This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning (ML). Almost all the analyzed works use the feedforward architecture, while the recurrent and hybrid networks are little exploited in the various tasks of the greenhouses. Throughout the document, different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The advantages and disadvantages of neural networks (NNs) are observed in the different applications in greenhouses, from microclimate prediction, energy expenditure, to more specific tasks such as the control of carbon dioxide. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in which systems involve technologies 4.0.

In high-fish-density aquaculture systems, tilapia producers are compelled to provide 100% of food required to obtain profitable growth rates. It is well known that fish have a low food conversion rate and feeding represents the most important expenditure, approximately 40% of total production cost. Therefore, precise quantities of food should be provided to avoid water pollution and economic losses due to food waste when water conditions are inadequate for fish feeding. A way to control food provisions in this work was determined by the conditions of temperature, dissolved oxygen, fish age, and body weight, since these variables have a direct effect on fish metabolism and growth. Thus, a change in metabolism is reflected in a modification of energy requirements and, as a consequence, in variations of food consumption. In this work, a new feeder with fuzzylogic control algorithms is proposed for fish feeding; this technique allows farmer knowledge to be taken into account in a series of if-then-type rules. To define these rules the temperature and dissolve oxygen were considered in order to provide precise food quantities. The results show minimal differences in growth (P [ 0.05) between treatments, important food saving of 29.12% (equivalent to 105.3 kg), and lower water pollution (reduced water dissolved solids and ammonium components) compared with timed feeders. This system provides an important contribution to sustainability of intensive aquaculture systems, increasing productivity and profitability, and optimizing water use.

Alginate is a polysaccharide with the property of forming hydrogels, which is economic production, zero toxicity, and biocompatibility. In the agro-industry, alginate is used as a super absorbent polymer, coating seeds, fruits, and vegetables and as a carrier of bacteria and fungi as plant-growth promoters and biocontrol. The latter has a high impact on agriculture since the implementation of microorganisms in a polymer matrix improves soil quality; plant nutrition, and is functional as a preventive measure for the appearance of phytopathogenic. Additionally, it minimizes losses of foods due to wrong post-harvest handling. In this review, we provide an overview of physicochemical properties of alginate, some methods for preparation and modification of capsules and coatings, to finally describe its application in agro-industry as a matrix of plant-growth-promoting microorganisms, its effectiveness in cultivation and post-harvest, and its effect on the environment, as well as the prospects for future agro-industrial applications.

Photothermal techniques allow the detection of characteristics of material without invading it. Researchers have developed hardware for some specific Phase and Amplitude detection (Lock-In Function) applications, eliminating space and unnecessary electronic functions, among others. This work shows the development of a Digital Lock-In Amplifier based on a Field Programmable Gate Array (FPGA) for low-frequency applications. This system allows selecting and generating the appropriated frequency depending on the kind of experiment or material studied. The results show good frequency stability in the order of 1.0 × 10−9 Hz, which is considered good linearity and repeatability response for the most common Laboratory Amplitude and Phase Shift detection devices, with a low error and standard deviation.

The density-dependents and physiological effects in bullfrog tadpoles (Rana catesbeiana Shaw, 1802) was evaluated to asses optimum stocking density. During 70 days, five treatments (1/4, 1/3, 1/2, 1 and 2 tadpole L -1 ) were evaluated under greenhouse conditions. Environmental variables, water quality parameters, biometric data, oxygen consumption and nitrogen excretion were measure every 3 weeks. At the end of experiment, the treatment of 1/4 tadpole L -1 obtained the highest weight, length and biomass. However, maximum survival (92.59%) and estimated final biomass (0.3051 g L . We suggest a stocking density of 1 tadpole per 3 L in order to minimize stress and optimize water.