Site-specific Weed Management Research Articles

Convolutional neural network for the early identification of weeds: A technological support to biodiversity and yield losses mitigation

Weeds are a major constraint for crop production and food security. Chemical management, the most utilized method for weed control, has serious drawbacks. In this context, the development of more sustainable methods like site-specific weed management (SSWM) is highly deemed. With this study, we assessed the possibility of applying two convolutional neural networks (CNN) for the recognition of different classes of weeds in a winter wheat (T. aestivum) cultivation based on RGB images. By using this methodology, we were able to recognize with a high average precision (AP > 0.6) some species whose abundance and distribution in the field was correlated with the final wheat yield. We demonstrated that where the presence of R. raphanistrum, A. arvensis and P. rhoeas was high at the tillering stage and weed biodiversity indexes were low (Menhinich's Index, Simpson's Reciprocal Index and Shannon's Index), wheat yield was significantly reduced. In contrast, higher weed biodiversity mitigated yield losses. Therefore, CNN is a useful tool for early evaluation of the impact that weeds may have on yield, and it can be used as SSWM classifier for an early mapping of weeds, which is critical to improve our understanding of weed ecology dynamics in agricultural fields.

Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review

Robotics and variable rate technology (VRT) have shown great potential for site-specific weed management (SSWM), but these technologies face several challenges like accurate weed identification, high initial cost, integration with existing farming practices, and the need for the development of precise and robust system for real-time application. Given the lack of comprehensive information on the current state of robotic technologies for weed management, this review study aims to provide an extensive overview of the individual components associated with SSWM technologies, such as autonomous navigation, imaging sensors, and weeding approaches. To perform this study, 91 technical papers were selected that involve the status, challenges, and potential improvements of individual components integrated to develop an efficient weed management technology. The key outcomes can be summarized as: (a) real-time kinematic-enabled global positioning system (RTK-GPS) and machine vision guidance systems are widely adopted technology for automatic row guidance because of their low cost, high accuracy, and easy accessibility, (b) GPS technology face challenges due to signal interference and dependency on satellite availability for accurate positioning, (c) sensor fusion has been suggested as a potential solution to improve the accuracy of positioning and weed identification, (d) deep learning techniques for weed identification offer significant promise in enhancing the feasibility of implementing robotic weed control in real-time, (e) due to robust nature of hyperspectral imaging, research in weed identification for real-time in open-field conditions is still lagging, (f) plethora of university-based research studies are limited to model optimization and simulation studies, and (g) integrated weed management practices, such as a combined application of mechanical and laser weeding, could be the future of robotic weed control. This review will serve as a valuable reference to extension educators, growers, researchers, and stakeholders for adopting state-of-the-art and sustainable practices in SSWM.

Weed Species Identification: Acquisition, Feature Analysis, and Evaluation of a Hyperspectral and RGB Dataset with Labeled Data

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed’s growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features.

Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach

Traditional weed management often involves blanket herbicide spraying, resulting in substantial herbicide wastage, environmental concerns, and herbicide resistant issues. Smart spraying systems utilizing robotics and sensors technologies can minimize herbicide usage and provide a sustainable solution for site-specific weed management. A machine vision-based spraying system was designed and developed for weed identification and precise spray application onto the target weeds. The sprayer platform utilizes a deep learning YOLOv4 model to accurately recognize multiple weed species, facilitating targeted spray application. The platform is equipped with an FLIR RGB camera for real-time image acquisition and Nvidia Jetson AGX Orin edge device for deploying weed detection deep-learning model. The GPIO pins of Nvidia Jetson were utilized to activate relay, providing precise on/off control over the TeeJet solenoid valves for spot spraying. Both indoor and field experiments were conducted to evaluate and compare the performance of vision-based sprayer system for weed identification and precise spraying onto the target weeds. In the indoor experiment, the sprayer system showed the average effective spraying rate of 93.33 %, with the precision of 100 % and recall of 92.8 %. Conversely, the field experiment resulted in a slightly lower average effective spraying rate of 90.6 %, while maintaining a precision of 95.5 % and a recall of 89.47 %. The reduced accuracy of the spraying system in field experiment was due to varying outdoor conditions such as lighting, shadows, and wind velocity. Overall, the result of this study demonstrates the spraying system's potential for targeted herbicide application onto the grid cells containing weeds, effectively reducing herbicide usage and overall weed management costs.

