Research in Agricultural Engineering - Online first
A spectral signature-based algorithm for the identifiability of crops and their cultivation conditions Original Paper
Sarah El Azizi, Halima Taia, Abdes-Samed Bernoussi, Mina Amharref, Edyta Wozniak
Res. Agr. Eng., X:X | DOI: 10.17221/163/2025-RAE 
Recent advancements in remote sensing techniques, especially the combination of hyperspectral imaging with analytical algorithms, have greatly improved precision agriculture. This study introduces some algorithms developed for identifying crops and evaluating their growth conditions, focusing on irrigation and fertilisation. The present approach is based on the concept of identifiability of a family of dynamic systems and the differentiation of plants using their spectral signatures. The method uses a repository of spectral data and applies a developed algorithm to compare...
Modelling the hydration process of wheat grain with layer-dependent diffusion coefficientsOriginal Paper
Bakhtiyar Ismailov, Abdushukur Urinboev, Khairulla Ismailov, Akmaljon Kuchkarov
Res. Agr. Eng., X:X | DOI: 10.17221/101/2025-RAE
This study develops and validates a multilayer diffusion model of wheat grain hydration that incorporates layer-dependent diffusion coefficients for bran, endosperm, and germ. The moisture transport is formulated using Fick’s law with two interface formulations: (i) classical continuity of the concentration and flux and (ii) an interlayer resistance formulation that permits concentration discontinuities. Diffusion coefficients and geometric parameters were determined experimentally; A 3D grain model (structured-light scanning, COMSOL Multiphysics) informed the computational domain. Numerical solutions combined...
A novel ensemble convolutional neural networksfor rice disease identificationOriginal Paper
Richard Alvin Pratama, Nabila Husna Shabrina
Res. Agr. Eng., X:X | DOI: 10.17221/59/2024-RAE
Rice is a crucial food commodity worldwide, particularly in Asian countries. However, various factors, such as drought, floods, and pest attacks, can lead to the emergence of diseases in rice plants. Accurately identifying these diseases poses a significant challenge for farmers, often leading to significant yield losses. Conventionally, farmers rely on manual methods based on their experience and visual inspections to identify rice diseases. However, this approach is highly ineffective, time-consuming, and prone to error. This study aimed to address this issue by proposing advanced deep learning techniques, an ensemble learning method, to automate...