Precision Weed Management Research Articles

Performance evaluation of semi-supervised learning frameworks for multi-class weed detection.

Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality and optimizes crop yield but also provides a sustainable alternative to herbicide use. However, existing DL-based algorithms on weed detection are mainly developed based on supervised learning approaches, typically demanding large-scale datasets with manual-labeled annotations, which can be time-consuming and labor-intensive. As such, label-efficient learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision and have demonstrated promising performance. These methods aim to utilize a small number of labeled data samples along with a great number of unlabeled samples to develop high-performing models comparable to the supervised learning counterpart trained on a large amount of labeled data samples. In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS (Fully Convolutional One-Stage Object Detection) and Faster-RCNN (Faster Region-based Convolutional Networks). Specifically, we evaluate a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for the unlabeled data. To enhance generalization, an ensemble student network is employed to facilitate the training process. Experimental results show that the proposed approach is able to achieve approximately 76% and 96% detection accuracy as the supervised methods with only 10% of labeled data in CottonWeedDet3 and CottonWeedDet12, respectively. We offer access to the source code (https://github.com/JiajiaLi04/SemiWeeds), contributing a valuable resource for ongoing semi-supervised learning research in weed detection and beyond.

Field-based multispecies weed and crop detection using ground robots and advanced YOLO models: A data and model-centric approach

The implementation of a machine-vision system for real-time precision weed management is a crucial step towards the development of smart spraying robotic vehicles. The intelligent machine-vision system, constructed using deep learning object detection models, has the potential to accurately detect weeds and crops from images. Both data-centric and model-centric approaches of deep learning model development offer advantages depending on environment and non-environment factors. To assess the performance of weed detection in real-field conditions for eight crop species, the Yolov8, Yolov9, and customized Yolov9 deep learning models were trained and evaluated using RGB images from four locations (Casselton, Fargo, and Carrington) over a two-year period in the Great Plains region of the U.S. The experiment encompassed eight crop species—dry bean, canola, corn, field pea, flax, lentil, soybean, and sugar beet—and five weed species—horseweed, kochia, redroot pigweed, common ragweed, and water hemp. Six Yolov8 and eight Yolov9 model variants were trained using annotated weed and crop images gathered from four different sites including combined dataset from four sites. The Yolov8 and Yolov9 models’ performance in weed detection were assessed based on mean average precision (mAP50) metrics for five datasets, eight crop species, and five weed species. The results of the weed and crop detection evaluation showed high mAP50 values of 86.2 %. The mAP50 values for individual weed and crop species detection ranged from 80.8 % to 98 %. The results demonstrated that the performance of the model varied by model type (model-centric), location due to environment (data-centric), data size (data-centric), data quality (data-centric), and object size in the image (data-centric). Nevertheless, the Yolov9 customized lightweight model has the potential to play a significant role in building a real time machine-vision-based precision weed management system.

AI Driven Applications for Precision Weed Management in Agricultural Crops

Agriculture plays a most important role in our Indian economy and therefore lowering the cost of production and improving the quality of agricultural products is highly demanded. A weed is a plant which grows in wrong place at the wrong time and doing mostly harm than crops. Weed competes with the crops for water, light, nutrients and space, and therefore it prevents crop yields. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology to prevent these weeds. Machine learning technologies, are becoming crucial in agriculture to increase productivity, where advanced automation and control have been required. Based on large training datasets and pre-trained models, (Deep Learning) DL-based Convolutional Neural Networks (CNN) methods have proven to be more accurate than previous traditional techniques. Recently, Deep Learning (DL) has gained much attention due to its advantages in object detection, classification, and feature extraction. The system implementation of image processing technique for weed detection, a trained image is taken as a sample in order to demonstrate the difference between weed and the crop. Yolo frame work is used for annotate boundary boxes to the image with datasets. The effectiveness of the (You Only Live Once) YOLO-WEED system for real-time Unmanned Aerial Vehicle (UAV) weed detection, given its high speed and high accuracy in detection. After certain steps, we get desired output, where the weeds are separated from the crop that has been taken in the sample image. Key Words: DeepLearning,CNN, Unmanned Aerial Vehicle (UAV), Precision Weed Detection.

