[Spatial Distribution Characteristics of Soil Carbon and Nitrogen in Citrus Orchards on the Slope of Purple Soil Hilly Area]

Since the 1990s, a large area of sloping farmland in a purple soil hilly region of southwest China was converted into an orchard to prevent soil erosion, increase soil fertility, and elevate economic benefits for farmers. In order to explore the spatial distribution of soil carbon (C) and nitrogen (N) fractions on the slope of returning arable lands to citrus orchards in purple soil hilly areas, a soil sampling event was carried out in a citrus orchard at the Yanting Agro-ecological Experimental Station of Purple Soil, Chinese Academy of Sciences, to examine the differences in soil C and N fractions and their influencing factors. The results showed that the slope position had significant effects on the contents of soil total nitrogen (TN), nitrate nitrogen (NO3--N), and dissolved organic carbon (DOC) (P < 0.05), but the effects were not obvious regarding the total organic carbon (SOC) and ammonia nitrogen (NH4+-N) of the soil (P > 0.05). For topsoil (0-30 cm), the variation trend of soil NO3--N content along the slope was upper slope < middle slope < lower slope, whereas the TN and DOC contents along the slope exhibited the trend of upper slope > middle slope > lower slope. The contents of soil C and N in each slope position generally showed a downward trend with increasing soil depth (0-30 cm). The contents of soil TN, SOC, NO3--N, and DOC were significantly affected by soil depth (P < 0.05). The TN storage (0-30 cm) significantly decreased from the top to the bottom within the soil slope, with a value of 2.37, 1.89, and 1.62 t·hm-2 (reported as N) for the upper slope, middle slope, and lower slope, respectively. There was no significant difference in SOC reserves along the slope, with a range from 56.12 to 58.48 t·hm-2 (reported as C). Our results provide scientific basis for understanding the spatial distribution of soil nutrients of the restored farmland in purple soil hilly areas. Our research suggests that the spatial distribution of soil carbon and nitrogen storage should not be ignored when predicting the response of soil nutrients to land use change.

Adult Diaphorina citri Biocontrol Using Hirsutella citriformis Strains and Gum Formulations

Hirsutella citriformis Speare is the only entomopathogenic fungus that has been applied to control the hemipteran Diaphorina citri Kuwayama. However, the use of available commercial products under field conditions is limited due to conidia's shelf life and short environmental persistence. We have previously reported the citrus psyllid D. citri adults' biocontrol potential using H. citriformis strains. The aim of the present study was to evaluate different formulations based on H. citriformis (OP-Hir-3, OP-Hir-10, and OP-Hir-12 strains) conidia and gums as additives to improve D. citri adults' biocontrol, under laboratory, greenhouse, and field conditions, using Hirsutella gums as conidia stabilizers to improve their viability under environmental drought conditions and as insecticide. Laboratory bioassay results showed that the highest (p < 0.05) D. citri mortality was achieved using FOP-Hir-10GH (63.5%), followed by the Hirsutella gum control (42.2%). Under greenhouse conditions, adults' mortality reached up to 84.6% with FOP-Hir-12 and 49.0% with Hirsutella gum. In addition, we applied H. citriformis formulations under field conditions in a commercial citrus grove located in Tecomán, Colima, México, at 21.5 °C and 73.3% relative humidity (RH) in March and 25.7 °C and 72.5% RH in October 2022 and observed 67.3% and 94.0% mortality of D. citri adults, respectively. Hirsutella gum alone showed significant insecticidal activity against D. citri adults. In conclusion, this study demonstrated that Hirsutella gum functioned as additive to H. citriformis conidia formulations, improving D. citri adults' mortality and showing potential for this pest biocontrol in citrus orchards.

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Deep Learning models have presented promising results when applied to Agriculture 4.0. Among other applications, these models can be used in disease detection and fruit counting. Deep Learning models usually have many layers in the architecture and millions of parameters. This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of processing power for inference. In addition, the lack of high-quality Internet connectivity in the field impedes the usage of cloud computing, pushing the processing towards edge devices. This work describes the proposal of an edge AI application to detect and map diseases in citrus orchards. The proposed system has low computational demand, enabling the use of low-footprint models for both detection and classification tasks. We initially compared AI algorithms to detect fruits on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied lean AI models to perform the classification task. In this context, we tested and compared the performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of 100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2 and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse. Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we provide a method to evaluate the results from these algorithms towards describing the disease spread using statistical parametric models and a genetic algorithm to perform the parameters' regression. With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to develop a mobile edge AI solution.

Molecular characterization of divergent isolates of Citrus bent leaf viroid (CBLVd) from citrus cultivars of Punjab, Pakistan

Citrus viroid infection is emerging as a serious threat because of its efficient systemic movement within the host plant and its quick spread due to contaminated pruning tools. A survey was conducted to investigate the primary distribution and molecular characterization of Citrus bent leaf viroid (CBLVd) and its variants in different citrus cultivars. A total of 154 symptomatic citrus samples were collected and detected by RT‒PCR with newly designed specific primers with the incidence of 36.33%. During biological indexing study on Etrog citron, expressions of reduced leaf size, yellowing with a light green pattern, and bending were observed. Amplified products were sequenced and analyzed using a nucleotide BLAST search, which showed 98% homology with other CBLVd isolates. The results of the phylogenetic tree analysis showed the presence of two main groups (A and B), with the predominant variants of CBLVd, i.e., CVd-I-LSS (Citrus viroid Low Sequence Similarity) sequences, clustering in subgroup A1 along with newly detected CVd-I-LSS from Palestinian sweet lime (Citrus limettioides), which has been identified as a new host of CVd-I-LSS in Pakistan. Further analysis of the sequences in subgroup A1 showed that the variant of CVd-I-LSS infecting citrus cultivars had a close relationship with isolates reported from China, Japan, and Iran, which may have resulted from the exchange of planting material. This study also unveiled the variability in nucleotide sequences of CBLVd, which made it unable to be detected by old primers. The results of this study indicate that the widespread presence of divergent variants of CBLVd is a major concern for the citrus industry in Pakistan and other countries where virulent isolates of CBLVd are prevalent. These findings suggest the need for future research on effective management and quarantine measures to stop the spread of CBLVd.