite classification, GoogLeNet achieved the strongest overall performance, with a test accuracy of 97.48% and a fivefold cross-validation accuracy of 95.22% ± 1.17%, substantially outperforming the best classical baseline model, which achieved a maximum test accuracy of 83.19%. These results demonstrate that deep CNN-based feature learning provides a significant performance advantage over simpler methods under controlled imaging conditions, supporting the suitability of the proposed system for rapid, image-based water quality assessment and motivating future evaluation under broader real-world deployment scenarios.

Development of an AI-Enabled Vision-Based On-Site Rapid Detection of Nitrates and Nitrites from Drainage Samples - Muhammad Roman; Azlan Zahid; Ali Mirzakhani Nafchi; Mazhar Sher (Water, Air, & Soil Pollution, 2026)
Plant growth is significantly impacted by the concentration of nutrients in the soil. Accurate and real-time measurement of nitrates and nitrites levels remains a significant challenge. Existing laboratory-based methods are expensive, time-consuming, and labor-intensive, highlighting the need for a rapid, on-site solution. This study proposes a rapid method for measuring nitrates and nitrite levels in water samples collected from tile drainage. We utilized Hach Nitrate and Nitrite test strips for image dataset collection as these test strips change color based on the concentration of nitrate and nitrite in the water samples. A purpose-built black box, equipped with an internal lighting arrangement for imaging test strips, was designed to collect images of the test strips. Unlike many existing smartphone-based colorimetric approaches, which are sensitive to ambient lighting variations and often rely on external calibration or offline analysis, the proposed system integrates a controlled illumination environment with real-time edge computation for robust on-site detection of nitrate and nitrite. An Nvidia Orin Nano module, connected with an IMX219 camera sensor, was used to capture images of the test strips. Image preprocessing was performed, followed by the implementation of a VGG16-based network for feature extraction. A dataset of approximately 3128 images spanning multiple nitrate and nitrite concentration levels was collected under controlled imaging conditions. Multiple machine learning models including logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), naïve Bayes (NB), and random forest (RF) were evaluated for classification. The nitrate detection using KNN achieved an accuracy of 99.96% training, 99.92% testing, and 99.79% cross-validation. For nitrite detection, the SVM model achieved accuracy of 99.02%, 98.04%, and 98.07% for training, testing, and cross-validation, respectively, demonstrating both systems' high reliability and practical applicability for real-time monitoring. The total system cost is approximately USD 300, highlighting the affordability and practicality of the proposed solution for on-site water quality monitoring. This technology can enable farmers, water quality researchers, and agronomists to efficiently monitor the levels of nitrates and nitrites in tile drainage samples, enabling data-driven decisions to maximize crop yields.

Advancements in plant wearable sensors - Chamika Kuruppuarachchi; Fnu Kulsoom; Hussam Ibrahim; Hamid Khan; Azlan Zahid; Mazhar Sher (Computers and Electronics in Agriculture, 2025)

Flexible temperature and humidity sensors of plants for precision agriculture: Current challenges and future roadmap - Muhammad Ikram; Sikander Ameer; Fnu Kulsoom; Mazhar Sher; Ashfaq Ahmad; Azlan Zahid; Young Chang (Computers and Electronics in Agriculture, 2024)

Estimating hydroponic lettuce phenotypic parameters for efficient resource allocation - Mike O. Ojo; Azlan Zahid; Joseph G. Masabni (Computers and Electronics in Agriculture, 2024)

A critical review on efficient thermal environment controls in indoor vertical farming - Md Shamim Ahamed; Muhammad Sultan; Danielle Monfet; Md Sazan Rahman; Ying Zhang; Azlan Zahid; Muhammad Bilal; T.M. Abir Ahsan; Yasmine Achour (Journal of Cleaner Production, 2023)

Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management - Md Sultan Mahmud; Long He; Azlan Zahid; Paul Heinemann; Daeun Choi; Grzegorz Krawczyk; Heping Zhu (Computers and Electronics in Agriculture, 2023)

Climate change adaptation strategies for sustainable water management in the Indus basin of Pakistan - Muhammad Muzammil; Azlan Zahid; Umar Farooq; Naeem Saddique; Lutz Breuer (Science of The Total Environment, 2023)

Essential oil composition of Alseodaphne perakensis (Gamble) Kosterm from Malaysia - Muhammad Zul Afiq Anuar; Wan Mohd Nuzul Hakimi Wan Salleh; Shamsul Khamis; Mohd Azlan Nafiah; Zahid Mat Said (Natural Product Research, 2021)