CottonWeeds: Empowering precision weed management through deep learning and comprehensive dataset

In sustainable crop production, effective weed management is crucial for agricultural productivity. Although deep learning techniques for weed detection have gained attention, their adoption is hindered by the lack of comprehensive datasets. Addressing this gap, we present ‘CottonWeeds,’ a meticulously curated database with 7578 images of horse purslane (Trianthema portulacastrum L.) and purple nutsedge (Cyperus rotundus L.), two common weed species in cotton (Gossypium hirsutum L.) fields. Captured under diverse conditions in the Indian subcontinent during the 2021 growth season, these labeled images constitute a valuable resource. We rigorously evaluated 11 state-of-the-art image classification models using deep transfer learning (DTL), achieving exceptional accuracy exceeding 93% within a 3-h training period. MobileNet emerged as the top performer, reaching 95.43% accuracy. Challenges arose with limited purple nutsedge training samples, impacting conventional models. To address this, we introduced a weighted cross-entropy loss function, significantly enhancing purple nutsedge classification. For instance, VGG16's purple nutsedge accuracy improved from 92.00% to 96.57%. Additionally, we assessed the dataset's utility for object detection, employing pre-trained YOLOv5 models. YOLOv5x exhibited exceptional performance, with a mAP_0.5:0.95 score of 72.5% and a mAP_0.5 score of 87.4%, proving invaluable for weed identification in cotton fields. Our study underscores CottonWeeds' potential as a foundational training platform for real-time, in-field weed recognition models.

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds. The precision, recall, F1-score, mAP0.5, mAP0.5:0.95 of You Only Look Once (YOLO) v5 for detecting cabbage were 0.986, 0.979, 0.982, 0.995, and 0.851, respectively, while these metrics were 0.973, 0.985, 0.979, 0.993, and 0.906 for YOLOv8, respectively. However, none of these metrics exceeded 0.891 when detecting weeds. The reduced performances for directly detecting weeds could be attributed to the diverse weed species at varying densities and growth stages with different plant morphologies. A segmentation procedure demonstrated its effectiveness for extracting weeds outside the bounding boxes covering the crops, and thereby realizing effective indirect weed detection. The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.

Genomic Insights into Seed Germination Differences in Buffalobur (Solanum rostratum Dunal) under Contrasting GA and ABA Availability

Buffalobur (Solanum rostratum Dunal) is an invasive species that seriously endangers crop production and the ecological environment. Seeds are the primary source of infestation; therefore, understanding the molecular basis of buffalobur seed dormancy, and germination is crucial for precision weed management. In this study, high-throughput RNA-Seq was performed on buffalobur seeds, which imbibed under 0.35 mmol/L giberellic acid (GA) and 0.35 mmol/L abscisic acid (ABA). In total, 3658 differentially expressed genes (DEGs) were identified during seed germination. Gene annotation revealed that the DEGs were significantly enriched during the protein metabolic process, as well as the macromolecular complex and cytoplasmic part for ABA versus GA. Pathway analysis predicted that the DEGs were associated with metabolic pathways, the biosynthesis of secondary metabolites and ribosome. Nine germination-related genes involved in the biosynthesis and metabolism of the phytohormones and encoding of the endo-β-mannanase (EBM) were identified. Gene expression indicated that GA upregulated GA3OX1 and MAN2 expression to increase the EBM activity, which caused the endosperm cap to weaken and lowered the puncture force to trigger the germination of buffalobur. The obtained results would be helpful to clarify the regulation of seed dormancy and the germination of buffalobur, and could serve as a valuable resource when unravelling the genetic basis of seed biology of this weed species.

An automated weed identification framework for sugarcane crop: A deep learning approach

Automatic weed identification using deep learning (DL) models will mark a revolution in developing site– specific artificial intelligence (AI) based herbicide sprayers which intend to maximize herbicide efficiency with reduced herbicide application in agriculture production systems and hence contribute to higher yields. Conventional weed control strategies pose challenges for integrating smart herbicide delivery and machinery systems. This deep learning approach significantly impacts developing a system for weed identification required in establishing successful real time precision weed management systems like smart spraying systems and AI based smart machinery. Minimal research has been done on automatic weed identification in sugarcane (Saccharum officinarum L.) cropping systems. This study analyzed the feasibility of a computer vision based DL approach for weed identification to achieve autonomous weed control. The image dataset containing 5660 augmented images was deployed to train and evaluate DL models with a spilt of 90% for training and the rest for validation. We trained the six DL models for identifying weeds in sugarcane crop using field imagery (5094 images), validated (566 images) and further evaluated their accuracy and F1 score performance. Model training was undertaken by varying the hyperparameters, such as mini batch size (16 and 32) and epoch (10, 20 and 30) at a learning rate of 0.001. DarkNet53 accomplished a high F1 score value (>99%) and outperformed other models (AlexNet, GoogLeNet, InceptionV3, ResNet50, and Xception) for the identification of weeds in actively growing sugarcane crop. Weeds can be identified with a higher level of confidence (>98%) with a minimum error rate (<1%) at the mini batch size of 16 and epochs 20. Post-training and validation, DarkNet53 was tested (stage I) with an independent 200-image dataset followed by stage II testing (30 images) and obtained 96.6% net accuracy compared with naked eye weed identification. Based on the high – level performance of DarkNet53, we conclude that DL based weed identification laid a potential future avenue with an effective decision system in the machine vision aspects of a precision herbicide applicator for weed control in sugarcane fields through a low cost camera integrated with a single board computer.

