Design and Evaluation of a Non-Intrusive Corn Population Sensor

Specific objectives of this study were to develop, prototype, and test a corn population sensor. Both intrusive mechanical and non-intrusive capacitive techniques have been used to develop the stalk population sensors in previous research. However, neither could generate consistent performance. The mechanical method required high maintenance and resulted in significant underestimations of stalk counts. The performance of capacitive systems was limited by inadequate sensing distance, especially at low stalk moisture levels. In this research, the sensitivity of the capacitive sensor was optimized for corn stalks. This system utilized a single-sided capacitive sensor, Wien bridge oscillator, phase-locked loop, and an operational amplifier to transform stalk presence to a change in electrical potential signal. The capacitive sensor patterns were simulated using the finite element method, which provided useful conceptual information. A number of different detection element patterns were modeled and tested. The patterns examined included single-sided two-plate, interdigital, polarized interdigital, semi-interdigital, and solid ground electrode. The key parameters affecting pattern sensitivity were investigated. The most promising pattern, the solid ground electrode, was selected for further evaluation and development. The solid ground electrode detection element was incorporated into circuitry including Wien-Bridge oscillator, a phase-locked loop used as a high-speed frequency-tovoltage converter, and an operational amplifier to provide impedance matching and maximize data acquisition resolution. The operational configuration, optimum operating parameters, and associated component sizes were determined using both modeling and laboratory testing. With an acceptable signal-sided pattern and signal-to-noise ratio, this sensing system was investigated in a realistic production environment. A preliminary field test was used to evaluate the sensor system (including a protective housing and mounting system) and data acquisition system to identify problems before conducting the final field test. Stalk moisture content and harvest speed were used as treatment blocks in the final test. The influences of environmental and mechanical noise and the noise-like influence of corn leaves and weeds were also investigated. The final field test accurately simulated realistic harvesting conditions and real-time data was collected for stalk identification analysis. Post-acquisition processing, feature extraction, and principal component analysis of the extracted features were performed on the raw field data. Three sensor signal features were selected to identify stalks. A backpropagation artificial neural network technique was used to develop the pattern classification model. Numerous neural network structures were evaluated and two-layer structure with four neurons in the first layer and one neuron in the second layer was selected based on maximum prediction precision and accuracy and minimum structure complexity. This structure was then evaluated to determine the prediction accuracy at various resolution levels. Results showed that the model can predict stalk population at 99.5% accuracy when the spatial resolution is 0.025 ha. The sensor can predict stalk population with a 95% accuracy when the resolution is a 9-meter row segment (approximately 10 seconds)

Ultrasound and Microwave Assisted Extraction of Soybean Oil

Soybeans, one of the world’s leading cash crops, contain appropriately 20% lipid. Currently, soybean oil is the principal vegetable oil used in the U.S. and the world market, totaling 29% of the world vegetable oil production in 2001. Development of extraction technology that could increase oil yield would thus significantly enhance the profitability of the soybean industry and reduce processing costs significantly. It has been shown that both ultrasound-assisted and microwave-assisted extraction methods can enhance extraction of phytochemicals from plant sources while reducing processing time and solvent consumption. However, little is known about how ultrasound and microwave can affect the soybean oil extraction. The main objectives of the research were to (1) determine the effect of ultrasound on soybean oil extraction, (2) determine the effect of microwave on soybean oil extraction, and (3) study the effect of different solvents on soybean oil extraction. Thus, this study aimed to compare the yield, composition, and quality of extracted soy oil obtained by traditional and ultrasound-assisted or microwave-assisted processing methods. The effects of different solvents and levels of ultrasound or microwave treatment on the extracted soybean oil were evaluated. Both laboratory-scale ultrasound-assisted and microwave-assisted soybean oil extraction procedures were developed in this research. Ultrasound-assisted extraction was found to be a simpler and more effective alternative to traditional methods for soybean oil extraction. Two different soybean or using a traditional procedure without ultrasound application. It was found that the ultrasound-assisted extraction of oil from soybeans yielded greater oil than the traditional method regardless of soybean variety. Higher sonication intensity allowed for more efficient oil extraction (faster and greater oil yield). The solvent influenced the sonication enhancement, i.e., the oil yield extracted by this procedure was highest when using hexane:isopropanol solvent mixture. Fatty acid profile analysis measured by GC indicated that the oil quality did not appreciably change from traditionally extracted oil when ultrasound assistance was used. varieties (TN 96-58, N 98-4573) were used in the experiments. Oil was extracted using different solvents [hexane, isopropanol, and mixed solvent (hexane:isopropanol 60%:40% v/v)] under either direct sonication by an ultrasonic probe at intensity levels ranging from 16.4 Wcm-2 to 47.6 Wcm-2 or using a traditional procedure without ultrasound application. It was found that the ultrasound-assisted extraction of oil from soybeans yielded greater oil than the traditional method regardless of soybean variety. Higher sonication intensity allowed for more efficient oil extraction (faster and greater oil yield). The solvent influenced the sonication enhancement, i.e., the oil yield extracted by this procedure was highest when using hexane:isopropanol solvent mixture. Fatty acid profile analysis measured by GC indicated that the oil quality did not appreciably change from traditionally extracted oil when ultrasound assistance was used. Microwave-assisted extraction can increase the oil yield and extraction process rate. Three different solvents (hexane, isopropanol, and a mixed solvent (hexane:isopropanol 60%:40%, v/v)) were used to extract soybean oil after being irradiated with microwaves (2450 MHz) at increasing reaction times. Oil yield obtained with microwave irradiation was highest using the mixed solvent. Microwave-assisted extraction of oil from soybeans yielded markedly higher oil percentages than traditional method, i.e., oil yield of microwave-assisted group was 0.45 g higher than that of control group during a 2 hr. extraction time. Increased microwave reaction times increase oil as well. For the mixed solvent, 4.17 g more oil was extracted when the reaction time changed from 30 to 120 min. Both ultrasound and microwave treatment had a positive effect on soybean oil extraction. Increases in soybean oil yield were achieved with the hexane:isopropanol mixed solvent. Further research is planned to evaluate the potential of ultrasound- and microwave-assistance in soybean oil extraction

Relational Sentence Embedding for Flexible Semantic Matching

We present Relational Sentence Embedding (RSE), a new paradigm to further discover the potential of sentence embeddings. Prior work mainly models the similarity between sentences based on their embedding distance. Because of the complex semantic meanings conveyed, sentence pairs can have various relation types, including but not limited to entailment, paraphrasing, and question-answer. It poses challenges to existing embedding methods to capture such relational information. We handle the problem by learning associated relational embeddings. Specifically, a relation-wise translation operation is applied to the source sentence to infer the corresponding target sentence with a pre-trained Siamese-based encoder. The fine-grained relational similarity scores can be computed from learned embeddings. We benchmark our method on 19 datasets covering a wide range of tasks, including semantic textual similarity, transfer, and domain-specific tasks. Experimental results show that our method is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art sentence embedding methods. https://github.com/BinWang28/RSEComment: RepL4NLP at ACL 202