Destructive methods for monitoring stress responses remain a bottleneck in precision agriculture. This study presents a non-destructive methodological framework evaluating drought responses in 30 Opuntia ficus-indica plants over four months under five irrigation levels. Cladode traits (color, weight, and thickness) were measured alongside RGB imagery from a UAV and hyperspectral imaging (400–1000 nm). Partial least squares regression (PLSR) models showed high capability to model proline (R² = 0.91), chlorophyll a (R² = 0.97), and total chlorophyll (R² = 0.97) within the experimental dataset. Crucially, these models reflected continuous spectral–physiological variation across the irrigation gradient rather than discrete treatment separation, with key spectral regions identified at 530–600 nm and 550–750 nm. UAV-derived RGB imagery enabled the estimation of plant area and biomass (R² = 0.88). Under extreme drought, cladode thickness decreased by approximately 41%, accompanied by reduced biomass and increased soluble solids (°Brix). While no statistically significant differences were observed among irrigation treatments for biochemical variables, limiting treatment discrimination based on discrete classification, the hyperspectral data successfully captured the underlying continuous physiological variation. Consequently, this work demonstrates the methodological viability of integrating UAV structural phenotyping and hyperspectral analysis as a continuous monitoring tool rather than a rigid classification system. These findings provide a methodological baseline that highlights the need for continuous sensing in CAM plants, though further validation with independent datasets remains essential for wider operational application.

A two-row upland seeder for cabbage was designed, developed, and tested to improve sowing efficiency and crop establishment in small-scale farming. It featured precision seeding and was evaluated with and without press wheels. Seed delivery rates decreased as ground speed increased, with the highest rate of 0.267 kg·ha ⁻¹ achieved at 0.6 m·s ⁻¹ ; faster speeds lowered accuracy. Slippage tests at 10 RPM showed stable traction with an average slippage of 3.92%. Field trials revealed that press wheels significantly enhanced seeding performance, providing more consistent sowing depths (mean 6.6 mm), higher germination rates (61%), and fewer missing hills than without press wheels. Statistical analysis (ANOVA, Tukey’s HSD) confirmed that press wheels had significant effects on sowing depth, germination rate, and seedling count ( p <0.05). Despite some issues like seed clogging and hopper damage, the seeder was user-friendly and effective. The study highlights the need for optimal speeds and press wheels to improve planting precision and crop establishment.

The water hammer pump presents a promising solution for water supply in water-scarce regions. In Mexico, despite abundant overall water resources, many arid areas still lack access to reliable water. This study focused on optimizing the design and performance of low-cost water hammer pumps, which are powered by river currents and require minimal maintenance. While these pumps offer a sustainable solution, they face challenges such as water wastage, noise, and potential for low efficiency. Specifically, this research aimed to evaluate the impact of air chamber design on pump performance. The materials used in the prototype included steel pipes, steel plates, connectors, and lift valves for the air chambers. The pump itself utilized steel reducers, elbows, bronze waste valves, and a steel frame. Testing materials comprised plastic hoses, a digital stopwatch, an overhead tank, graduated cylinders, and plastic buckets. During the design phase, water flow rate, head height, and pipe diameters were taken into account. The Michaud-Venso formula and Allievi's equation were used to calculate pressure and wave velocity. A prototype featuring welded air chambers and waste valves was fabricated for functional testing. Laboratory performance tests were conducted using a 0.15 m³ tank at 1.0 m and 1.2 m heights, with 52 mm and 76 mm inlet pipe diameters. The pump's power output was not measured. Specifically, air chamber sizes of approximately 2690 cm³ resulted in a 15% increase in performance. The lowest efficiency, 5%, for a 76 mm waste valve, occurred with an 880 cm³ air chamber, while the highest efficiency (17.0%) was achieved with a 6108 cm³ air chamber. The volume of water collected at the outlet was dependent on the air chamber size. As the air chamber volume increased, the flow rate at the inlet valve also increased. Therefore, optimally sizing both air chambers and waste valves is significant to maximizing pump efficiency.

Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m⁻¹. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m⁻¹). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments.