Search

Computer vision and deep learning methods have attracted substantial interest in agriculture owing to their contactless, non-destructive nature. These technologies are increasingly being embedded in modern farming platforms, such as aquaponics, to tackle the barriers limiting their commercial expansion and industrial-scale deployment. Aquaponics is a production technique that fuses a recirculating aquaculture setup with soilless hydroponic cultivation, holding promise for addressing food security challenges. To supplement existing investigative efforts, a procedure is proposed to automatically quantify crop morphological features—namely, width, length, and area—and to infer the effective planting distance between grow channels. Planting distance is a core design variable that depends on crop type and its morphological expression; it therefore requires continuous surveillance to safeguard high yield and quality, which can be compromised by canopy occlusion or overlapping as plants develop. The presented pipeline applies Mask R-CNN to estimate crop dimensions and a mathematical formulation to determine plant spacing tailored to a self-adapting aquaponics installation. For common little gem romaine lettuce, growth is predicted within a 2 cm deviation for both length and width. The finalized model is served via a cloud-based platform and is coupled with an ontology model that encodes domain expertise in aquaponics systems. Pertinent knowledge regarding crop traits and ideal planting intervals is drawn from the ontology and cross-checked against the model’s outputs to propose follow-up measures. The introduced application derives its value as a decision-support framework, potentially paving the way for intelligent oversight and regulation.

Mango fruit exists in multiple cultivar types, and the principal maturity markers for quality assessment are total soluble solids (TSS) and pH. The investigation assessed the viability of coupling a handheld NIR spectrometer (NIRS) covering 740-1070 nm with multivariate algorithms and a smartphone to classify mango cultivars and assess their ripeness. A batch of 198 whole mango fruits was scanned using the NIR device; concurrently, a digital refractometer and a pH meter were used to quantify TSS and pH from extracted juice. Following various spectral preprocessing treatments, multivariate classification models—support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), neural network (NN), and a novel hybrid classifier (LDA-SVM)—were developed for varietal discrimination. Quantitative models predicting TSS and pH were constructed using partial-least-squares regression variants, including interval partial-least-squares (IPLS), synergy partial-least-squares (Si-PLS), and back interval-partial-least-squares (Bi-PLS). For classification, RAW, MC, SNV, FD, and SD preprocessing paired with the LDA-SVM classifier delivered perfect (100 %) varietal recognition in the training set and 97.44 % accuracy in the prediction set. For quantification, the optimal prediction model for both TSS and pH was Si-PLS, achieving R² values of 0.63 and 0.81, respectively and RMSEP values of 1.83 and 0.49, respectively. The study proved that swift, non-invasive estimation of TSS and pH is feasible by integrating a handheld NIR sensor with suitable chemometric analyses.