In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

Wet chemistry was used to produce copper oxide nanoparticles (CuO NPs). The results indicated that most nanoparticles were bacillus-shaped and relatively uniform in size (less than 30 nm). The effect of synthesized CuO NPs on wheat (Triticum aestivum L.) germination and growth parameters was studied and compared to bulk Cu. The results showed that no significant difference was obtained in germination rate among all treatments. Bulk Cu additions significantly affect the mean germination rate and mean germination time. On the contrary, germinability was significantly affected by CuO NPs additions. Seed vigor index was calculated to demonstrate the superior treatment in wheat germination parameters, and the results confirmed that 0.1 mg L⁻¹ of CuO NPs could be successfully used to improve wheat seed germination. Moreover, the general average Cu concentrations in the plant tissue were 139 and 103 mg kg⁻¹ dry weight for bulk and CuO NPs, respectively, indicating the dissolution behavior of CuO NPs. The addition of CuO NPs (0.1 mg L⁻¹) promotes chlorophyll formation equal to 0.5 mg L⁻¹ of the bulk Cu addition. This means using nanoparticles as fertilizer could reduce 80% of traditional fertilizers. Nonetheless, Cu additions in both forms (NPs and bulk) reduce root growth substantially compared to control. The effective toxic dose (EC₅₀) for bulk Cu and CuO NPs was 0.37 mg L⁻¹ and 0.94 mg L⁻¹, respectively. The results indicated that approximately 2.5 times CuO NPs concentration is equal to the toxicity dose of bulk Cu due to lowered CuO NPs dissolution. Our study showed that Cu phytotoxicity is a non-nanosized effect and showed that plant-induced changes under environmentally real conditions should be considered when measuring the dissolution of CuO NPs near wheat plant roots. This study implies that using nano-CuO as a micronutrient amendment has a potential benefit rather than the soluble Cu salt for plant growth.

Anaerobic digestion (AD) is a biological-based technology that generates methane-enriched biogas. A microbial electrolysis cell (MEC) uses electricity to initiate bacterial oxidization of organic matter to produce hydrogen. This study determined the effect of energy production and waste treatment when using dairy manure in a combined AD and MEC (AD-MEC) system compared to AD without MEC (AD-only). In the AD-MEC system, a single chamber MEC (150 mL) was placed inside a 10 L digester on day 20 of the digestion process and run for 272 h (11 days) to determine residual treatment and energy capacity with an MEC included. Cumulative H₂ and CH₄ production in the AD-MEC (2.43 L H₂ and 23.6 L CH₄) was higher than AD-only (0.00 L H₂ and 10.9 L CH₄). Hydrogen concentration during the first 24 h of MEC introduction constituted 20% of the produced biogas, after which time the H₂ decreased as the CH₄ concentration increased from 50% to 63%. The efficiency of electrical energy recovery (ηE) in the MEC was 73% (ηE min.) to 324% (ηE max.), with an average increase of 170% in total energy compared to AD-only. Chemical oxygen demand (COD) removal was higher in the AD-MEC (7.09 kJ/g COD removed) system compared to AD-only (6.19 kJ/g COD removed). This study showed that adding an MEC during the digestion process could increase overall energy production and organic removal from dairy manure.

Currently, there are challenges with proper disposal of cassava processing wastewater, and a need for sustainable energy in the cassava industry. This study investigated the impact of co-digestion of cassava wastewater (CW) with livestock manure (poultry litter (PL) and dairy manure (DM)), and porous adsorbents (biochar (B-Char) and zeolite (ZEO)) on energy production and treatment efficiency. Batch anaerobic digestion experiments were conducted, with 16 treatments of CW combined with manure and/or porous adsorbents using triplicate reactors for 48 days. The results showed that CW combined with ZEO (3 g/g total solids (TS)) produced the highest cumulative CH₄ (653 mL CH₄/g VS), while CW:PL (1:1) produced the most CH₄ on a mass basis (17.9 mL CH₄/g substrate). The largest reduction in lag phase was observed in the mixture containing CW (1:1), PL (1:1), and B-Char (3 g/g TS), yielding 400 mL CH₄/g volatile solids (VS) after 15 days of digestion, which was 84.8% of the total cumulative CH₄ from the 48-day trial. Co-digesting CW with ZEO, B-Char, or PL provided the necessary buffer needed for digestion of CW, which improved the process stability and resulted in a significant reduction in chemical oxygen demand (COD). Co-digestion could provide a sustainable strategy for treating and valorizing CW. Scale-up calculations showed that a CW input of 1000–2000 L/d co-digested with PL (1:1) could produce 9403 m³ CH₄/yr using a 50 m³ digester, equivalent to 373,327 MJ/yr or 24.9 tons of firewood/year. This system would have a profit of $5642/yr and a $47,805 net present value.