In precision farming technology, the interest of the researchers has been focused on the applications of autonomous mobile robots for agricultural operations such as planting, inspection, spraying, and harvesting. However, each autonomous robot generally performs a single agricultural task. In this context, complete autonomy in precision farming can be achieved by using coordinated multi-robot systems that can easily and safely cooperate to accomplish agricultural tasks. The efficiency of the multi-robot system depends on the number of robots, the size of the robots, the distance between each robot, the instant location and heading angle of the robots, and the size of the farmland. This paper describes the development of wireless Robot to Robot (R2R) communication system architecture and the collision avoidance algorithm for multi-robot precision farming applications. The developed system uses the fusion of a digital compass and GPS receiver for wirelessly broadcasting the spatial and temporal data of the mobile robots through WiFi. In this study, WiFi broadcasting was chosen for reasons such as the advantages of long wireless signal range and strength, not being easily affected by weather and dust, low cost, and so on. The proposed system realizes the real-time wireless broadcasting of the mobile robot information for eliminating the collision of mobile robots and improving the level of safety management. The results show that the system has flexible, reliable, and adaptable solution, and thus can increase the efficiency of the multi-robot system in precision farming applications.

Early detection of pest damage and accurate assessment of fruit ripeness are essential for improving the quality, productivity, and sustainability of citrus production. Moreover, precisely assessing ripeness is crucial for establishing the optimal harvest time, preserving fruit quality, and enhancing yield. The simultaneous and precise early detection of pest damage and assessment of fruit ripeness greatly enhance the efficacy of contemporary agricultural decision support systems. This study presents a lightweight deep learning model based on an optimized YOLO12n-Seg architecture for the simultaneous detection of ripeness stages (unripe and fully ripe) and pest damage caused by Red Scale (Aonidiella aurantii). The model is based on an improved version of YOLO12n-Seg, where the backbone and head layers were retained, but the neck was modified with a GhostConv block to reduce parameter size and improve computational efficiency. Additionally, a Global Attention Mechanism (GAM) was incorporated to strengthen the model’s focus on target-relevant features and reduce background noise. The improvement procedure improved both the ability to gather accurate spatial information in several dimensions and the effectiveness of focusing on specific target object areas utilizing the attention mechanism. Experimental results demonstrated high accuracy on test data, with mAP@0.5 = 0.980, mAP@0.95 = 0.960, precision = 0.961, and recall = 0.943, all achieved with only 2.7 million parameters and a training time of 2 h and 42 min. The model offers a reliable and efficient solution for real-time, integrated pest detection and fruit classification in precision agriculture.

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

This paper proposes the angular correction algorithm for the autonomous navigation of the agricultural mobile robots, which are driven in a straight line, with simple hardware based on the data of the digital compass and the GPS receiver. The motion of the mobile robot was accomplished by the differential drive mechanism with four driving wheels in which the overall velocity is split between left and right wheels. The two-channel DC motor controller was used to drive motors. The digital compass was used to calculate the position angle of the mobile robot with respect to the target point. A Kalman filter was used to fuse the information from GPS and digital compass. In the proposed algorithm, the mobile robot is driven in a straight line along a predefined path by calculating in real time the deviation angle difference with respect to the target point. When the robot encounters an unexpected external force varying the desired path, it achieves a smooth and stabilized straight line movement by correcting the deviation angle difference. The performance of the mobile robot was evaluated based on a total of 18 straight lines in a farmland. Standard errors of cross track error (XTE) values of straight lines for each target point were analyzed. The mean of arithmetic means was found to be 4.14 cm. The mean of R-square was 0.990. This value shows that the proposed angular correction algorithm is useful in driving the mobile robot in a straight line. DOI: 10.61416/ceai.v25i3.8326

The integration of high precision orientation sensors into resilient communication infrastructures is a key requirement for next-generation autonomous systems. Digital compass sensors typically generate data in the NMEA format, whereas distributed control architectures in mobile robotics predominantly utilize the CAN 2.0B protocol. This study presents the design and implementation of a microcontroller-based hardware-software gateway that enables seamless interoperability between these heterogeneous communication standards within autonomous agricultural platforms. The proposed system operates as a protocol translation layer, acquiring orientation data (heading, pitch, and roll) from a digital compass via serial communication and converting it into CAN-compliant messages. The incoming NMEA sentences are parsed, validated, and filtered before being encapsulated into extended CAN frames. To address the payload limitations of Classical CAN and discrepancies in transmission rates, a multi-frame segmentation strategy and a synchronization mechanism based on handshaking are implemented. Additionally, buffering techniques are employed to ensure temporal consistency and prevent data loss during high-frequency data exchange. Experimental evaluations demonstrated that the proposed gateway achieved reliable real-time communication performance, with an average end to end communication latency of 19.6 ms, a 99.98% message reconstruction success rate, and 0.02% packet loss during continuous CAN Bus data transmission tests. The proposed integration framework effectively bridges the gap between legacy serial sensor protocols and modern CAN Bus-based distributed systems. Consequently, this work offers a scalable and practical solution for protocol interoperability in precision agriculture and contributes to the development of robust sensor integration strategies for autonomous field robotics.

A typical orchard's mechanical operation consists of three or four stages: lining, digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging the planting hole is the most time-consuming operation. In fruit orchards, robots' usage is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GIS), to develop an autonomous drilling machine and use the developed robot to open planting holes. There isn't a comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS software was used for designing and drawing of the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.