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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.

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.

Bioplastics have emerged as a viable alternative to traditional petroleum-based plastic (PET). Three of the most common bioplastic polymers are polyhydroxybutyrate-valerate (PHBV), polylactide (PLA), and cellulose-based bioplastic (CBB). This study assessed biodegradation through anaerobic digestion (AD) of these three bioplastics and PET digested with food waste (FW) at mesophilic (35 °C) and thermophilic (55 °C) temperatures. The four plastic types were digested with FW in triplicate batch reactors. Additionally, two blank treatments (inoculum-only) and two PHBV treatments (with FW + inoculum and inoculum-only) were digested at 35 and 55 °C. The PHBV treatment without FW at 35 °C (PHBV-35) produced the most methane (CH4) normalized by the volatile solids (VS) of the bioplastics over the 104-day experimental period (271 mL CH4/g VS). Most bioplastics had more CH4 production than PET when normalized by digester volume or gram substrate added, with the PLA-FW-55 (5.80 m3 CH4/m3), PHBV-FW-55 (2.29 m3 CH4/m3), and PHBV-55 (4.05 m3 CH4/m3) having 848,275 and 561%, respectively, more CH4 production than the PET treatment. The scanning electron microscopy (SEM) showed full degradation of PHBV pellets after AD. The results show that when PHBV is used as bioplastic, it can be degraded with energy production through AD.

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.