AgriEngineering MDPI

17/12/2024

🌾The New Article is online: "Using Data-Driven Techniques to Improve Wheat Yield Prediction"

Authors: Merima Smajlhodžić-Deljo, Madžida Hundur Hiyari, Lejla Gurbeta Pokvić, Nejra Merdović, Faruk Bećirović, Lemana Spahić from Verlab Institute and Željana Grbović, Dimitrije Stefanović, Ivana Miličić, Oskar Marko from BioSense Institute

🖇️Read it in : https://bit.ly/49Fi8rg

Abstract: Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture.

Keywords: ; ; ; ; -timeprocessing;

🖇️Read it in : https://bit.ly/49Fi8rg

13/12/2024

🌱 New Article is online: "Development and Evaluation of a Laser System for Autonomous Weeding Robots"

Authors: Vitali Czymmek, Jost Völckner and Stephan Hussmann from Fachhochschule Westküste

🖇️Read it in : https://bit.ly/3OVK8xi

Abstract: Manual w**d control is becoming increasingly costly, necessitating the development of alternative methods. This work investigates the feasibility of using laser technology for autonomous w**d regulation. We developed a system utilizing a laser scanner to target and eliminate w**ds, which was first tested using a pilot laser for accuracy and performance. Subsequently, the system was upgraded with a high-power fiber laser. Experimental results demonstrated a high w**d destruction accuracy with real-time capabilities. The system achieved efficient w**d control with minimal environmental impact, providing a potential alternative for sustainable agriculture.

Keywords: ; **dcontrol; ; ;

🖇️Read it in : https://bit.ly/3OVK8xi

Manual w**d control is becoming increasingly costly, necessitating the development of alternative methods. This work investigates the feasibility of using laser technology for autonomous w**d regulation. We developed a system utilizing a laser scanner to target and eliminate w**ds, which was first t...

19/11/2024

🌟 AgriEngineering Outstanding Reviewer Award 🌟

Each year, we honor one exceptional reviewer for their dedication, professionalism, and timeliness in reviewing papers.

🏆 Prize: – CHF 500 – Free article processing voucher (valid 1 year) – Certificate

📅 Winner announcement: 31 March 2025

All reviewers from the past year are eligible—evaluated on the quality, quantity, and timeliness of reviews.

🖇 More information: https://bit.ly/4fwL5b3

13/11/2024

The AgriEngineering Reviewer Award in 2023 goes to Dr. Marija Simić for her outstanding contributions and insightful reviews.

A heartful thank you to all reviewers in 2023—your expertise and dedication are invaluable to the academic community!

23/10/2024

🌾🔧 AgriEngineering focuses on technologies, precision farming, automation, and innovative engineering solutions that enhance food production systems. 🚜🌍

🖇️ Check our Aim and Scope:

AgriEngineering, an international, peer-reviewed Open Access journal.

10/10/2024

🐖 New Article is online: "Proof-of-Concept Recirculating Air Cleaner Evaluation in a Pig Nursery"

Authors: Jackson O. Evans, MacKenzie L. Ingle, Junyu Pan, Himanth R. Mandapati, Praveen Kolar, Lingjuan Wang-Li and Sanjay B. Shah from NC State University

🖇️Read it in : https://bit.ly/3A1Lkvd

Abstract: Low ventilation rates used to conserve energy in pig nurseries in winter can worsen air quality, harming piglet health. A recirculating air cleaner consisting of a dust filter and ultraviolet C (UVC) lamps was evaluated in a pig nursery. It had a recirculation rate of 6.4 air changes per hour, residence time of 0.43 s, and UVC volumetric dose of 150 J·m−3. Reduced ventilation led to high particulate matter (PM) concentrations in the nursery. During the first 9 d, the air cleaner increased floor temperature in its vicinity by 1.9 °C vs. a more distant location. The air cleaner had average removal efficiencies of 29 and 27% for PM2.5 (PM with aerodynamic equivalent diameter or AED < 2.5 µm) and PM10 (PM with AED < 10 µm), respectively. It reduced PM2.5 and PM10 concentrations by 38 and 39%, respectively, in its vicinity vs. a more distant location. The air cleaner was mostly inconsistent in inactivating heterotrophic bacteria, but it eliminated fungi. It trapped 56% of the ammonia but did not trap nitrous oxide, methane, or carbon dioxide. The air cleaner demonstrated the potential for reducing butanoic, propanoic, and pentanoic acids. Design improvements using modeling and further testing are required.

Keywords: ; ; ; ; ; ; ;

🖇️Read it in : https://bit.ly/3A1Lkvd

Low ventilation rates used to conserve energy in pig nurseries in winter can worsen air quality, harming piglet health. A recirculating air cleaner consisting of a dust filter and ultraviolet C (UVC) lamps was evaluated in a pig nursery. It had a recirculation rate of 6.4 air changes per hour, resid...

27/09/2024

🚨 Volume 6, Issue 3 released

🚜 All 88 Articles published in AgriEngineering, Volume 6, Issue 3 (September 2024) are available in on:

🖇️ https://bit.ly/3MZWcfY

10/09/2024

📈 🍌 Read the most cited paper in AgriEngineering from the past two years: "An Improved Agro Deep Learning Model for the Detection of Panama Wilts Disease in Banana Leaves."

Authors: Ramachandran Sangeetha from Karunya Institute of Technology and Sciences - Deemed University, Jaganathan Logeshwaran from Sri Eshwar College Of Engineering, Javier Rocher Morant and Jaime Lloret Mauri from Universitat Politècnica de València UPV

🔗Read it : https://bit.ly/3Xzqf4q

Abstract: Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score.