Weed species classification with UAV imagery and standard CNN models: Assessing the frontiers of training and inference phases

Accurate weed species identification is crucial for effective site-specific weed management (SSWM), enabling targeted and timely control measures for each weed in crop field. This study advanced the current approach to species-level weed identification during the early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 and Inception-Resnet-v2. For this, a robust dataset was created with 33,467 labels of weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) and crops (maize, tomato), which was subjected to different training, validation and test scenarios. Model inputs were adjusted in order to align them with the information represented by the UAV images. Initially, models were developed in balanced scenarios, gradually increasing label numbers to assess their performance. Inception-ResNet-v2 achieved over 90% accuracy with 400 labels, while ResNet152 and VGG16 required 600 and 800 labels, respectively, for similar accuracy. In a more complex and realistic scenarios with unbalanced datasets, Inception-ResNet-v2 outperformed, likely due to its deeper architecture and enhanced capability to capture intricate features and patterns within UAV images. The study emphasized the importance of the minority-to-majority species ratio in unbalanced datasets, which affects minority species classification. To prevent misclassification, it is crucial to determine the right number of labels for CNN model training and validation. Weed maps were generated after species classification using the Faster R–CNN algorithm as an object detector. This advancement in methodology facilitates the precise and efficient implementation of SSWM techniques.

Weed Management Using UAV and Remote Sensing in Malaysia Paddy Field: A Review

Controlling weed infestation is pivotal to achieving the maximum yield in paddy fields. At a time of exponential human population growth and depleting arable land mass, finding the solution to this problem is crucial. For a long time, herbicides have been the most favoured approach for weed control due to their efficacy and ease of application. However, adverse effects on the environment due to the excessive use of herbicides have prompted more cautious and effective herbicide usage. Many weed species tend to dominate the field, and the weed thrived in patches, rendering conventional broad herbicide spraying futile. Site-specific weed management (SSWM) consists of two strategies: weed mapping and selective herbicide application. Since its introduction into the agriculture sector, unmanned aerial vehicles (UAV) have become the platform of choice for carrying both the remote sensing system for weed mapping and the selective application of herbicide. Red-Green-Blue (RGB), multispectral and hyperspectral sensors on UAVs enable highly accurate weed mapping. In Malaysia, adopting this technology is highly possible, given the nature of government-administrated rice cultivation. This review provides insight into the weed management practice using remote sensing techniques on UAV platforms with potential applications in Malaysia's paddy field. It also discusses the recent works on weed mapping with imaging remote sensing on a UAV platform.

Spectral mixture analysis for weed traits identification under varying resolutions and growth stages

Spectral and spatial resolutions are critical factors in Site-Specific Weed Management (SSWM) applications, which aim to adjust and apply herbicides according to the weed composition and coverage during early growth. Thus, a reliable SSWM requires high spectral and spatial resolution data to create informative maps indicating and spatially localizing the weed species. Besides, upscaling and improving the mapping process are essential to motivate farmers to adopt SSWM practices. However, high-resolution data in spectral and spatial domains is not always available, and mixed pixels are highly probable. Therefore, evaluating approaches that deal with such pixels that pose a significant problem for weed classification tasks is vital. In this regard, Spectral Mixture Analysis (SMA) can exploit subpixel information from coarse-resolution data. However, despite its potential, SMA has not been examined for subpixel weed mapping. In this work, we aim to evaluate the potential advantages of SMA for coverage estimation of different weed traits (weed botanical group, photosynthetic pathway, and species). For this purpose, we first created a dataset containing weed species characterized by different botanical groups and photosynthetic pathways. Then, we evaluated the performance of SMA in estimating the weed’s coverage at varying growth stages and spectral and spatial resolutions. The experimental results revealed that using botanical weed groups as Endmembers (EM) is advantageous over other traits. A quantitative evaluation revealed strong correlation values between the actual and predicted weed coverage and a Mean Absolute Error (MAE) lower than 10 % for the different EM at all resolutions and growth stages. Besides, SMA outperformed the traditional classification-based approach in estimating weed coverage with up to ∼ 30 % lower Mean Absolute Error. Finally, our study establishes a basis for incorporating SMA in various SSWM practices.

