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Satellite Guided Agriculture: Soil Fertility and Crop Management
Published in K. R. Krishna, Push Button Agriculture, 2017
Satellite imagery using high-resolution sensors could be used regularly to assess, if fruit trees are afflicted with diseases. In case of Citrus grown in Florida or in other regions, trees are vulnerable to attack by citrus can-ker. It seems hyperspectral images of trees could help in tracing variety of citrus peel affecting maladies, such as cankerous tissue on ruby red grape fruits, greasy spots, insect attacks, melanose scab, wind scar, etc. Qin et al. (2009) have reported that citrus canker disease could be identified rapidly using satellite imagery. The hyperspectral images need to be compared with canker reference images. Canker intensity and spread could be quan-tified by comparing imagery with standard reference images. They have obtained 95.2% accuracy in detecting canker-affected regions within cit-rus orchards. At this juncture, we may have to note that, if scouting and mapping were done manually at different stages of tree growth and fruit maturation, it would cost farmers exorbitantly. A satellite image shows up and covers large areas and trees at one stretch. It costs relatively margin-ally to procure and to overlay satellite image it with growth/yield data. Satellite imagery of field and plantation crops to monitor crop health is now rather routine and is available from private agencies (Galileao Geo Inc., 2014). There are reports that suggest that images from low flying drones show greater details about disease/pest attack on trees. A coordi-nated effort, first to obtain satellite imagery covering large areas, then to focus on disease-afflicted spots using drones is perhaps shrewder. Such coordinated effort could provide accurate control of disease/insect attack.
Bio-ethanol production from a mixture of rice hull and orange peel wastes
Published in Biofuels, 2022
Ahmad Taghizadeh-Alisaraei, Ahmad Abbaszadeh-Mayvan, Seyyed Hassan Hosseini
Previous researchers have successfully used some agriculture wastes to produce ethanol. For instance, Grohmann et al. investigated ethanol production from enzymatically hydrolyzed orange peel by the yeast Saccharomyces cerevisiae. They argued that the enzymatic hydrolysates of orange peel are suitable substrates for fermentation [16]. Widmer et al. studied the effects of various pretreatments on orange processing wastes for making ethanol by simultaneous saccharification and fermentation (SSF). They produced ethanol yields of 76–94% based on sugar content by enzymatic hydrolysis [17]. Taj Awan et al. studied orange wastes as a biomass for second-generation ethanol production using low-cost enzymes and co-culture fermentation. Their results showed a successful conversion of orange waste into a mixture of fermentable sugars using low-cost enzymes obtained from the citrus-canker bacterium Xanthomonas axonopodis pv. citri strain 306 (IBSBF 1594). They found that co-culture fermentation was enabled, reducing fermentation time to 6 h, and pure ethanol of 98.9% was obtained from sugar conversion [18].
Automated disease classification in (Selected) agricultural crops using transfer learning
Published in Automatika, 2020
Krishnaswamy Rangarajan Aravind , Purushothaman Raja
Overall, the accuracy has dropped significantly when the models were provided with the test set. It was interesting to observe that VGG16 resulted in the best accuracy (i.e. 90%) compared to other deeper architectures such as GoogLeNet (i.e. 60%), VGG19 (80%), ResNet101 (76%) and DenseNet201 (64%) using the segmented images. The class which was misclassified in almost all cases was Cercospora leaf spot with VGG16. It was falsely classified as citrus canker as the symptoms of this disease were similar, hence the prediction score is 100% for citrus canker as shown in Figure 12.