Anisur Rahman

Post‐harvest losses of onion remain a major challenge in developing regions due to inadequate storage infrastructure, resulting in substantial economic losses for smallholder farmers. This study presents the design and evaluation of a low‐cost IoT‐assisted onion storage system that integrates real‐time environmental monitoring with automated ventilation control under ambient tropical conditions. A distinguishing feature of the proposed system is the integration of locally available construction materials, sensor‐assisted ventilation control, and a techno‐economic assessment within a low‐cost IoT‐assisted onion storage platform for resource‐constrained farming conditions. The system incorporates a microcontroller‐based platform with temperature–humidity and MQ‐series gas sensors for environmental monitoring, automated ventilation, and indicative spoilage detection. An 80‐day storage experiment was conducted using 134 kg of Murikata onions, with storage performance evaluated at eight sampling dates by comparing the proposed system with conventional storage practices. The IoT‐assisted system maintained temperature and relative humidity within ranges of 18.81°C–28.17°C and 54.32%–82.60%, respectively, while exhibiting improved environmental stability relative to traditional storage. Exploratory statistical comparisons of the repeated observations indicated statistically significant reductions in interval weight loss ( p = 0.027) and sprouting loss ( p = 0.033), whereas rotten loss did not differ significantly ( p = 0.952). Total weight loss decreased from 27.27% in traditional storage to 19.96% in the proposed system, while sprouting loss declined from 1.53% to 0.37%. Consequently, marketable yield increased from 68.22% to 76.81%. The MQ‐136 sensor demonstrated an average spoilage‐detection efficiency of approximately 85%. A preliminary techno‐economic assessment estimated an implementation cost of approximately 110USD. Additionally, periods with approximately 7 h day ⁻¹ of fan operation were associated with favorable storage performance during the experimental period. Overall, the findings suggest that integrating IoT‐based monitoring with controlled ventilation may provide an approach for reducing onion post‐harvest losses and improving marketable yield under resource‐constrained tropical conditions.

The rapid, nondestructive assessment of tamarind quality is crucial for grading, pricing, and industrial standardization. Therefore, the purpose of this study is to classify and predict intact tamarind quality based on soluble solid content (SSC) by combining short‐wave infrared (SWIR) spectroscopy with variable selection algorithms and chemometric models. SWIR absorbance spectra (900–1700 nm) were acquired from 120 samples of intact tamarind. Partial least squares‐discriminant analysis (PLS‐DA) and partial least squares‐regression (PLS‐R) models were developed using full spectra and various spectral preprocessing techniques to classify and predict SSC of tamarind, respectively, and the developed models were evaluated. The multiplicative scatter correction (MSC) and Savitzky–Golay (SG) second derivatives were selected as the best options for classification and prediction, respectively. Then, competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and variable importance in projection (VIP) were applied to select effective wavelengths for developing simplified, more robust classification and prediction models for intact tamarind based on SSC. The PLS‐DA‐CARS model using MSC preprocessing achieved the best classification results with 97.2% accuracy, whereas PLS‐R‐CARS model with range normalization to predict SSC yielded a coefficient of prediction ( R _p ) of 0.85 and standard error of prediction (SEP) of 2.488%Brix.