Spatial Analysis of Digital Imagery of Weeds in a Maize Crop

Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m2) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favoured some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches

Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight

Mid to Late Season Weed Detection in Soybean Production Fields Using Unmanned Aerial Vehicle and Machine Learning

Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were compared with the Mobilenet v2 based convolutional neural network (CNN) model for their classification performance. The results showed that the CNN model had the best classification performance for individual patches. Two different image slicing approaches – patches with and without overlap were tested, and it was found that slicing with overlap leads to improved weed detection but with higher inference time. For the object detection approach, two models with different network architectures, namely Faster RCNN and SSD were evaluated and compared. It was found that Faster RCNN had better overall weed detection performance than the SSD with similar inference time. Also, it was found that Faster RCNN had better detection performance and shorter inference time compared to the patch-based CNN with overlapping image slicing. The influence of spatial resolution on weed detection accuracy was investigated by simulating the UAV imagery captured at different altitudes. It was found that Faster RCNN achieves similar performance at a lower spatial resolution. The inference time of Faster RCNN was evaluated using a regular laptop. The results showed the potential of on-farm near real-time weed detection in soybean production fields by capturing UAV imagery with lesser overlap and processing them with a pre-trained deep learning model, such as Faster RCNN, in regular laptops and mobile devices. Advisor: Yeyin Sh

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery

Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds.m(-2). Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages