Mapping Invasive Giant Goldenrod (Solidago gigantea) with Multispectral Images Acquired by Unmanned Aerial Vehicle

Invasive alien species are one of the main threats to worldwide biodiversity loss. Unmanned aerial vehicles with multispectral sensors offer a cost-effective alternative to monitor invasive plant species at a centimetre scale. Giant Goldenrod (Solidago gigantea) is one of the most problematic invasive alien plant species in Switzerland and controlling this species – especially in nature protection areas – is a priority. In this study, a methodology is developed to detect the Giant Goldenrod coverage via unmanned aerial vehicle (UAV) equipped with multispectral sensors. Very high resolution maps (6.5 cm) are produced and high accuracy is achieved for the classification of the Giant Goldenrod coverage with a kappa coefficient of 0.902 and an overall accuracy of 92.12%. These results indicate that UAV equipped with multispectral sensors is a valuable tool in monitoring and combatting invasive alien species

Techniques of deep learning and image processing in plant leaf disease detection: a review

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated

Biovanillin production from lemongrass leaves hydrolysates by Phanerochaete chrysosporium ATCC 24725 in batch culture

Biovanillin is one of fungi secondary metabolites, that is widely used as aromatic and flavour compound with high fiscal value. The use of vanillin as flavour for various products is its foremost application in food industries. The global market demand for natural vanillin as flavour stands for less than one percent (1%) of its market demand annually. However, most of the flavour compounds are normally obtained through the process of chemical synthesis, which could cause health problem and environmental hitches. Demand for natural and healthy products coupled with the fact that acid ferulic (FA) extracted from plant materials can be a precursor for biovanilin production makes it relatively inexpensive as a natural product. The research was aimed to extract FA from lemongrass leaves (LGL), which was used as precursor for one-step biovanillin production by Phanerochaete chrysosporium (ATCC 24725) in batch culture. Initially, optimization of the LGL pretreatment practices using liquid hot water with sodium bisulfite (0.5% w/v) towards the release of the FA was investigated with central composite design (CCD). The optimized results produced 0.750 g/L as the highest FA released from the lemongrass leaves hydrolysates (LLH). Considerable alterations of the major LGL contents were observed during the pretreatment process, which increased the cellulose content by 39%. The Fourier transform infrared (FTIR) and field emission scanning electron microscopic (FESEM) analyses confirmed that the lignin which serves as the shielding layer from the LGL components became fragmented, thus decreasing the lignin content by 46%. The total reducing sugar production with enzymatic saccharification using enzymes cocktail (celluclast and viscozyme, 1 % v/v each) improved by up to 8.4-folds as compared to the direct enzymatic saccharification without removing the LGL extracts. Screening of significant factors for biovanillin production using 2-level Factorial Design showed that the biovanillin production processes was affected by the interactive effects of initial FA concentration, incubation temperature, incubation time and initial pH. The highest biovanillin production (0.093 g/L with molar yield 23 %) in shake flasks using the CCD was determined with FA (0.5 g/L), temperature (35 °C), time (72 h), and initial pH (6.0). Application of both pH and dissolved oxygen control strategies in 2 litre stirred tank bioreactor had increased the biovanillin production by 1.41 and 1.53-folds as compared to the optimized experiment using the shake flasks. The evaluation of kinetics from the two-phase pH control strategy demonstrated the performance of P. chrysosporium with the highest specific growth rate (µ) of 0.056 h-1, with an increase in the yield coefficient of biomass formation Yx/s (0.5191 g/g) and maximum cell concentration Xmax (13.0 g/L) by 1.03 and 1.05-folds as compared to one-phase of pH control, respectively. Performance of the kinetics using two-phase dissolved oxygen (DO) control strategy has shown that 80 % saturations of DO during active growth phase with 40 % saturations during production phase were highly essential for enhancement of biovanillin production from LLH by P. chrysosporium using 2 litre stirred tank bioreactor. LGL residue which contained FA can be used as a precursor to produce biovanillin by natural means via one-step bioconversion process with P. chrysosporium in batch culture using 2 litre stirred tank bioreactors