A majority of the population of developing countries is associated with agriculture directly or indirectly. The liaison of engineering technology and Sustainable Development Goals (SDGs) can build a bridge for farmers to enhance their skills regarding advancements through future generation agriculture trends. The next-generation trends include better soil preparation, intelligent irrigation systems, advanced methods of crop nutrient inspection, smart fertilizers applications, and multi-cropping practices. This work proposes a smart Decision Support System (DSS) that acquires the input parameters based on real-time monitoring to optimize the yield that realizes sustainability by improving per hectare production and lessening water seepage wastage in agribusiness. The proposed model comprises three basic units including an intelligent sensor module, smart irrigation system and controlled fertilizer module. The system has integrated sensors, cloud employing decision support layers, and networking based DSS to recommend cautions for optimum sustainable yield. The intelligent sensors module contains a temperature and humidity sensor, NPK sensor, soil moisture sensor, soil conductivity sensor, and pH sensor to transmit the statistics to the cloud over the internet via Long Range (LoRa) using Serial Peripheral Interface (SPI) communication protocol. Moreover, an android application has been developed for real-time data monitoring according to GPS location and node information (accessed remotely). Furthermore, the DSS contemplates the accessible information from sensors, past patterns, monitoring climate trends and creating cautions required for sustainable fertilizer consumption. The presented results and comparison validate the novelty of the design as it embraces smart irrigation with smart control and smart decision-making based on accurate real-time field data. It is better than existing systems as it transmits the data over the LoRa that is an open-source communication with long-range transmission ability up to several kilometres. The sensor nodes helped in advancing the yield of crops, which resulted in achieving inclusive and sustainable economic goals.

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This study aimed to examine the levels of Pakistani university learners' higher order thinking skills (HOTS) in English reading comprehension and to explore how explicit assessments of HOTS can reveal learners' strengths and weaknesses in reading as compared to the traditional assessment of reading. Secondly, the study analyzed the effect of learners' metacognitive awareness on reading on the relationship between their HOTS levels and reading performance. It used a mixed-method approach. In the quantitative phase, an explicit HOTS-based reading test was designed using Bloom's taxonomy. Using random stratified sampling, it was administered to the learners of Bachelor of Science in English (n = 200). Stratified sampling was performed for investigating the gender gap in HOTS levels. The learners' metacognitive awareness of reading was analyzed using Survey of Reading Strategies. Descriptive analysis, independent sample t-test, correlation analysis, and moderator analysis were conducted. It was found that the learners' HOTS levels correlated with their reading performance where creative skill was the strongest, and evaluating skill was the weakest. Metacognitive awareness of reading positively moderated the relationship between the learners' HOTS levels and reading performance. In the qualitative phase, purposive stratified sampling was applied. A subsample (n = 6) was extracted from the core sample based on the high, average, and low reading performance, and three groups were formed. A semi-structured interview was conducted to know learners' opinions on using HOTS-based assessment in reading and their cognitive and metacognitive abilities. The thematic analysis of the qualitative data revealed that three out of six learners did not approve of the way they were assessed by the university. They informed that they had issues with cognitive and metacognitive control of English texts. The findings revealed that the majority of learners suffered low HOTS levels in English reading. The study offers recommendations for English as a second language (ESL) teachers, assessors, and policymakers to improve learner performance to the possible optimal level, especially in underdeveloped contexts that use traditional reading assessments.

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