The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency.

Crop disease detection with remote sensing is a challenging area that can have significant economic and environmental impact on crop disease management. Spectroscopic remote sensing in the visible and near-infrared (NIR) region has the potential to detect crop changes due to diseases. Soybean cyst nematode (SCN) and sudden death syndrome (SDS) are two common soybean diseases that are extremely difficult to detect in the early stages under mild to moderate infestation levels. The objective of this research study was to relate leaf reflectance to disease conditions and to identify wavebands that best discriminated these crop diseases. A microplot experiment was conducted. Data collected included 800 leaf spectra, corresponding leaf chlorophyll content and disease rating of four soybean cultivars grown under different disease conditions. Disease conditions were created by introducing four disease treatments of control (no disease), SCN, SDS, and SCN+SDS. Crop data were collected on a weekly basis over a 10-week period, starting from 71 days after planting (DAP). The correlation between disease rating and selected vegetation indices (VI) were evaluated. Wavebands with the most disease discrimination capability were identified with stepwise linear discriminant analysis (LDA), logistic discriminant analysis (LgDA) and linear correlation analysis of pooled data. The identified band combinations were used to develop a classification function to identify plant disease condition. The best correlation (>0.8) between disease rating and VI occurred during 112 DAP. Both LDA and LgDA identified several bands in the NIR, red, green and blue regions as critical for disease discrimination. The discriminant models were able to detect over 80% of the healthy plants accurately under cross-validation but showed poor accuracy in discriminating individual diseases. A two-class discriminant model was able to identify 97% of the healthy plants and 58% of the infested plants as having some disease from the plant spectra.

Biopolymers are an emerging class of materials being widely pursued due to their ability to degrade in short periods of time. Understanding and evaluating the recyclability of biopolymers is paramount for their sustainable and efficient use in a cost-effective manner. Recycling has proven to be an important solution, to control environmental and waste management issues. This paper presents the first recycling assessment of Solanyl, Bioflex, polylactic acid (PLA) and PHBV using a melt extrusion process. All biopolymers were subjected to five reprocessing cycles. The thermal and mechanical properties of the biopolymers were investigated by GPC, TGA, DSC, mechanical test, and DMA. The molecular weights of Bioflex and Solanyl showed no susceptible effect of the recycling process, however, a significant reduction was observed in the molecular weight of PLA and PHBV. The inherent thermo-mechanical degradation in PHBV and PLA resulted in 20% and 7% reduction in storage modulus, respectively while minimal reduction was observed in the storage modulus of Bioflex and Solanyl. As expected from the Florry-Fox equation, recycled PLA with a high reduction in molecular weight (78%) experienced 9% reduction in glass transition temperature. Bioflex and Solanyl showed 5% and 2% reduction in molecular weight and experienced only 2% reduction in glass transition temperature. These findings highlight the recyclability potential of Bioflex and Solanyl over PLA and PHBV.