Black-grass (Alopecurus myosuroides) in cereal multispectral detection by UAV

AbstractSite-specific weed management (on the scale of a few meters or less) has the potential to greatly reduce pesticide use and its associated environmental and economic costs. A prerequisite for site-specific weed management is the availability of accurate maps of the weed population that can be generated quickly and cheaply. Improvements and cost reductions in unmanned aerial vehicles (UAVs) and camera technology mean these tools are now readily available for agricultural use. We used UAVs to collect aerial images captured in both RGB and multispectral formats of 12 cereal fields (wheat [Triticum aestivumL.] and barley [Hordeum vulgareL.]) across eastern England. These data were used to train machine learning models to generate prediction maps of locations of black-grass (Alopecurus myosuroidesHuds.), a prolific weed in UK cereal fields. We tested machine learning and data set resampling methods to obtain the most accurate system for predicting the presence and absence of weeds in new out-of-sample fields. The accuracy of the system in predicting the absence ofA. myosuroidesis 69% and its presence above 5 g in weight with 77% accuracy in new out-of-sample fields. This system generates prediction maps that can be used by either agricultural machinery or autonomous robotic platforms for precision weed management. Improvements to the accuracy can be made by increasing the number of fields and samples in the data set and the length of time over which data are collected to gather data across the entire growing season.

Instance segmentation method for weed detection using UAV imagery in soybean fields

Weed detection in crops is a new frontier of precision agriculture, which will enable the distinction between desirable and undesirable plants. Accurate and efficient weed detection and discrimination are fundamental for precision weed management. This study presents a novel approach combining the visible colour index with the instance segmentation method based on the encoder and decoder architecture. This approach tackles the challenge of accurately detecting and segmenting weeds amidst the dense distribution of weeds and soybean crops. The colour index was designed to highlight the contrast between plants and soil to mitigate the effects of illumination and background, while the integration of the ResNet101_v and DSASPP in the encoder-decoder architecture served to reinforce the extraction of abundant multi-scale semantic information to elevate the accuracy of weed patch boundary segmentation. Experimental results on real-world field imagery indicated that the proposed approach exhibited superior performance with an accuracy of 0.905 and an IoU score of 0.959 for weeds segmentation, an aAcc of 0.978, mIoU of 0.939 and a mAcc of 0.972 for overall performance. In comparison with Deeplabv3plus, Deeplabv3, FCN, U-net, FastFCN, Swin Transformer, and Vision Transformer, the proposed pipeline achieved comparable performance in terms of segmentation precision and execution time for differentiating between weeds and soybeans. This work represents a noteworthy contribution to the field of precision agriculture, offering a promising approach to the challenging task of weed detection and precision weed control.

A survey of weed research priorities: key findings and future directions

AbstractWe conducted an online survey of weed scientists in the United States and Canada to (1) identify research topics perceived to be important for advancing weed science in the next 5 to 10 years and (2) gain insight into potential gaps in current expertise and funding sources needed to address those priorities. Respondents were asked to prioritize nine broad research areas, as well as 5 to 10 subcategories within each of the broad areas. We received 475 responses, with the majority affiliated with academic institutions (55%) and working in cash crop (agronomic or horticultural) study systems (69%). Results from this survey provide valuable discussion points for policy makers, funding agencies, and academic institutions when allocating resources for weed science research. Notably, our survey reveals a strong prioritization of Cultural and Preventative Weed Management (CPWM) as well as the emerging area of Precision Weed Management and Robotics (PWMR). Although Herbicides remain a high-priority research area, continuing challenges necessitating integrated, nonchemical tactics (e.g., herbicide resistance) and emerging opportunities (e.g., robotics) are reflected in our survey results. Despite previous calls for greater understanding and application of weed biology and ecology in weed research, as well as recent calls for greater integration of social science perspectives to address weed management challenges, these areas were ranked considerably lower than those focused more directly on weed management. Our survey also identified a potential mismatch between research priorities and expertise in several areas, including CPWM, PWMR, and Weed Genomics, suggesting that these topics should be prime targets for expanded training and collaboration. Finally, our survey suggests an increasing reliance on private sector funding for research, raising concerns about our discipline’s capacity to address important research priority areas that lack clear private sector incentives for investment.

