AgriEngineering MDPI

01/12/2025

🚨 Volume 7, Issue 11 released!

📊 All 41 Articles published in AgriEngineering, Volume 7, Issue 10 (November 2025) are available online.

🖇️ Read it in : https://shorturl.at/zI0rh

20/11/2025

📊 AgriEngineering has maintained its Impact Factor of 3.0 (WoS, 2024).

The stability of the IF reflects the consistent contributions of our authors, reviewers, and editors.

🖇️ Check our Indexing: https://shorturl.at/CZKED

14/11/2025

🚨 Volume 7, Issue 10 released!

🚜 All 43 Articles published in AgriEngineering, Volume 7, Issue 10 (October 2025) are available in on:

🖇️ https://shorturl.at/CCEvE

20/10/2025

🚁 Recent publication: "SVMobileNetV2: A Hybrid and Hierarchical CNN-SVM Network Architecture Utilising UAV-Based Multispectral Images and IoT Nodes for the Precise Classification of Crop Diseases"

Authors: Rafael Linero-Ramos, Carlos Parra-Rodríguez and Mario Gongora

🖇️ Read it in : https://www.mdpi.com/3536802

Abstract: This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images.

Keywords: multispectral ; ; ; ; Convolutional Neural Network; Support Vector Machines; plant disease detection

Read it in : https://www.mdpi.com/3536802

15/10/2025

🇷🇸 Proud to have participated in the 7th International Symposium on Agricultural Engineering in Belgrade, Serbia.

✨ Exciting conversations and new collaborations ahead!

👉 https://www.mdpi.com/about/announcements/13396

15/10/2025

✨ Meet Us at CANVAS 2025!
📍 Salt Lake City, USA
📅 9–12 November 2025

Visit our booth #530 & connect with us and other MDPI journals Agronomy, Horticulturae, Plants, Agriculture, Crops, JoF, Land and Sustainability.

🖇️ https://www.mdpi.com/journal/agriEngineering/announcements/13534

26/09/2025

📊 AgriEngineering has maintained its CiteScore of 4.7 (Scopus, 2024).

A steady score reflects the consistent quality of work by our authors, reviewers, and editors.

Check our website at:🔗 https://shorturl.at/SNjBX

03/09/2025

🚨 New Special Issue is online: "Agricultural and Biosystems Engineering for Implementing the Circular Economy Concept in Agriculture"

Guest Editors: Pietro Picuno, Salvatore Margiotta and Roberto Puglisi from Università degli Studi della Basilicata

🖇️ More information: https://shorturl.at/gaT1p

Dear Colleagues,

Agricultural and Biosystems Engineering provides agricultural actors with the knowledge and skills required to use advanced technologies that can transform waste into new resources, thus contributing to the implementation of the Circular Economy concept in agriculture. Indeed, by managing organic (biomass) and non-organic (mostly, agro-plastics) waste, farmers can increase their economic returns while reducing the environmental footprint of agriculture. This Special Issue reports the results of scientific research and training activities carried out by Agricultural Engineers—mostly those implemented within the TANGO-Circular Project, in which farmers and agricultural stakeholders were trained on the appropriate use, post-consumer collection, and recycling of agricultural co-products, by-products, residuals, and waste. Their upskilling allowed them to play a proactive role in the valorization of agricultural waste, which was consolidated thanks to the participation of other interested actors involved in the Quadruple-Helix, including public institutions, private industries, universities/research centers, and civil society/non-profit organizations. During the Project Final Conference, held in Matera, Italy, on 25–27 June 2025, several European universities specializing in Agricultural and Biosystems Engineering presented data on the latest technologies and systems for valorizing agricultural waste, discussing on how to implement and harmonize them, thus shading light on cutting-edge technological solutions and creating innovative opportunities in agricultural waste valorization.

🖇️ More information: https://shorturl.at/gaT1p

27/08/2025

🌱 New Article: "Automated -Based Monitoring of Industrial H**p in Using Open-Source Systems and "

Authors: Carmen Rocamora Osorio, Fernando Aragón Rodríguez, Ana Maria Codes Alcaraz and Francisco-Javier Ferrández-Pastor

📢 "We've developed an open-source computer vision system to monitor greenhouse h**p! It uses sensors and cameras to track growth and it detects water stress with 97% accuracy."

🖇️ Read it in : https://bit.ly/3HFEv6B

Abstract: Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring h**p (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automation software installed on a single-board computer. It integrates various temperature and humidity sensors and surveillance cameras, automating image capture. H**p seeds of the Tiborszallasi variety were sown. After germination, plants were transplanted into pots. Five specimens were selected for growth monitoring by image analysis. A surveillance camera was placed in front of each plant. Different approaches were applied to analyse growth during the early stages: two traditional computer vision techniques and a deep learning algorithm. An average growth rate of 2.9 cm/day was determined, corresponding to 1.43 mm/°C day. A mean MAE value of 1.36 cm was obtained, and the results of the three approaches were very similar. After the first growth stage, the plants were subjected to water stress. An algorithm successfully identified healthy and stressed plants and also detected different stress levels, with an accuracy of 97%. These results demonstrate the system’s potential to provide objective and quantitative information on plant growth and physiological status.

🖇️ Read it in : https://bit.ly/3HFEv6B

Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring h**p (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automa...

25/07/2025

🚨 Volume 7, Issue 6 released!

🚜 All 35 Articles published in AgriEngineering, Volume 7, Issue 6 (June 2025) are available in on:

🖇️ https://bit.ly/45d55vI