Agricultural robots play a crucial role in ensuring the sustainability of agriculture. Fruit detection is an essential part of orange-harvesting robot design. Ripe oranges need to be detected accurately in an orchard so they can be successfully picked. Accurate fruit detection in the orchard is significantly hindered by the challenges posed by the illumination and occlusion of fruit. Hence, it is important to detect fruit in a dynamic environment based on real-time data. This paper proposes a deep-learning convolutional neural network model for orange-fruit detection using a universal real-time dataset, specifically designed to detect oranges in a complex dynamic environment. Data were annotated and a dataset was prepared. A Keras sequential convolutional neural network model was prepared with a convolutional layer-activation function, maximum pooling, and fully connected layers. The model was trained using the dataset then validated by the test data. The model was then assessed using the image acquired from the orchard using Kinect RGB-D camera. The model was then run and its performance evaluated. The proposed CNN model shows an accuracy of 93.8%, precision of 98%, recall of 94.8%, and F1 score of 96.5%. The accuracy was mainly affected by the occlusion of oranges and leaves in the orchard’s trees. Varying illumination was another factor affecting the results. Overall, the orange-detection model presents good results and can effectively identify oranges in a complex real-time environment, like an orchard.
One of the most challenging areas in robot design is its kinematic analysis for proper and efficient path planning. In agricultural robots, this study is even more crucial due to the uneven terrain and unstructured environment. In agricultural robots, work has been done on fruit harvesting robots yet its commercial recognition is still underway. Further research needs to be done in this field to make the fruit harvesting robots more commercially acceptable. In this paper, a 6 degree of freedom (DOF) orange harvesting robot is designed and its kinematic analysis is done. Forward kinematic is done using Denavit-Hartenberg (DH) parameters while the inverse kinematics is done using algebraic method. The calculated formulas are verified by simulation on RoboAnalyzer software. The algorithm for inverse kinematics using probabilistic approach did not generate any error and worked successfully generating 16 results within the workspace. The simulated dynamic results also supported the kinematic model. The kinematic study validates the model design and calculations, whereas, its simulation verifies the path planning and reachability of oranges on the trees within the confined workspace.
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