AgriEngineering MDPI

🚨 Issue 1, Volume 6 released! 📚 All Articles published in AgriEngineering, Volume 6, Issue 1 (March 2024) are available in #OpenAccess on: 🖇️ https://lnkd.in/d_RSSrNb 📜 The cover paper for this issue is: "Estimating Fuel Consumption of an Agricultural Robot by Applying Machine Learning Techniques during Seeding Operation" Authors: Mahdi Vahdanjoo , René Gislum and Claus Grøn Sørensen from Aarhus University 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: #machinelearning; #fuelconsumption; #robotics; #gaussianprocessregression 🖇️ https://lnkd.in/d_RSSrNb

🐖 New Article is online: "Proof-of-Concept Recirculating Air Cleaner Evaluation in a Pig Nursery" Authors: Jackson Evans, MacKenzie L. Ingle, Junyu Pan, Himanth Mandapati, Praveen Kolar, Lingjuan Wang-Li and Sanjay B. Shah from North Carolina State University 🖇️Read it in #OpenAccess: 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: #ammonia; #bioburden; #fluence; #greenhousegas; #particulatematter; #Photocatalyst; #thermalstratification; #VolatileOrganicCompounds 🖇️Read it in #OpenAccess: https://bit.ly/3A1Lkvd

🚨 Volume 6, Issue 3 released 🚜 All 88 Articles published in AgriEngineering, Volume 6, Issue 3 (September 2024) are available in #OpenAccess on: 🖇️ https://lnkd.in/dQEm_k4V

📈 🍌 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, Jaganathan Logeshwaran from Sri Eshwar College of Engineering, Javier Rocher Morant and Jaime Lloret Mauri from Universitat Politècnica de València (UPV) 🔗Read it #OpenAccess: https://lnkd.in/ddUtMx9T 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; #deeplearning; accuracy; precision; recall; F1-score 🔗Read it #OpenAccess: https://lnkd.in/ddUtMx9T

🌾🚜 Check out the most cited papers in AgriEngineering—available for download in Open Access! Discover the innovations in farming and cutting-edge technology here: 🔗 https://lnkd.in/dcCxUneZ

🤖 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 Centre for Research & Technology Hellas (CERTH) 🖇 Read it in #OpenAccess: https://lnkd.in/dkzCeHr4 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 #OpenAccess: https://lnkd.in/dkzCeHr4

🚨 Issue 2 released! 📜 All Articles published in AgriEngineering, Volume 6, Issue 2 (June 2024) are available in #OpenAccess on: 🖇️ https://lnkd.in/dWhT_ahz

🎉 Announcement! 🎉 🚜 AgriEngineering's CiteScore has just increased to 4.7, ranking it in the Q1 category for Horticulture! 📈🌽 🖇️ Check our indexing webpage: https://lnkd.in/dzsk2pTZ #AgriEngineering #ImpactFactor

🎉 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://lnkd.in/dzsk2pTZ #AgriEngineering #ImpactFactor

🇫🇮 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: https://lnkd.in/gDNU5ZrY