Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. Therefore, in this study, we approach the use of Histogram Filters (Bayesian Discrete Filters) to estimate the position of tomatoes in the tomato plant. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.

翻译：在农业作业如水果监测或采摘中，需要感知目标物体的空间位置。由于光照干扰，RGB-D相机在开放田间环境中存在局限性。因此，本研究采用直方图滤波（贝叶斯离散滤波）来估计番茄植株中番茄的位置。研究分析了两种核滤波方法：方形核和高斯核。所实现的算法分别在无噪声、含高斯噪声与随机噪声的仿真环境及实验室条件下进行了测试。实验结果表明，在约0.5米评估距离下，算法在仿真环境中的平均绝对误差低于10毫米，在实验室测试平台中低于20毫米。因此，该结果在实际环境中具有可行性，且可通过缩短距离进一步提高精度。