Agriculture is the most important industry for human survival and solving the hunger problem worldwide. With the growth of the global population, the demand for food is increasing, which needs more agriculture labor. However, the number of people willing to engage in agricultural work is decreasing, causing a severe shortage of agricultural labor. Therefore, it is necessary to study the mode of agricultural production without labor force participation. With the rapid development of the Internet of Things, Big Data, artificial intelligence, robotics and fifth-generation (5G) communication technology, robots can replace humans in agricultural operations, thus enabling the establishment of unmanned farms in the near future. In this review, we have defined unmanned farms, introduced the framework of unmanned farms, analyzed the current state of the technology and how these technologies can be used in unmanned farms, and finally discuss all the technical challenges. We believe that this review will provide guidance for the development of unmanned farms and provide ideas for further investigation of these farms.

A SPEC/AuNPs/PMB modified electrode was prepared by electrodeposition and electro-polymerization. The electrochemical behavior of reduced nicotinamide adenine dinucleotide (NADH) on the surface of the modified electrode was studied by cyclic voltammetry. A certain amount of substrate and glutamate dehydrogenase (GLDH) were coated on the modified electrode to form a functional enzyme membrane. The ammonia nitrogen in the water sample could be calculated indirectly by measuring the consumption of NADH in the reaction. The results showed that the strength of electro-catalytic current signal was increased by two times; the catalytic oxidation potential was shifted to the left by 0.5 V, and the anti-interference ability of the sensor was enhanced. The optimum substrate concentration and enzyme loading were determined as 1.3 mM NADH, 28 mM α-Ketoglutarate and 2.0 U GLDH, respectively. The homemade ceramic heating plate controlled the working electrode to work at 37 °C. A pH compensation algorithm based on piecewise linear interpolation could reduce the measurement error to less than 3.29 μM. The biosensor exhibited good linearity in the range of 0~300 μM with a detection limit of 0.65 μM NH₄⁺. Compared with standard Nessler’s method, the recoveries were 93.71~105.92%. The biosensor was found to be stable for at least 14 days when refrigerated and sealed at 4 °C.

In this paper, a new nanocomposite AuNPs/MXene/ERGO was prepared for sensitive electrochemical detection of nitrite. The nanocomposite was prepared by a facile one-step electrodeposition, HAuCl4, GO and MXene mixed in PBS solution with the applied potential of −1.4 V for 600 s. The modified material was characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) and cyclic voltammetry (CV). The electrochemical behavior of nitrite at the modified electrode was performed by CV and chronoamperometry. The AuNPs/MXene/ERGO/GCE showed a well-defined oxidation peak for nitrite at +0.83 V (Vs. Ag/AgCl) in 0.1 M phosphate buffer solution (pH 7). The amperometric responses indicated the sensor had linear ranges of 0.5 to 80 μM and 80 to 780 μM with the LOD (0.15 μM and 0.015 μM) and sensitivity (340.14 and 977.89 μA mM⁻¹ cm⁻²), respectively. Moreover, the fabricated sensor also showed good selectivity, repeatability, and long-term stability with satisfactory recoveries for a real sample. We also propose the work that needs to be done in the future for material improvements in the conclusion.

Nitrite is common inorganic poison, which widely exists in various water bodies and seriously endangers human health. Therefore, it is very necessary to develop a fast and online method for the detection of nitrite. In this paper, we prepared an electrochemical sensor for highly sensitive and selective detection of nitrite, based on AuNPs/CS/MXene nanocomposite. The characterization of the nanocomposite was demonstrated by scanning electron microscopy (SEM), a transmission electron microscope (TEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). Under the optimized conditions, the fabricated electrode showed good performance with the linear range of 0.5–335.5 μM and 335.5–3355 μM, the limit of detection is 69 nM, and the sensitivity is 517.8 and 403.2 μA mM⁻¹ cm⁻². The fabricated sensors also show good anti-interference ability, repeatability, and stability, and have the potential for application in real samples.

A surface-enhanced Raman scattering (SERS) method for measuring nitrate nitrogen in aquaculture water was developed using a substrate of β-cyclodextrin-modified gold nanoparticles (SH-β-CD@AuNPs). Addressing the issues of low sensitivity, narrow linear range, and relatively poor selectivity of single metal nanoparticles in the SERS detection of nitrate nitrogen, we combined metal nanoparticles with cyclodextrin supramolecular compounds to prepare a AuNPs substrate enveloped by cyclodextrin, which exhibits ultra-high selectivity and Raman activity. Subsequently, vanadium(III) chloride was used to convert nitrate ions into nitrite ions. The adsorption mechanism between the reaction product benzotriazole (BTAH) of o-phenylenediamine (OPD) and nitrite ions on the SH-β-CD@AuNPs substrate was studied through SERS, achieving the simultaneous detection of nitrate nitrogen and nitrite nitrogen. The experimental results show that BTAH exhibits distinct SERS characteristic peaks at 1168, 1240, 1375, and 1600 cm⁻¹, with the lowest detection limits of 3.33 × 10⁻², 5.84 × 10⁻², 2.40 × 10⁻², and 1.05 × 10⁻² μmol/L, respectively, and a linear range of 0.1–30.0 μmol/L. The proposed method provides an effective tool for the selective and accurate online detection of nitrite and nitrate nitrogen in aquaculture water.

In the aquaculture industry, maintaining stable levels of dissolved oxygen (DO) is crucial for ensuring the health of aquatic organisms and enhancing farming efficiency. This article delves into the challenges faced in predicting and controlling DO levels, such as the need for real-time monitoring and response, the complexity of systems, and limitations in technology and resources. The paper comprehensively reviews various methods for DO prediction and control, including mechanistic modeling prediction, machine learning techniques, and both classical and intelligent control strategies. It analyzes their advantages, limitations, and applicability in aquaculture environments. Through this review and analysis, the article provides more comprehensive insights and guidance for future research directions in DO prediction and control in aquaculture.