Using Semi-Supervised Learning to Predict Weed Density and Distribution for Precision Farming

If weed growth is not controlled, it can have a devastating effect on the size and quality of a harvest. Unrestrained pesticide use for weed management can have severe consequences for ecosystem health and contribute to environmental degradation. However, if you can identify problem spots, you can more precisely treat those areas with insecticide. As a result of recent advances in the analysis of farm pictures, techniques have been developed for reliably identifying weed plants. . On the other hand, these methods mostly use supervised learning strategies, which require a huge set of pictures that have been labelled by hand. Therefore, these monitored systems are not practicable for the individual farmer because of the vast variety of plant species being cultivated. In this paper, we propose a semi-supervised deep learning method that uses a small number of colour photos taken by unmanned aerial vehicles to accurately predict the number and location of weeds in farmlands. Knowing the number and location of weeds is helpful for a site-specific weed management system in which only afflicted areas are treated by autonomous robots. In this research, the foreground vegetation pixels (including crops and weeds) are first identified using an unsupervised segmentation method based on a Convolutional Neural Network (CNN). There is then no need for manually constructed features since a trained CNN is used to pinpoint polluted locations. Carrot plants from the (1) Crop Weed Field Image Dataset (CWFID) and sugar beet plants from the (2) Sugar Beets dataset are used to test the approach. The proposed method has a maximum recall of 0.9 and an accuracy of 85%, making it ideal for locating weed hotspots. So, it is shown that the proposed strategy may be used for too many kinds of plants without having to collect a huge quantity of labelled data

Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated ( 10 4 instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science. Scatter transform only trained on synthetic data shows an accuracy of 85 % when tested on real data