Calcium phosphate flocs and the clarification of sugar cane juice from whole of crop harvesting

Sugar cane biomass is one of the most viable feedstocks for the production of renewable fuels and chemicals. Therefore, processing the whole of crop (WC) (i.e., stalk and trash, instead of stalk only) will increase the amount of available biomass for this purpose. However, effective clarification of juice expressed from WC for raw sugar manufacture is a major challenge because of the amounts and types of non-sucrose impurities (e.g., polysaccharides, inorganics, proteins, etc.) present. Calcium phosphate flocs are important during sugar cane juice clarification because they are responsible for the removal of impurities. Therefore, to gain a better understanding of the role of calcium phosphate flocs during the juice clarification process,the effects of impurities on the physicochemical properties of calcium phosphate flocs were examined using small-angle laser light scattering technique, attenuated total reflectance Fourier transformed infrared spectroscopy, and X-ray powder diffraction. Results on synthetic sugar juice solutions showed that the presence of SiO2 and Na+ ions affected floc size and floc structure. Starch and phosphate ions did not affect the floc structure; however, the former reduced the floc size, whereas the latter increased the floc size. The study revealed that high levels of Na+ ions would negatively affect the clarification process the most, as they would reduce the amount of suspended particles trapped by the flocs. A complementary study on prepared WC juice using cold and cold/intermediate liming techniques was conducted. The study demonstrated that, in comparison to the one-stage (i.e., conventional) clarification process, a two-stage clarification process using cold liming removed more polysaccharides (≤19%),proteins (≤82%), phosphorus (≤53%), and SiO2 (≤23%) in WC juice but increased Ca2+ (≤136%) and sulfur (≤200%)

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Improving the selectivity of engineered protease inhibitors: Optimizing the P2 prime residue using a versatile cyclic peptide library

Standard mechanism inhibitors are attractive design templates for engineering reversible serine protease inhibitors. When optimizing interactions between the inhibitor and target protease, many studies focus on the nonprimed segment of the inhibitor's binding loop (encompassing the contact β-strand). However, there are currently few methods for screening residues on the primed segment. Here, we designed a synthetic inhibitor library (based on sunflower trypsin inhibitor-1) for characterizing the P2′ specificity of various serine proteases. Screening the library against 13 different proteases revealed unique P2′ preferences for trypsin, chymotrypsin, matriptase, plasmin, thrombin, four kallikrein-related peptidases, and several clotting factors. Using this information to modify existing engineered inhibitors yielded new variants that showed considerably improved selectivity, reaching up to 7000-fold selectivity over certain off-target proteases. Our study demonstrates the importance of the P2′ residue in standard mechanism inhibition and unveils a new approach for screening P2′ substitutions that will benefit future inhibitor engineering studies.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

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 technology 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 the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling 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. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.