UAV Technology for Precision Weed Detection and Management

In India population increases rapidly day-by-day, therefore food demand also increases. Traditional farming is insufficient to meet these demands. Thus, Unmanned Aerial Vehicles (drones) were introduced, enabling Weed Management in less time, efficient use of herbicide and pesticide to bring sustainability to the environment and their resources. One of the most detrimental biotic variables in agriculture, weeds significantly reduces yields across the globe. Weed patches from crop fields can be identified by using drone sensors. Drone camera sensors can optimize weed in relation to leaf density, chlorophyll concentration and other plant canopy characteristics. The article emphasizes weed detection and management by the use of potential sensors of Unmanned Aerial Vehicles (drones) technology for better agriculture output.

Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value &lt; 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality.

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton

Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed management systems, but recent developments in neural networks have offered great prospects. However, a major limitation with the neural network models is the requirement of high volumes of data for training. The current study aims at exploring an alternative approach to the use of real images to address this issue. In this study, synthetic images were generated with various strategies using plant instances clipped from UAV-borne real images. In addition, the Generative Adversarial Networks (GAN) technique was used to generate fake plant instances which were used in generating synthetic images. These images were used to train a powerful convolutional neural network (CNN) known as "Mask R-CNN" for weed detection and segmentation in a transfer learning mode. The study was conducted on morningglories (MG) and grass weeds (Grass) infested in cotton. The biomass for individual weeds was also collected in the field for biomass modeling using detection and segmentation results derived from model inference. Results showed a comparable performance between the real plant-based synthetic image (mean average precision for mask-mAPm: 0.60; mean average precision for bounding box-mAPb: 0.64) and real image datasets (mAPm: 0.80; mAPb: 0.81). However, the mixed dataset (real image + real plant instance-based synthetic image dataset) resulted in no performance gain for segmentation mask whereas a very small performance gain for bounding box (mAPm: 0.80; mAPb: 0.83). Around 40–50 plant instances were sufficient for generating synthetic images that resulted in optimal performance. Row orientation of cotton in the synthetic images was beneficial compared to random-orientation. Synthetic images generated with automatically-clipped plant instances performed similarly to the ones generated with manually-clipped instances. Generative Adversarial Networks-derived fake plant instances-based synthetic images did not perform as effectively as real plant instance-based synthetic images. The canopy mask area predicted weed biomass better than bounding box area with R2 values of 0.66 and 0.46 for MG and Grass, respectively. The findings of this study offer valuable insights for guiding future endeavors oriented towards using synthetic images for weed detection and segmentation, and biomass estimation in row crops.

An Overview of Precision Weed Mapping and Management Based on Remote Sensing

Precision Agriculture face the challenge of feeding an increasing population, maximizing yield while optimizing inputs [...]

Precision Chemical Weed Management Strategies: A Review and a Design of a New CNN-Based Modular Spot Sprayer

Site-specific weed control offers a great potential for herbicide savings in agricultural crops without causing yield losses and additional weed management costs in the following years. Therefore, precision weed management is an efficient tool to meet the EU targets for pesticide reduction. This review summarizes different commercial technologies and prototypes for precision patch spraying and spot spraying. All the presented technologies have in common that they consist of three essential parts. (1) Sensors and classifiers for weed/crop detection, (2) Decision algorithms to decide whether weed control is needed and to determine a suitable type and rate of herbicide. Usually, decision algorithms are installed on a controller and (3) a precise sprayer with boom section control or single nozzle control. One point that differs between some of the techniques is the way the decision algorithms classify. They are based on different approaches. Green vegetation can be differentiated from soil and crop residues based on spectral information in the visible and near-infrared wavebands (“Green on Brown”). Those sensors can be applied for real-time on/off control of single nozzles to control weeds before sowing after conservation tillage and in the inter-row area of crops. More sophisticated imaging algorithms are used to classify weeds in crops (“Green on Green”). This paper will focus on Convolutional Neural Networks (CNN) for plant species identification. Alternatively, the position of each crop can be recorded during sowing/planting and afterward herbicides can be targeted to single weeds or larger patches of weeds if the economic weed threshold is exceeded. With a standardized protocol of data communication between sensor, controller and sprayer, the user can combine different sensors with different sprayers. In this review, an ISOBUS communication protocol is presented for a spot sprayer. Precision chemical weed control can be realized with tractor-mounted sprayers and autonomous robots. Commercial systems for both classes will be introduced and their economic and environmental benefits and limitations will be highlighted. Farmers ask for robust systems with less need for maintenance and flexible application in different crops.

Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems

Precision weed management offers a promising solution for sustainable cropping systems through the use of chemical-reduced/non-chemical robotic weeding techniques, which apply suitable control tactics to individual weeds or small clusters. Therefore, accurate identification of weed species plays a crucial role in such systems to enable precise, individualized weed treatment. Despite recent progress, the development of a robust weed identification and localization system in the presence of unstructured field environments remains a serious challenge, requiring supervised modeling using large volumes of annotated data. This paper makes a first comprehensive evaluation of deep transfer learning (DTL) for identifying common weed species specific to cotton (Gossypium hirsutum L.) production systems in southern United States (U.S.). A new dataset for weed identification was created, consisting of 5187 color images of 15 weed classes collected under natural light conditions and at varied weed growth stages, in cotton fields (primarily in Mississippi and North Carolina) during the 2020 and 2021 growth seasons. We evaluated 35 state-of-the-art deep learning models through transfer learning with repeated holdout validations and established an extensive benchmark for the considered weed identification task. DTL achieved high classification accuracy of F1 scores exceeding 95%, requiring reasonably short training time (less than 2.5 h) across models. ResNeXt101 achieved the best overall F1-score of 98.93 ± 0.34%, whereas 10 out of the 35 models achieved F1 scores near or above 98.0%. However, the performance on minority weed classes with few training samples was less satisfactory for models trained with a conventional, unweighted cross entropy loss function. To address this issue, a weighted cross entropy loss function was adopted, which achieved substantially improved accuracies for minority weed classes (e.g., the F1-scores for Xception and MnasNet on the Spurred Anoda weed increased from 48% to 90% and 50% to 82%, respectively). Furthermore, a deep learning-based cosine similarity metric was employed to analyze the similarity among weed classes, assisting in the interpretation of classifications. Both the codes (https://github.com/Derekabc/CottonWeeds) for model benchmarking and the weed dataset (https://www.kaggle.com/yuzhenlu/cottonweedid15) of this study are made publicly available, which expect to be a valuable resource for future research on weed identification and beyond.

Sustainable Approach to Weed Management: The Role of Precision Weed Management

In the last few decades, the increase in the world’s population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).

Review of Current Robotic Approaches for Precision Weed Management.

Purpose of ReviewThe goal of this review is to provide an overview of current robotic approaches to precision weed management. This includes an investigation into applications within this field during the past 5 years, identifying which major technical areas currently preclude more widespread use, and which key topics will drive future development and utilisation.Recent FindingsStudies combining computer vision with traditional machine learning and deep learning are driving progress in weed detection and robotic approaches to mechanical weeding. Integrating key technologies for perception, decision-making, and control, autonomous weeding robots are emerging quickly. These effectively save effort while reducing environmental pollution caused by pesticide use.SummaryThis review assesses different weed detection methods and weeder robots used in precision weed management and summarises the trends in this area in recent years. The limitations of current systems are discussed, and ideas for future research directions are proposed.

Precision weed management: A means of boosting agricultural productivity

Precision weed management: A means of boosting agricultural productivity

Greenhouse application of light-drone imaging technology for assessing weeds severity occurring on baby-leaf red lettuce beds approaching fresh-cutting

Aim of study: For baby-leaf lettuces greenhouse cultivations the absence of weeds is a mandatory quality requirement. One of the most promising and innovative technologies in weed research, is the use of Unmanned Aerial Vehicles (or drones) equipped with acquisition systems. The aim of this study was to provide an estimation of the exact weed amount on baby-sized red lettuce beds using a light drone equipped with an RGB microcamera.Area of study: Trials were performed at specialized organic farm site in Eboli (Salerno, Italy), under polyethylene multi-tunnel greenhouse.Material and methods: The RGB images acquired were processed with specific algorithms distinguishing weeds from crop yields, estimating the weeds covered surface and the severity of weed contamination in terms of biomass. A regression between the percentage of the surface covered by weed (with respect to the image total surface) and the weight of weed (with respect to the total harvested biomass) was calculated.Main results: The regression between the total cover values of the 25 calibration images and the total weight measured report a significant linear correlation. Digital monitoring was able to capture with accuracy the highly variable weed coverage that, among the different grids positioned under real cultivation conditions, was in the range 0-16.4% of the total cultivated one.Research highlights: In a precision weed management context, with the aim of improving management and decreasing the use of pesticides, this study provided an estimation of the exact weed amount on baby-sized red lettuce beds using a light drone.