In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)—the general mechanisms affecting plant processes—has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.

In soybeans, off-target damage from the use of dicamba and 2,4-D herbicides for broadleaf weed control can significantly impact sensitive vegetation and crops. The early detection and assessment of such damage are critical for plant diagnostic labs and regulatory agencies to inform regulated usage policies. However, the existing technologies that calculate the average spectrum often struggle to detect and differentiate the damage caused by these herbicides, as they share a similar mode-of-action. In this study, a high-precision spatial and spectral imaging solution was tested for the early detection of dicamba and 2,4-D-induced damage in soybeans. A 2021 study was conducted using LeafSpec, a touch-based hyperspectral leaf scanner, to detect damage on soybean leaves. VIS-NIR (visible–near infrared) hyperspectral images were captured from 180 soybean plants exposed to nine different herbicide treatments at different intervals after spraying. Leaf damage was distinguished as early as 2 h after treatment (HAT) using pairwise partial least squares discriminant analysis (PLS-DA) models based on spectral data. Leaf color distribution, texture, and morphological features were analyzed to separate herbicide dosages. By fully exploiting the spatial and spectral information from high-resolution hyperspectral images, classification accuracy was improved from 57.4% to over 80% for all evaluation dates. This work demonstrates the potential and advantages of using spectral and spatial features of LeafSpec hyperspectral images for the early and accurate detection of herbicide damage in soybean plants.

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging methods, plant phenotyping enables the precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.

Spectral imaging has been widely applied in plant phenotyping to assess corn leaf nitrogen status. Recent studies indicate that spatial variations within a single leaf’s multispectral image provide stronger signals for corn nitrogen estimation. However, current technologies for corn multispectral imaging cannot capture a large corn leaf segment with high-resolution and simple operation, limiting their efficiency and accuracy in nitrogen estimation. To address this gap, this study developed a proximal multispectral imaging device that can capture high-resolution snapshot multispectral images of a large segment of a single corn leaf. This device uses airflow to autonomously position and flatten the leaf to minimize the noise in images due to leaf curvature and simplify operation. Moreover, this device adopts a transmittance imaging regime by clamping the corn leaf between the camera and the lighting source to block the environmental lights and supply uniform lighting to capture high-resolution and high-precision leaf images within six seconds. A field assay was conducted to validate the effectiveness of the multispectral images captured by this device in assessing nitrogen status by classifying the nitrogen treatments applied to corn. Six nitrogen treatments were applied to 12 plots of corn fields, and 10 images were collected at each plot. By using the average vegetative index of the whole image, only one treatment was significantly different from the other five treatments, and no significant difference was observed among any other groups. However, by extracting the spatial and spectral features from the images and combining these features, the accuracy of nitrogen treatment classification improved compared to using the average index. In another analysis, by applying spatial–spectral analysis methods to the images, the nitrogen treatment classification accuracy has improved compared to using the average index. These results demonstrated the advantages of this high-resolution and high-throughput imaging device for distinguishing nitrogen treatments by facilitating spatial–spectral combined analysis for more precise classification.

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging, plant phenotyping enables precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.