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Farmers are encouraged to invest in crops that will grow and produce under the predicted climate change, such as global warming, limited water supplies, and drought-imposed water restrictions on agriculture. The drought-tolerant pomegranate (Punica granatum L.) is a promising horticulture crop that can be grown under arid conditions. To successfully implement irrigation management strategies in orchards, knowledge of plant responses to water deficits, tree shoot and fruit growth patterns, and irrigation management strategies that conserve water is required. The present study described the response of pomegranate trees of the Manfalouty variety to deficit irrigation under the local climate. The experiment was conducted during the 2020 and 2021 seasons. The experiment was carried out on a private farm in the Bani Uday district of Manfalouty, Assiut Governorate, Egypt. Eighteen trees with comparable strength and growth were chosen, free of elemental deficiency signs, planted at 3.5 × 3.5 m, with an age of 8 years. Three irrigation levels 50%, 75%, and 100% of the pomegranate tree’s water standard were applied. With the randomized complete block design (RCBD), each treatment was used three times, with two trees in each iteration. Results revealed that applying irrigation at a rate of 75% enhanced fruit quality and yield, mitigated sun sting and cracking, and improved vegetative growth. Moreover, water use efficiency (WUE) was increased, which was reflected in the increase of the percentage of commercial and marketing fruits and decreased production costs when applying 75% of the water ration, which maintains soil and natural resources for sustainable agriculture.

Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400–1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0–98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.