Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Increasing vegetable consumption: a means-end chain approach

Purpose – The purpose of this study is to illustrate how means-end chain theory can inform communications that effectively convey the health messages of vegetable consumption to various publics. Design/methodology/approach – Laddering interviews were conducted with 61 participants who consumed at least two serves of vegetables a day and were responsible in part or whole for shopping in their household. A means-end chain value map was then constructed using mecanalyst software. Findings – Using means-end theory, an example communications strategy was developed from the dominant chain. The health and wellness features that respondents associated with vegetables were “freshness”, a “source of vitamins and minerals”, and “high nutritional value”. In the mind of the consumer, these features were linked to the benefit concept “maintain energy and vitality”, which in turn was connected to the consequence “maintain an active life”. The end-states or goals participants ultimately connected to the health and wellness features of vegetables were that of “enjoy life” and “achieve goals”. Research limitations/implications – The research is limited in so far as subjects who consume less than two serves of vegetables are not recruited for this study. Practical implications – It is suggested that social marketing initiatives designed to increase vegetable consumption may base messages on health-related values or end-states of being to resonate more effectively with consumers. Social implications – High vegetable consumption is associated with a reduced risk of chronic disease. Effective strategies designed to increase vegetable consumption amongst populations may reduce the burden on health systems. Originality/value – This study illustrates how consumers' cognitive processes can inform social marketing communications.