Agriculture and plants, which are a component of a nation's internal economy, play an important role in boosting the economy of that country. It becomes critical to preserve plants from infection at an early stage in order to be able to treat them. Previously, recognition and classification were carried out by hand, but this was a time-consuming operation. Nowadays, deep learning algorithms are frequently employed for recognition and classification tasks. As a result, this manuscript investigates the diseases of sunflower leaves, specifically Alternaria leaf blight, Phoma blight, downy mildew, and Verticillium wilt, and proposes a hybrid model for the recognition and classification of sunflower diseases using deep learning techniques. VGG-16 and MobileNet are two transfer learning models that are used for classification purposes, and the stacking ensemble learning approach is used to merge them or create a hybrid model from the two models. This work makes use of a data set that was built by the author with the assistance of Google Images and comprises 329 images of sunflowers divided into five categories. On the basis of accuracy, a comparison is made between several existing deep learning models and the proposed model using the same data set as the original comparison.

Natural biomaterials have favored human society for ages. Nevertheless, in late years, the tailoring of natural materials for diverse biomedical applications has turned into a core of attention, quarterbacked via...

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Agriculture is an important component of the concept of sustainable development. Given the projected population growth, sustainable agriculture must accomplish food security while also being economically viable, socially responsible, and having the least possible impact on biodiversity and natural ecosystems. Deep learning has shown to be a sophisticated approach for big data analysis, with several successful cases in image processing, object identification, and other domains. It has lately been applied in food science and engineering. Among the issues and concerns addressed by these systems were food recognition; quality detection of fruits, vegetables, meat, and aquatic items; food supply chain; and food contamination. In precision agriculture, Artificial Intelligence (AI) is a commonly used technology for estimating food quality. It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. In this paper, we use Convolutional Neural Networks (CNNs)-based U-Net, DeepLab, and Mask R-CNN models to detect and predict postharvest deterioration zones in stored apple fruits. Our approach is unique in that it segmented and predicted postharvest decay and nondecay zones in fruits separately. This review will focus on postharvest physiology and management of fruits and vegetables, including harvesting, handling, packing, storage, and hygiene, to reduce postharvest loss (PHL) and improve crop quality. It will also cover postharvest handling under extreme weather conditions and potential impacts of climate change on vegetable postharvest and postharvest biotechnology on PHL.

Numerous Algae oils with low and medium viscosity were investigated as fuel for CI engines. However, high viscous algae oil has not been explored in detail as a replacement for diesel in CI engines due to operational problems and poor performance characteristics. Esterification of neat algae oil to obtain its biodiesel is a complex process. The biodiesel obtained also has viscosity nearly five times more than diesel viscosity. Hence, research efforts on CI engines using algae oil methyl ester are lacking, particularly in combustion characteristics. This work focuses on utilizing algae oil as a fuel in CI engines. Algae oil has more affinity for alcohols due to a higher percentage of ricinoleic acid which aids in forming a homogeneous mixture. Alcohols with better fuel properties improve the combustion capability of algae oils with low and medium viscosity. However, not much research has been carried out in alcohols with very high viscous algae oil. Hence, in this work, higher and lower-order alcohols were blended with algae oil in their neat form and their biodiesel with Al2O3 nanoadditives for performance improvement.

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