Detection and mapping of Amaranthus spinosus L. in bermudagrass pastures using drone imagery and deep learning for a site‐specific weed management

AbstractWeed encroachment negatively affects pasture productivity by reducing herbage allowance, stocking rates, and livestock performance. Amaranthus spinosus L. is a weed species widely found in pastures worldwide and is considered challenging for ranchers due to its great potential for invasion, making it difficult to control. The high costs of chemical application and the global concern about environmental impacts restrict indiscriminate herbicide spraying in pastures. Site‐specific weed management (SSWM) is a weed management strategy based on weed spot‐spraying that has the potential to overcome these issues. Images from unmanned aerial vehicles (UAVs) can provide valuable information for weed mapping to drive the herbicide application in pastures. Deep learning techniques have been highlighted in image classification tasks. We developed a deep convolutional neural network (CNN)‐based image segmentation model based on the U‐Net architecture to detect and map Amaranthus spinosus in bermudagrass pastures using red–green–blue images acquired through UAV flying in moderate‐high altitude. The images were acquired from twelve paddocks under three treatments (weed‐free, weed‐strips, or weed‐infested) during the summer (2021‐2022). The CNN model was able to detect around 80% of the A. spinosus with an average prediction accuracy of 94%. Our weed mapping showed the potential of using the U‐Net model to generate a herbicide application map to be inserted into the sprayer system, reducing up to 76% of the amount of herbicide applied. Further studies are encouraged to increase the robustness of the model across species and development stages and develop sprayer systems to implement the spot‐spraying in field conditions.

Channel Attention GAN-Based Synthetic Weed Generation for Precise Weed Identification.

Weed is a major biological factor causing declines in crop yield. However, widespread herbicide application and indiscriminate weeding with soil disturbance are of great concern because of their environmental impacts. Site-specific weed management (SSWM) refers to a weed management strategy for digital agriculture that results in low energy loss. Deep learning is crucial for developing SSWM, as it distinguishes crops from weeds and identifies weed species. However, this technique requires substantial annotated data, which necessitates expertise in weed science and agronomy. In this study, we present a channel attention mechanism-driven generative adversarial network (CA-GAN) that can generate realistic synthetic weed data. The performance of the model was evaluated using two datasets: the public segmented Plant Seedling Dataset (sPSD), featuring nine common broadleaf weeds from arable land, and the Institute for Sustainable Agro-ecosystem Services (ISAS) dataset, which includes five common summer weeds in Japan. Consequently, the synthetic dataset generated by the proposed CA-GAN obtained an 82.63% recognition accuracy on the sPSD and 93.46% on the ISAS dataset. The Fréchet inception distance (FID) score test measures the similarity between a synthetic and real dataset, and it has been shown to correlate well with human judgments of the quality of synthetic samples. The synthetic dataset achieved a low FID score (20.95 on the sPSD and 24.31 on the ISAS dataset). Overall, the experimental results demonstrated that the proposed method outperformed previous state-of-the-art GAN models in terms of image quality, diversity, and discriminability, making it a promising approach for synthetic agricultural data generation.

