Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agricultural tasks. The ability of automatic feature extraction creates an adaptive nature in deep learning (DL), specifically convolutional neural networks to achieve human-level accuracy in various agricultural applications, prominent among which are plant disease detection and classification, weed/crop discrimination, fruit counting, land cover classification, and crop/plant recognition. This review presents the performance of recent uses in agricultural robots by the implementation of ML and DL algorithms/architectures during the last decade. Performance plots are drawn to study the effectiveness of deep learning over traditional machine learning models for certain agricultural operations. The analysis of prominent studies highlighted that the DL-based models, like RCNN (Region-based Convolutional Neural Network), achieve a higher plant disease/pest detection rate (82.51%) than the well-known ML algorithms, including Multi-Layer Perceptron (64.9%) and K-nearest Neighbour (63.76%). The famous DL architecture named ResNet-18 attained more accurate Area Under the Curve (94.84%), and outperformed ML-based techniques, including Random Forest (RF) (70.16%) and Support Vector Machine (SVM) (60.6%), for crop/weed discrimination. Another DL model called FCN (Fully Convolutional Networks) recorded higher accuracy (83.9%) than SVM (67.6%) and RF (65.6%) algorithms for the classification of agricultural land covers. Finally, some important research gaps from the previous studies and innovative future directions are also noted to help propel automation in agriculture up to the next level.

最近，农业在人工智能技术和机器人系统的自动化方面引起了很多关注。特别地，随着机器学习（ML）概念的进步，在农业任务中已观察到显着的进步。自动特征提取的能力为深度学习（DL）（特别是卷积神经网络）创造了适应性，可在各种农业应用中达到人为水平的准确性，其中突出的领域包括植物病害检测和分类，杂草/作物鉴别，水果计数，土地覆盖分类和作物/植物识别。这篇综述通过最近十年中ML和DL算法/体系结构的实施，展示了最近在农业机器人中的使用性能。绘制了性能图，以研究深度学习相对于某些农业运营而言相对于传统机器学习模型的有效性。对著名研究的分析强调，基于DL的模型（如RCNN（基于区域的卷积神经网络））比包括多层感知器在内的著名ML算法实现更高的植物病虫害检测率（82.51％）。 （64.9％）和K近邻（63.76％）。著名的名为ResNet-18的DL体系结构获得了更准确的“曲线下面积”（94.84％），并且优于基于ML的技术，包括随机森林（RF）（70.16％）和支持向量机（SVM）（60.6％）。作物/杂草的歧视。另一个称为FCN（完全卷积网络）的DL模型记录的准确性（83.9％）高于SVM（67.6％）和RF（65。6％）的农业用地覆盖分类算法。最后，还指出了以前研究中的一些重要研究空白和创新的未来方向，以帮助将农业自动化推进到新的水平。