Accurate identification of tomato ripeness and precise detection of picking points is the key to realizing automated picking. Aiming at the problems faced in practical applications, such as low accuracy of tomato ripeness and picking points detection in complex greenhouse environments, which leads to wrong picking, missed picking, and fruit damage by robots, this study proposes the YOLO-TMPPD (Tomato Maturity and Picking Point Detection) model. YOLO-TMPPD is structurally improved and algorithmically optimized based on the YOLOv8 baseline architecture. Firstly, the Depthwise Convolution (DWConv) module is utilized to substitute the C2f module within the backbone network. This substitution not only cuts down the model’s computational load but also simultaneously enhances the detection precision. Secondly, the Content-Aware ReAssembly of FEatures (CARAFE) operator is utilized to enhance the up-sampling operation, enabling precise content-aware processing of tomatoes and picking keypoints to improve accuracy and recall. Finally, the Convolutional Attention Mechanism (CBAM) module is incorporated to enhance the model’s ability to detect tomato-picking key regions in a large field of view in both channel and spatial dimensions. Ablation experiments were conducted to validate the effectiveness of each proposed module (DWConv, CARAFE, CBAM), and the architecture was compared with YOLOv3, v5, v6, v8, v9, and v10. The experimental results reveal that, when juxtaposed with the original network model, the YOLO-TMPPD model brings about remarkable improvements. Specifically, it improves the object detection F1 score by 4.48% and enhances the keypoint detection accuracy by 4.43%. Furthermore, the model’s size is reduced by 8.6%. This study holds substantial theoretical and practical value. In the complex environment of a greenhouse, it contributes significantly to computer-vision-enabled detection of tomato ripening. It can also help robots accurately locate picking points and estimate posture, which is crucial for efficient and precise tomato-picking operations without damage.

Partial substitution of mineral N fertilizer with manure (organic substitution) is considered as an effective way to reduce N input in intensive agroecosystems. Here, based on a 3-year field experiment, we assessed the influence of different organic substitution ratios (15%, 30%, 45%, and 60%, composted chicken manure applied) on vegetable productivity and soil physicochemical and biochemical properties as well as microbiome (metagenomic sequencing) in an intensive greenhouse production system (cucumber-tomato rotation). Organic substitution ratio in 30% got a balance between stable vegetable productivity and maximum N reduction. However, higher substitution ratios decreased annual vegetable yield by 23.29–32.81%. Organic substitution (15–45%) improved soil fertility (12.18–19.94% increase in soil total organic carbon content) and such improvement was not obtained by higher substitution ratio. Soil mean enzyme activity was stable to organic substitution despite the activities of some selected enzymes changed (catalase, urease, sucrase, and alkaline phosphatase). Organic substitution changed the species and functional structures rather than diversity of soil microbiome, and enriched the genes related to soil denitrification (including nirK, nirS, and nosZ). Besides, the 30% of organic substitution obviously enhanced soil microbial network complexity and this enhancement was mainly associated with altered soil pH. At the level tested herein, organic substitution ratio in 30% was suitable for greenhouse vegetable production locally. Long-term influence of different organic substitution ratios on vegetable productivity and soil properties in intensive greenhouse system needs to be monitored.

The application of nitrification inhibitors (NIs) is an effective way to reduce soil nitrogen (N) losses and increase crop N uptake. Yet, the efficacy of NIs commonly varies with dosages, crop systems and soil environmental conditions. Hence, clarifying the suitable type and dosage of NIs is extremely important for structuring the best N management regime at a regional scale. Here, based on microcosm experiments, we evaluated the influence of three widely used NIs [Dicyandiamide, DCD; 3,4-Dimethylpyrazole phosphate, DMPP; 2-chloro-6-(trichloromethyl) pyridine, Nitrapyrin] on the nitrification activity of an intensively cultivated greenhouse soil. The results showed that both DCD and DMPP imposed a transient inhibition on nitrification (less than five days) regardless of the dosages applied, and, on the contrary, Nitrapyrin presented a persistent suppression, with a longer duration of the inhibition action by a higher dosage. Accordingly, the incorporation of Nitrapyrin at 2% of the applied N rate (w/w) is a recommendable dosage for local intensive greenhouse production. Further, we assessed the influence of various dosages of Nitrapyrin incorporation (0%, 0.25%, 0.5%, 2% and 5%) on the abundance and community of three groups of soil ammonia oxidizers [i.e., ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB) and completely ammonia-oxidizing bacteria (Comammox Nitrospira)] by qPCR and high-throughput amplicon sequencing. Nitrapyrin incorporation strongly lowered both the AOB and Comammox Nitrospira abundances and their community richness even at the lowest dosage. Nitrapyrin incorporation also significantly altered the community structure of all of the tested ammonia oxidizers, and the average relative abundance of some major community members (i.e., the Nitrososphaerales Clade Nitrososphaera, Nitrososphaerales Clade A, Nitrosospira briensis Clade, Nitrosospira multiformis Clade, Comammox Nitrospira Clade A.2 and Comammox Nitrospira Clade A-associated) obviously responded to Nitrapyrin incorporation. Overall, our findings indicated that AOB and Comammox Nitrospira were more sensitive to Nitrapyrin incorporation as compared with AOA. The results obtained here highlight the importance of optimizing the type and dosage of NIs for N fertilization management in intensive greenhouse vegetable production. Nitrapyrin incorporation inhibits soil nitrification probably by suppressing the Nitrosospira multiformis Clade in the AOB community at the level tested herein.