Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Background Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments. Results Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 field images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 field images, showing that the combination of synthetic and original field images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected field images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-off between accuracy and speed. Specifically, the average precisions ([email protected]) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800 × 1200) on a NVIDIA Titan X GPU environment

UAV Detection of sinapis arvensis infestation in alfalfa plots using simple vegetation indices from conventional digital cameras

Producción CientíficaUnmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps.Unión Europea (project LIFE11 ENV/ES/000535

Assessment of RGB vegetation indices to estimate chlorophyll content in sugar beet leaves in the final cultivation stage

Estimation of chlorophyll content with portable meters is an easy way to quantify crop nitrogen status in sugar beet leaves. In this work, an alternative for chlorophyll content estimation using RGB-only vegetation indices has been explored. In a first step, pictures of spring-sown ‘Fernanda KWS’ variety sugar beet leaves taken with a commercial camera were used to calculate 25 RGB indices reported in the literature and to obtain 9 new indices through principal component analysis (PCA) and stepwise linear regression (SLR) techniques. The performance of the 34 indices was examined in order to evaluate their ability to estimate chlorophyll content and chlorophyll degradation in the leaves under different natural light conditions along 4 days of the canopy senescence period. Two of the new proposed RGB indices were found to improve the already good performance of the indices reported in the literature, particularly for leaves featuring low chlorophyll contents. The 4 best indices were finally tested in field conditions, using unmanned aerial vehicle (UAV)-taken photographs of a sugar beet plot, finding a reasonably good agreement with chlorophyll-meter data for all indices, in particular for I2 and (R−B)/(R+G+B). Consequently, the suggested RGB indices may hold promise for inexpensive chlorophyll estimation in sugar beet leaves during the harvest time, although a direct relationship with nitrogen status still needs to be validated

RGB vegetation indices applied to grass monitoring: a qualitative analysis

ArticleIn developing countries such as Brazil, research on low-cost remote sensing and computational techniques become essential for the development of precision agriculture (PA), and improving the quality of the agricultural products. Faced with the scenario of increasing production of emerald grass (Zoysia Japônica) in Brazil, and the value added the quality of this agricultural product. The objective of this work was to evaluate the performance of RGB (IV) vegetation indices in the identification of exposed soil and vegetation. The study was developed in an irrigated area of 58 ha cultivated with emerald grass at Bom Sucesso, Minas Gerais, Brazil. The images were obtained by a RGB digital camera coupled to an remotely piloted aircraft. The flight plan was setup to take overlapping images of 70% and the aircraft speed was 10 m s -1 . Six RGB Vegetation index (MGVRI, GLI, RGBVI, MPRI, VEG, ExG) were evaluated in a mosaic resulting from the images of the study area. All of the VIs evaluated were affected by the variability of lighting conditions in the area but MPRI and MGVRI were the ones that presented the best results in a qualitative evaluation regarding the discrimination of vegetation and soil

Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier

Weed detection with aerial images is a great challenge to generate field maps for site-specific plant protection application. The requirements might be met with low altitude flights of unmanned aerial vehicles (UAV), to provide adequate ground resolutions for differentiating even single weeds accurately. The following study proposed and tested an image classifier based on a Bag of Visual Words (BoVW) framework for mapping weed species, using a small unmanned aircraft system (UAS) with a commercial camera on board, at low flying altitudes. The image classifier was trained with support vector machines after building a visual dictionary of local features from many collected UAS images. A window-based processing of the models was used for mapping the weed occurrences in the UAS imagery. The UAS flight campaign was carried out over a weed infested wheat field, and images were acquired between a 1 and 6 m flight altitude. From the UAS images, 25,452 weed plants were annotated on species level, along with wheat and soil as background classes for training and validation of the models. The results showed that the BoVW model allowed the discrimination of single plants with high accuracy for Matricaria recutita L. (88.60%), Papaver rhoeas L. (89.08%), Viola arvensis M. (87.93%), and winter wheat (94.09%), within the generated maps. Regarding site specific weed control, the classified UAS images would enable the selection of the right herbicide based on the distribution of the predicted weed species. © 2018 by the authors

Analysis of the influence of environmental variables on carbon content of sugar beet crop and estimation of nitrogen content in leaves by vegetation indices

Esta Tesis trata de conocer el papel del cultivo de la remolacha azucarera como sumidero de CO2 cuantificando, por una parte, la absorción de dicho gas de efecto invernadero y dilucidando, por otra, si este proceso de asimilación está determinado ontogénicamente o si bien está influenciado por condiciones ambientales. Una vez demostrado que el factor localización (clima, suelo y campaña) es determinante e identificada la influencia de radiación y temperatura, se plantea la necesidad de, a través de un escenario de cambio climático del AR5 de IPCC y el modelo de simulación de cultivos Aquacrop de FAO, conocer los posibles futuros comportamientos y tendencias del cultivo. Todo ello y junto al uso de índices de vegetación RGB (existentes y dos nuevos propuestos), como herramientas para la detección de cambios críticos en el cultivo que afecten a su capacidad como sumidero, se genera el conocimiento necesario para ulteriores medidas de adaptación.Departamento de Ingeniería Agrícola y Foresta

Sensor technology for precision weeding in cereals. Evaluation of a novel convolutional neural network to estimate weed cover, crop cover and soil cover in near-ground red-green-blue images

Precision weeding or site-specific weed management (SSWM) take into account the spatial distribution of weeds within fields to avoid unnecessary herbicide use or intensive soil disturbance (and hence energy consumption). The objective of this study was to evaluate a novel machine vision algorithm, called the ‘AI algorithm’ (referring to Artificial Intelligence), intended for post-emergence SSWM in cereals. Our conclusion is that the AI algorithm should be suitable for patch spraying with selective herbicides in small-grain cereals at early growth stages (about two leaves to early tillering). If the intended use is precision weed harrowing, in which also post-harrow images can be used to control the weed harrow intensity, the AI algorithm should be improved by including such images in the training data. Another future goal is to make the algorithm able to distinguish weed species of special interest, for example cleavers (Galium aparine L.).Sensor technology for precision weeding in cereals. Evaluation of a novel convolutional neural network to estimate weed cover, crop cover and soil cover in near-ground red-green-blue imagespublishedVersio

Recent results in the development of band steaming for intra-row weed control

The recent achievements with developing band-steaming techniques for intra-row weed control in vegetables are presente

Combining physical and cultural weed control with biological methods – prospects for integrated non-chemical weed management strategies

The paper deals with the possibilities of combining physical weed control with biological weed control