Cross-domain transfer learning for weed segmentation and mapping in precision farming using ground and UAV images

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains.

Agricultural weed identification in images and videos by integrating optimized deep learning architecture on an edge computing technology

Recent advancements in deep learning (DL)-based model optimization techniques have resulted in better weed identification accuracy. However, optimizing these models to identify weeds in images captured using small unmanned aerial system (UAS) has not been much explored. Moreover, leveraging the optimized model on resource constrained edge platform that could be easily integrated with UAS for real-time weed identification could be of significant advantage in developing precision aerial spraying weed management technology. Therefore, this study proposes YOLO-Spot model that is based on YOLOv7-tiny architecture, has been optimized and reconstructed to identify weeds amongst crop plants in aerial images and videos. The optimized model tends to use a smaller number of trainable parameters and reduced feature map sizes for weed identification. Most of the redundant convolutional layers along with feature map sizes have been reduced coupled with an integration of a novel module re-parameterized convolutional layer (RCL) within the neck component of the network. Furthermore, YOLO-Spot model has been trained on the three image resolutions, 320 × 320, 640 × 640 and 1280 × 1280, and has been named as YOLO-Spot_S, YOLO-Spot_M and YOLO-Spot_L, respectively. Out of all the variants, YOLO-Spot_M model has achieved significant prediction accuracy as compared to other variants and a denser layered model YOLOv7-Base. YOLO-Spot_M model utilizes over 75 % less parameters and 86 % reduced GFLOPs compared to YOLOv7-Base. As per the results, YOLO-Spot_M has outperformed YOLOv7-Base by achieving +1.3 % and +2.7 % overall accuracy and mAP(@0.5), respectively. The optimized architecture utilizes 4X less power (in W) when trained on a normal graphical processing unit (GPU). Moreover, converting YOLO-Spot_M to half-precision (FP16) for resource constrained device deployment (AGX Xavier), led to a +0.6 % accuracy and 5X faster weed recognition accuracy in aerial images and videos during inferencing. Based on the metrics obtained, YOLO-Spot_M model is recommended model that could be integrated with remote sensing technologies for site-specific weed management.

Transformer in UAV Image-Based Weed Mapping

Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.

Analysis of Stable Diffusion-derived fake weeds performance for training Convolutional Neural Networks

Weeds challenge crops by competing for resources and spreading diseases, impacting crop yield and quality. Effective weed detection can enhance herbicide application, thus reducing environmental and health risks. A major challenge in Site-Specific Weed Management (SSWM) is developing a reliable weed identification system, especially given the diversity and similarity between certain weeds and crops during early growth stages. Image-based deep learning (DL) methods have become vital for weed classification. However, accurate weed classification and detection using DL techniques face the bottleneck of requiring large labeled data. Furthermore, labeling this specific extensive data is a time-consuming and tedious task apart from necessitating weed science experts. This research's central focus is to present a novel approach to weed detection using convolutional neural network (CNN) classifiers, specifically Yolov8l and RetinaNet, augmented with Stable Diffusion data i.e., artificial weed images. Stable Diffusion enhanced the training data, increasing the classifiers' adaptability. The study targeted specific weeds (Solanum nigrum L.; Portulaca oleracea L.; Setaria Verticillata L.) found in tomato crops, using a limited number of real images (30 samples) to produce artificial training images for the CNNs. All validation and test sets are comprised of real weed images. Results showed high performance when using only artificial images in terms of Mean Average Precision (mAP). In isolated conditions (0.91 mAP), i.e., only one weed species per image, an average performance gain of about 3% in all tests is obtained. When adding the artificial images to the real ones (mixed dataset), a mAP of 0.99 is obtained. In contrast, results using only artificial images obtained 0.81 mAP when detecting more than a single weed species. However, when implementing the trained CNNs with a mixed dataset, a 6% − 9% performance gain was achieved in all cases. A mAP of up to 0.93 was achieved in the most challenging conditions where weed species could overlap. The results indicate that the proposed approach outperformed existing methods, such as Generative Adversarial Networks (GANs) regarding mAP. Furthermore, the Yolov8l model distinctly emerged as the most favorable option for real-time detection systems considering Frame Detection Speed (FDS). Specifically, the Yolov8l model registered an FDS of 10.2 ms, which is considerably faster when compared to the 21.2 ms that the RetinaNet model exhibited. Additionally, the method is versatile and applicable to various crops and weed species, thereby enhancing automated weed management systems. This research illustrates that Stable Diffusion can efficiently expand small image sets, significantly reducing field imaging. The study offers valuable insights for future SSWM efforts utilizing artificially generated images for weed detection and classification.

Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model

BackgroundEfficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks.ResultsIn this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmentation in motion blurred images. For this purpose, we first collected a new dataset of matching sharp and naturally blurred image pairs of real sorghum and weed plants from drone images of the same agricultural field. The data was used to train and evaluate the performance of DeBlurWeedSeg on both sharp and blurred images of a hold-out test-set. We show that DeBlurWeedSeg outperforms a standard segmentation model that does not include an integrated deblurring step, with a relative improvement of 13.4 % in terms of the Sørensen-Dice coefficient.ConclusionOur combined deblurring and segmentation model DeBlurWeedSeg is able to accurately segment weeds from sorghum and background, in both sharp as well as motion blurred drone captures. This has high practical implications, as lower error rates in weed and crop segmentation could lead to better weed control, e.g. when using robots for mechanical weed removal.

Leveraging machine learning and drone multispectral data for site-specific weed management in tomato agricultural areas

Leveraging machine learning and drone multispectral data for site-specific weed management in tomato agricultural areas

Trouble in the rice field: Distribution ecology and indicator value of weed species in the rice fields of Himalayan region

Field-based evaluation of indicator weed species in agriculture systems is effective for developing site-specific weed management. Because of anthropogenic activities and climate change, new weeds are invading agricultural fields. The current study examined the weed species in agricultural fields with a focus on diversity, distribution ecological patterns, and identification of indicator species, all of which might help in effective weed management practices. Following the calculation of the Importance Value Index for each weed species, ordination, and indicator species analysis were conducted. We collected vegetation data through random sampling, and the current study recorded 47 species, taxonomically distributed across 41 genera and 19 families. Asteraceae with 13% of species was the leading family followed by Poaceae, and Brassicaceae (11% each). Annual weed species with 49% were the dominant life span category. The phytogeographical analysis revealed that the highest number (28 species) recorded from the study area were native, while 19 species were alien. Of the total alien species, 4 were invasive, and 14 were naturalised and one species Bassia scoparia was casual. The diversity indices show significant differences (p ≤ 0.05) in weeds in rice fields recorded from the study area. Detrended correspondence analysis grouped the species Geranium rotundifolium, Sisymbrium loeselii, Mentha longifolia, Rumex dentatus, Amaranthus viridis on the extreme left side suggest a low score on axis 1 and a high score on axis 2. The analysis of indicator species revealed a significant difference (p ≤ 0.05) in agriculture fields across the sampled sites. According to the indicator species analysis, Poa annua, Persicaria hydropiper, Ranunculus sceleratus, and Echinochloa colona were the key weed species found in the study area. This investigation might aid weed management strategies to minimize crop losses and increase of rice production to fulfill the current food requirement.

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.

Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images

For sustainability and efficiency in maintaining high crop yield and less chemically polluted agricultural lands, precise weed mapping is essential for the total implementation of site-specific weed management which currently stands as a major challenge in present day agriculture. In this research, the robustness of the training epochs of You Only Look Once (YOLO) v5s, a Convolutional Neural Network (CNN) model was evaluated for the development of an automatic crop and weeds classification using UAV images. The images were annotated using a bounding box and they were trained on google colaboratory over 100, 300, 500, 600, 700 and 1000 epochs. The model detected and categorized five different classes which are sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. To find the optimal performance on the test set, the model was trained across several epochs, and training was stopped when the test performance (classification accuracy, precision, and recall) began to drop. The obtained result shows that the performance of the classifier improved significantly as the range of training epochs tends to rise from 100 through to 600 epochs. Meanwhile, a slight decline was observed as the number of epoch was increased to 700 when the classification accuracy, the precision of weed and recall of 65, 43 and 43%, respectively, was recorded as against 67, 78 and 34% that was obtained as the classification accuracy, weed precision and recall, respectively, at 600 epochs. This decline continued even when the epoch was increased to 1000 where classification accuracy, weed precision and recall of 65%, 45% and 40%, respectively was obtained. The results showed that the training epoch of YOLOv5s significantly affects the model's robustness in automatic crop and weep classification and identified 600 as the epoch for optimal performance.

Improving agricultural robot patch-spraying accuracy and precision through combined error adjustment

Site-specific weed management (SSWM) involves precision spraying operations based on the spatial variability of weeds in the field. High spraying precision enables extensive economic and environmental benefits. We developed a high-precision patch-spraying robot system based on weed maps to conduct patch-spraying operations at the individual plant scale. The patch-spraying droplet deposition effect is evaluated by post-spraying droplet deposition images. The agricultural robot mainly consists of a walking chassis, spraying, and control systems. The walking chassis provides walking power and support for the suspended spraying system. Combining the static structural and dynamic error models of the robot improves patch-spraying accuracy and precision. Twelve patch-spraying simulations were carried out on the leveling site. Different correction methods and driving speeds were tested, with driving rates of 0.5, 1.0, and 1.5 m/s. Aerial images of the patch-spraying process were evaluated using pixel point-based discriminant statistics after operation completion. The droplet deposition coverage accuracy ranged from 95.9 to 98.8 %. The center of droplet deposition offset distance was 3.7–7.2 cm. Field results showed that the patch-spraying weed control rate based on individual plant scales (spraying precision ≤ 15 cm) was 94.2–98.1 %. In summary, the developed agricultural robot performs patch-spraying operations at the individual plant scale. Patch-spraying accuracy and precision are improved by modeling corrections for random errors. A general error correction method for the deposition position of fog droplets is explored to achieve precise droplet delivery in the walking state of plant protection operation equipment.

Multispectral UAV data for detection of weeds in a citrus farm using machine learning and Google Earth Engine: Case study of Morocco

Accurate and timely weed mapping between and within trees is considered one of the major challenges in the site-specific weed management systems. This research presents the first cloud computing approach based on the incorporation of multispectral Unmanned Aerial Vehicle (UAV) imagery in the Google Earth Engine (GEE) programming environment with the aim of improving the mapping of weed patches between and within trees in a citrus farm. For this purpose, the UAV multispectral bands (red, green, blue, near infrared, and red edge), as well as the estimated vegetation height from the UAV Digital Elevation Model (DEM) were analysed in terms of tree and weed discrimination and used as input into Random Forest (RF) and k-nearest neighbors (KNN) machine learning algorithms. From the DEM, the Digital Terrain Model (DTM) was estimated using the Inverse Distance Weighted Interpolation (IDW) of the elevation values of dense points on the soil. The plant height was derived by subtracting the DTM from the DEM, resulting in the Canopy Height Model (CHM). The experimental results show that: (i) the combination of spectral bands and the CHM can classify both trees and weeds with an overall accuracy reached 96.87%; (ii) the RF classifier was more robust compared to KNN in the classification performance; (iii) when compared to the use of UAV spectral bands, the addition of the CHM can improve the accuracy of crop classification by 13.36% (KNN) and 1.79% (RF). Furthermore, the integration of UAV imagery in the GEE was highly efficient in terms of automation of the UAV imagery processing.