Prediction of mustard yield using different machine learning techniques: a case study of Rajasthan, India.

Mustard is the second most important edible oilseed after groundnut for India. Adverse weather drastically reduces the mustard yield. Weather variables affect the crop differently during different stages of development. Weather influence on crop yield depends not only on the magnitude of weather variables but also on weather distribution pattern over the crop growing period. Hence, developing models using weather variables for accurate and timely crop yield prediction is foremost important for crop management and planning decisions regarding storage, import, export, etc. Machine learning plays a significant role as it has a decision support tool for crop yield prediction. The models for mustard yield prediction was developed using long-term weather data during the crop growing period along with mustard yield data. Techniques used for developing the model were variable selection using stepwise multiple linear regression (SMLR) and artificial neural network (SMLR-ANN), variable selection using SMLR and support vector machine (SMLR-SVM), variable selection using SMLR and random forest (SMLR-RF), variable extraction using principal component analysis (PCA) and ANN (PCA-ANN), variable extraction using PCA and SVM (PCA-SVM), and variable extraction using PCA and RF (PCA-RF). Optimal combinations of the developed models were done for improving the accuracy of mustard yield prediction. Results showed that, on the basis of model accuracy parameters nRMSE, RMSE, and RPD, the PCA-SVM model performed best among all the six models developed for mustard yield prediction of study areas. Performance of mustard yield prediction done by optimum combinations of the models was better than the individual model.

Comparison of ultrasound-guided intermediate vs subcutaneous cervical plexus block for postoperative analgesia in patients undergoing total thyroidectomy: A randomised double-blind trial.

BACKGROUND AND AIMS: Intermediate cervical plexus block (CPB) is a new procedure whose analgesic efficacy compared to superficial cervical plexus block is yet to be established. We compared the analgesic efficacy of superficial vs intermediate CPB for post-operative analgesia after thyroid surgery. METHODS: Forty-five patients with American Society of Anaesthesiologists' physical status 1 or 2 undergoing total thyroidectomy were recruited. Forty-four patients in superficial/subcutaneous CPB group (n = 22) and intermediate CPB (n = 22) received 20 mL 0.25% bupivacaine with adrenaline 100 µg bilaterally in ultrasound-guided superficial and intermediate cervical plexus block before induction of general anaesthesia., respectively. The primary outcome measure was the postoperative visual analogue scale (VAS) scores at 0, 2, 4, 6, 12 and 24. Secondary outcome measures included the total dose of rescue analgesic required, duration of postoperative analgesia and patient's satisfaction score. Statistical analysis was with the Mann-Whitney U test and independent t-test. RESULTS: The post-operative VAS scores were lower in intermediate CPB group compared to superficial CPB group at 2, 4, 6, 12, 18 and 24 h [P < 0.05]. Time tofirst rescue analgesic demand was prolonged 10.06 ± 3.62 h in intermediate group compared to 7.94 ± 3.62 h in superficial group [P = 0.017] and total analgesic consumption were lower in intermediate group (71.25 ± 16.70 µg) than the superficial group (101.25 ± 50.31 µg) [P = 0.011]. CONCLUSION: Ultrasound-guided intermediate CPB reduces post-operative pain scores, prolongs duration of analgesia and decreases demands for rescue analgesia compared to superficial CPB.