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.

Effects of secondary crops on bacterial growth and nitrogen removal in shrimp farm effluent treatment systems

Secondary crops provide a means of assimilating some effluent nitrogen from eutrophic shrimp farm settlement ponds. However, a more important role may be their stimulation of beneficial bacterial nitrogen removal processes. In this study, bacterial biomass, growth and nitrogen removal capacity were quantified in shrimp farm effluent treatment systems containing vertical artificial substrates and either the banana shrimp Penaeus merguiensis (de Man) or the grey mullet, Mugil cephalus L. Banana shrimp were found to actively graze biofilm on the artificial substrates and significantly reduced bacterial biomass relative to a control (24.5 ± 5.6mgCm−2 and 39.2 ± 8.7mgCm−2, respectively). Bacterial volumetric growth rates, however, were significantly increased in the presence of the shrimp relative to the control 45.2±11.3mgCm−2 per day and 22.0±4.3mgCm−2 per day, respectively). Specific growth rate, or growth rate per cell, of bacteria was therefore appreciably stimulated by the banana shrimp. Nitrate assimilation was found to be significantly higher on grazed substrate biofilm relative to the control (223±54 mgNm−2 per day and 126±36 mg Nm−2 per day, respectively), suggesting that increased bacterial growth rate does relate to enhanced nitrogen uptake. Regulated banana shrimp feeding activity therefore can increase the rate of newbacterial biomass production and also the capacity for bacterial effluent nitrogen assimilation. Mullet had a negligible influence on the biofilm associated with the artificial substrate but reduced sediment bacterial biomass (224 ± 92 mgCm−2) relative to undisturbed sediment (650 ± 254 mgCm−2). Net, or volumetric bacterial growth in the sediment was similar in treatments with and without mullet, suggesting that the growth rate per cell of bacteria in grazed sediments was enhanced. Similar rates of dissolved nitrogen mineralisation werefound in sediments with and without mullet but nitrificationwas reduced. Presence of mullet increased water column suspended solids concentrations, water column bacterial growth and dissolved nutrient uptake. This study has shown that secondary crops, particularly banana shrimp, can play a stimulatory role in the bacterial processing of effluent nitrogen in eutrophic shrimp effluent treatment systems.