More sustainable technologies in agriculture are important not only for increasing crop yields, but also for reducing the use of agrochemicals and improving energy efficiency. Recent advances rely on computer vision systems that differentiate between crops, weeds, and soil. However, manual dataset capture and annotation is labor-intensive, expensive, and time-consuming. Agricultural robots provide many benefits in effectively performing repetitive tasks faster and more accurately than humans, and despite the many advantages of using robots in agriculture, the solutions are still often expensive. In this work, we designed and built a low-cost autonomous robot (DARob) in order to facilitate image acquisition in agricultural fields. The total cost to build the robot was estimated to be around $850. A low-cost robot to capture datasets in agriculture offers advantages such as affordability, efficiency, accuracy, security, and access to remote areas. Furthermore, we created a new dataset for the segmentation of plants and weeds in bean crops. In total, 228 RGB images with a resolution of 704 × 480 pixels were annotated containing 75.10% soil area, 17.30% crop area and 7.58% weed area. The benchmark results were provided by training the dataset using four different deep learning segmentation models.

Nonlinear effects are broadly presents in several kinds of mechanical systems. Thus, it is necessary to use a suitable tool that becomes possible to characterize these nonlinearities in many situations. Volterra series can be useful for describing nonlinear systems through multiple convolutions. In this sense, the main goal of this work is to approximate the solutions of the motion equations using Volterra series in order to describe the nonlinear dynamical behavior of some mechanical benchmarks. Duffing oscillator, bilinear oscillator and a quadratically damped oscillator are analyzed to illustrate the efficiency, advantages and drawbacks of the proposed approach.

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