Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduce AgriNet models, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19 achieved the highest classification accuracy of 94 % and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model.Finally, experiments to evaluate of superiority of AgriNet models compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir.

翻译：深层次学习和转让学习的进展为农业中各种自动化分类任务铺平了道路,包括植物疾病、病虫害、杂草和植物物种检测;然而,农业自动化仍面临各种挑战,例如数据集规模有限,缺乏具体的植物域域专用先行模型。Done 特定预先培训模型显示了各种计算机愿景任务中的先进性能,包括面部识别和医学成像诊断。在本文中,我们提议建立AgriNet数据集,收集来自19个以上地理位置的160k农业图像,若干图像说明装置,以及423种植物物种和疾病的类别。我们还采用了AgriNet模型,这是一套关于五种图像网结构的预先训练模型:VGG16、VGG19、Inception-V3、InceptionionResNet-V2和Xception。AgriNet-VGG19实现了94 % 的最高分类准确度和92%最高的F1核心。此外,所有拟议的模型都用于准确分类423类植物物种、疾病、虫害和423种植物物种和疾病的类别。我们还采用了AgriNet模型模型,这是对87%的模型和孟加拉国的模型数据进行了两张的模型的模型。