Keywords: Panama wilts disease; ; accuracy; precision; recall; F1-score

🔗Read it : https://bit.ly/3Xzqf4q

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro....

02/09/2024

🌾🚜 Check out the most cited papers in AgriEngineering—available for download in Open Access!

Discover the innovations in farming and cutting-edge technology here:
🔗

AgriEngineering, an international, peer-reviewed Open Access journal.

21/08/2024

🤖 New Article is online: "Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments"

Authors: Vasileios Moysiadis, Lefteris Benos, George Karras, Dimitrios Kateris, Andrea Peruzzi, Remigio Berruto, Elpiniki Papageorgiou and Corresponding author Dionysis Bochtis from Εθνικό Κέντρο Έρευνας και Τεχνολογικής Ανάπτυξης

🖇 Read it in : https://bit.ly/4fVFLP0

𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭: In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety.

𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬: human–robot collaboration; natural communication framework; vision-based human activity recognition; situation awareness

🖇 Read it in : https://bit.ly/4fVFLP0

26/07/2024

🚨 Issue 2 released!

📜 All Articles published in AgriEngineering, Volume 6, Issue 2 (June 2024) are available in on:

🖇️ https://bit.ly/3YeVAdr

16/07/2024

🎉 Announcement! 🎉

🚜 AgriEngineering's CiteScore has just increased to 4.7, ranking it in the Q1 category for Horticulture! 📈🌽

🖇️ Check our indexing webpage: https://bit.ly/47SnD4p

02/07/2024

🎉 Exciting Announcement! 🎉

AgriEngineering has just received its second Impact Factor from the Web of Science, now increased to 3.0! 📈 🚜

🖇 Check our Indexing & Archiving: https://bit.ly/47SnD4p

19/06/2024

🇫🇮 Announcement 🇫🇮

🚨 We are pleased to inform you that AgriEngineering is now listed at Level 1 in JUFO, the Finnish journal ranking list!

🔗Check it on:

15/05/2024

🍎 New Article online: "Nighttime Harvesting of OrBot (Orchard RoBot)"

Authors: Jakob Waltman, Ethan Buchanan and Duke Bulanon from Northwest Nazarene University

🖇️ Read it in : https://www.mdpi.com/2780910

Abstract: The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 s. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted in images with the lowest noise. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94% compared to 88% during the daytime operations.

Keywords: ; ;

🖇️ Read it in : https://www.mdpi.com/2780910

The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the targe...

30/04/2024

🚜 New Article: "Prediction of Noise Levels According to Some Exploitation Parameters of an Agricultural Tractor: A Machine Learning Approach"

Authors: Željko Barač, Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić and Monika Marković from Sveučilište Josipa Jurja Strossmayera u Osijeku

🖇️ Read it in : https://www.mdpi.com/2752032

Abstract: The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further.

Keywords: ; ; ; ; ; ; ;

🖇️ Read it in : https://www.mdpi.com/2752032

The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machi...

23/04/2024

🔖 New Review is online: "Challenges of in Crop Production: A Review"

Authors: Jose Paulo Molin, Marcelo Chan and Eudocio Rafael Otavio da Silva from Laboratory of Precision Agriculture (LAP-USP/ESALQ)

🖇️ Read it in : https://www.mdpi.com/2739664

Abstract: Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems.

Keywords: ; ; ;

🖇️ Read it in : https://www.mdpi.com/2739664

Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communi...

05/04/2024

🚨 Issue 1, Volume 6 released!

📚 All Articles published in AgriEngineering, Volume 6, Issue 1 (March 2024) are available in on:

🖇️ https://www.mdpi.com/2624-7402/6/1

📜 The cover paper for this issue is: "Estimating Fuel Consumption of an Agricultural Robot by Applying Machine Learning Techniques during Seeding Operation"

By: Mahdi Vahdanjoo , René Gislum and Claus Grøn Sørensen from Aarhus Universitet

Abstract: The integration of agricultural robots in precision farming plays a pivotal role in tackling the pressing demands of minimizing energy usage, enhancing productivity, and maximizing crop yield to meet the needs of an expanding global population and depleting non-renewable resources. Evaluating the energy expenditure is vital when assessing agricultural machinery systems. Through the reduction of fuel consumption, operational costs can be curtailed while simultaneously minimizing the overall environmental footprint left by these machines. Accurately calculating fuel usage empowers farmers to make well-informed decisions about their farming operations, resulting in more sustainable and productive methods. In this study, the ASABE model was applied to predict the fuel consumption of the studied robot. Results show that the ASABE model can predict the fuel consumption of the robot with an average error equal to 27.5%. Moreover, different machine-learning techniques were applied to develop an effective and novel model for estimating the fuel consumption of an agricultural robot. The proposed GPR model (gaussian process regression) considers four operational features of the studied robot: total operational time, total traveled distance, automatic working distance, and automatic turning distance. The GPR model with four features, considering hyperparameter optimization, showed the best performance (R-squared validation = 0.93, R-squared test = 1.00) among other models. Furthermore, three different ML methods (gradient boosting, random forest, and XGBoost) were considered in this study and compared with the developed GPR model. The results show that the GPR model outperformed the mentioned models. Moreover, the one-way ANOVA test results revealed that the predicted values from the GPR model and observation do not have significantly different means. The results of the sensitivity analysis show that the traveled distance and the total time have a significant correlation with the fuel consumption of the studied robot.

Keywords: ; ; ;

🖇️ https://www.mdpi.com/2624-7402/6/1