As the most important resource for life, water has been a central issue on the international agenda for several decades. Yet, the world’s supply of clean freshwater is steadily decreasing due to extensive agricultural demand for irrigated lands. Therefore, water resources should be used with greater efficiency, and the use of non-traditional water resources, such as Treated Wastewater (TW), should be increased. Reusing TW could be an alternative option to increase water resources. Thus, many countries have decided to turn wastewater into an irrigation resource to help meet urban demand and address water shortages. However, because of the nature of that water, there are potential problems associated with its use in irrigation. Some of the major concerns are health hazards, salinity build-up, and toxicity hazards. The objectives of this comprehensive literature review are to illuminate the importance of using TW in irrigation as an alternative freshwater source and to assess the effects of its use on soil fertility and other soil properties, plants, and public health. The literature review reveals that TW reuse has become part of the extension program for boosting water resource utilization. However, the uncontrolled application of such waters has many unfavorable effects on both soils and plants, especially in the long-term. To reduce these unfavorable effects when using TW in irrigation, proper guidelines for wastewater reuse and management should be followed to limit negative effects significantly.

Biochar and biogas slurry have been proved to improve the quality of some soil types, but the long-term effects on fluvo-aquic soil are not fully understood. This study aimed to compare the continuity effects of peanut-shell biochar and biogas slurry on the physicochemical properties, microbial population size, and enzyme activities of fluvo-aquic soil. We conducted a four-year field experiment of winter wheat-summer maize rotation in the North China Plain. Along with equal nitrogen inputs, three treatments were applied—conventional fertilizers, peanut-shell biochar, and hoggery biogas slurry—after which various soil quality indicators were compared. Compared with those of control, both biochar and biogas slurry increased the soil total nitrogen and organic matter content, and improved soil aggregation, microbial biomass, and actinomycetes. Biogas slurry decreased soil pH and improved urease and protease activities. With biochar and biogas slurry treatments, wheat yield increased by 8.46% and 23.47%, and maize yield by 18% and 15.46%, respectively. Biogas slurry increased the content of crude protein and starch in the grains. Both biogas slurry and peanut-shell biochar improved fluvo-aquic soil nutrient content, water-stable macroaggregates, and microbial population, which might be related to their high nutrient content, large specific surface area, adsorption capacity, and functional groups. Biogas slurry generally exhibited stronger effects than biochar probably because of its richness in nutrients and bioactive substances.

Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories.