Nanotechnology offers a viable solution to enhancing agricultural sustainability by supporting seed germination and crop growth. Besides augmenting crop yields and mitigating the effects of abiotic stresses on crops, nanoparticles (NPs) can also serve as carriers to amplify the effectiveness of fertilizers and pesticides by facilitating their gradual or highly targeted release. However, application of NPs in agricultural systems can result in interaction with crops and pave the way for transfer along the food chain, which raises concerns for consumers. To address these matters, this article provides a comprehensive review of the tracking of various NPs delivered to crops through different uptake pathways. Three types of NPs (including inorganic, carbon-based, and organic NPs) are highlighted for uptake, transport, and their impact on crop growth in agricultural crops. NPs can enter the plant via seeds, roots, and leaves, while the translocation of NPs mainly involves the apoplastic and symplastic pathways. The influence of NPs on plant growth highly depends on the specific plant species as well as the type, size, charge, and concentration of NPs. NPs emit fluorescence through inherent photoluminescence or in combination with fluorescent dyes, which enables monitoring of their distribution within the crop. The benefits and challenges of NPs are discussed, establishing signposts for future research that will enable NPs to contribute to precise and sustainable agriculture.

Weeds in the field affect the normal growth of lettuce crops by competing with them for resources such as water and sunlight. The increasing costs of weed management and limited herbicide choices are threatening the profitability, yield, and quality of lettuce. The application of intelligent weeding robots is an alternative to control intra-row weeds. The prerequisite for automatic weeding is accurate differentiation and rapid localization of different plants. In this study, a squeeze-and-excitation (SE) network combined with You Only Look Once v5 (SE-YOLOv5x) is proposed for weed-crop classification and lettuce localization in the field. Compared with models including classical support vector machines (SVM), YOLOv5x, single-shot multibox detector (SSD), and faster-RCNN, the SE-YOLOv5x exhibited the highest performance in weed and lettuce plant identifications, with precision, recall, mean average precision (mAP), and F1-score values of 97.6%, 95.6%, 97.1%, and 97.3%, respectively. Based on plant morphological characteristics, the SE-YOLOv5x model detected the location of lettuce stem emerging points in the field with an accuracy of 97.14%. This study demonstrates the capability of SE-YOLOv5x for the classification of lettuce and weeds and the localization of lettuce, which provides theoretical and technical support for automated weed control.

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.