Excessive use of herbicides for weed control increases the cost of crop production and can lead to environmental degradation. An intelligent spraying system can apply agrochemicals on an as-needed basis by detecting and selectively targeting the weeds. The objective of this research was to investigate the feasibility of using deep convolutional neural networks (DCNNs) for detecting lamb’s quarters (Chenopodium album) in potato fields. Five potato fields were selected in Prince Edward Island (PEI) and New Brunswick (NB), Canada to collect images of spatially and temporally varied potato plants and lamb’s quarters. The image database included pictures, taken under varying growth stages of potato, outdoor light (clear, cloudy, and partly cloudy), and shadowy conditions. The images were trained for DCNN models, namely GoogLeNet, VGG-16, and EfficientNet to classify lamb’s quarters and potato plants. Performance of two frameworks, namely TensorFlow and PyTorch, were compared in training, testing, and during inferring the DCNNs. Results showed excellent performance of DCNNs in lamb’s quarters and potato plant classification (accuracy > 90%). However, the EfficientNet with PyTorch framework showed a maximum accuracy of (0.92–0.97) for every growth stage of the plants. Inference times of DCNNs were recorded using three graphics processing units (GPUs), namely Nvidia GeForce 930MX, Nvidia GeForce GTX1080 Ti, and Nvidia GeForce GTX1050. All the DCNNs performed better with PyTorch than TensorFlow frameworks. It was concluded that the trained models can be used in automation of the spraying systems for the site-specific application of agrochemicals for weed control in potato fields. Such precision agriculture technologies will ensure economically viable and environmentally safe potato cultivation.

过度使用除草剂控制杂草会增加作物生产成本，并可能导致环境退化。智能喷洒系统可以通过检测杂草并有针对性地将杂草按需施药。这项研究的目的是研究使用深度卷积神经网络（DCNN）检测羊羔的四分之一的可行性（Chenopodium album）在马铃薯田中。选择了加拿大爱德华王子岛（PEI）和新不伦瑞克（NB）的五个马铃薯田，以收集时空变化的马铃薯植株和羔羊四分之一的图像。图像数据库包括在马铃薯，室外光照（晴朗，多云和部分多云）和阴暗条件下不同生长阶段拍摄的照片。这些图像针对DCNN模型（即GoogLeNet，VGG-16和EfficientNet）进行了训练，以对羔羊的宿舍和马铃薯植物进行分类。在训练，测试和推断DCNN的过程中，比较了TensorFlow和PyTorch这两个框架的性能。结果表明，DCNNs在羊肉区和马铃薯植株分类中表现出色（准确性> 90％）。但是，带有PyTorch框架的EfficientNet显示最大精度为（0.92-0。97）用于植物的每个生长阶段。使用三个图形处理单元（GPU）记录了DCNN的推理时间，即Nvidia GeForce 930MX，Nvidia GeForce GTX1080 Ti和Nvidia GeForce GTX1050。使用PyTorch的所有DCNN都比TensorFlow框架表现更好。得出的结论是，经过训练的模型可以用于喷洒系统的自动化中，以用于农药在马铃薯田中除草的特定位置应用。这种精密农业技术将确保经济上可行和对环境安全的马铃薯种植。得出的结论是，经过训练的模型可以用于喷洒系统的自动化中，以用于农药在马铃薯田中除草的特定位置应用。这种精密农业技术将确保经济上可行和对环境安全的马铃薯种植。得出的结论是，经过训练的模型可以用于喷洒系统的自动化中，以用于农药在马铃薯田中除草的特定位置应用。这种精密农业技术将确保经济上可行和对环境安全的马铃薯种植。