Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Aiming at the real-time detection and classification of the growth period of crops in the current digital cultivation and regulation technology of facility agriculture, an improved YOLOv4 method for identifying the growth period of strawberries in a greenhouse environment was proposed. The attention mechanism into the Cross Stage Partial Residual (CSPRes) module of the YOLOv4 backbone network was introduced, and the target feature information of different growth periods of strawberries while reducing the interference of complex backgrounds was integrated, the detection accuracy while ensured real-time detection efficiency was improved. Took the smart facility strawberry in Yunnan province as the test object, the results showed that the detection accuracy (AP) of the YOLOv4-CBAM model during flowering, fruit expansion, green and mature period were 92.38%, 82.45%, 68.01% and 92.31%, respectively, the mean average precision (mAP) was 83.78%, the mean inetersection over union (mIoU) was 77.88%, and the detection time for a single image was 26.13 ms. Compared with the YOLOv4-SC model, mAP and mIoU were increased by 1.62% and 2.73%, respectively. Compared with the YOLOv4-SE model, mAP and mIOU increased by 4.81% and 3.46%, respectively. Compared with the YOLOv4 model, mAP and mIOU increased by 8.69% and 5.53%, respectively. As the attention mechanism was added to the improved YOLOv4 model, the amount of parameters increased, but the detection time of improved YOLOv4 models only slightly increased. At the same time, the number of fruit expansion period recognized by YOLOv4 was less than that of YOLOv4-CBAM, YOLOv4-SC and YOLOv4-SE, because the color characteristics of fruit expansion period were similar to those of leaf background, which made YOLOv4 recognition susceptible to leaf background interference, and added attention mechanism could reduce background information interference. YOLOv4- CBAM had higher confidence and number of identifications in identifying strawberry growth stages than YOLOv4-SC, YOLOv4-SE and YOLOv4 models, indicated that YOLOv4-CBAM model can extract more comprehensive and rich features and focus more on identifying targets, thereby improved detection accuracy. YOLOv4-CBAM model can meet the demand for real-time detection of strawberry growth period status.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Irrigation is an important factor that affects crop yield. In order to control irrigation of facility crops more effectively and accurately, this study took "Zhangji" strawberry as an example, introduced crop real-time growth characteristics into irrigation decision-making, and combined Penman-Monteith (P-M) model and knowledge reasoning to study the irrigation of strawberry. In the first step, the influencing factors and expert experience in identifying strawberry growth period of "Zhangji" strawberry irrigation were standardized, and the strawberry irrigation data structure based on Resource Description Framework (RDF) was established. The second step was to collect expert experience of strawberry irrigation according to the standardized knowledge structure model. Firstly, all data were unified into structured data, and then were stored in *.csv format together with expert experience, and strawberry irrigation knowledge map based on Neo4j was constructed. The third step was to collect the environmental data and plant data of strawberry in each growth period. The fourth step was using P-M model to calculate the initial irrigation value of strawberry, and then adjusted the initial irrigation value by knowledge reasoning.The fifth step was to conduct experimental planting and evaluate the sampled fruits. In knowledge reasoning, irrigation adjustment strategies of each expert was different. In strawberry irrigation experiment based on P-M model and path sorting algorithm, a group of irrigation reasoning values with the highest probability value were selected to adjust irrigation with the goal of maximizing strawberry yield. The experimental results showed that under the condition of harvesting at a specified time, The total fruit yield, average fruit yield per plant and average fruit weight percentage increased by 2478.5 g, 20.65 g and 12.15% (average fruit weight increased by 1.65 g per fruit) based on P-M model and path sorting algorithm compared with traditional P-M model, respectively. First, on the basis of P-M model, the yield-first irrigation adjustment strategy was adopted. Based on knowledge reasoning, the irrigation frequency and amount were adjusted timely according to the crop growth situation, which improved the yield. Second, under the condition of harvesting and recording yield at a specified time, the experiment accurately controlled the growth period to ensure early fruit ripening, while the other three groups of fruits were not fully mature and the yield of immature fruits were not calculated. Under the method of strawberry irrigation based on Penman-Monteith model and path sorting algorithm, the fruit was picked within a fixed time and reached 0.39 kg/cm2, which increased by 0.1 kg/cm2. Because the planting goal of this study was yield first, only the influence of irrigation on yield was considered. The experimental resulted show that the irrigation method based on model and knowledge reasoning could improve the yield of strawberry, and can provide a new idea for precise irrigation.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Basic Disciplines of Agriculture submitted time 2017-11-07 Cooperative journals: 《中国生态农业学报》
Abstract：为了进一步明确地膜覆盖的农业生产潜力, 本研究在北京市昌平区小汤山镇国家精准农业示范基地(40°10′33.26″N, 116°23′37.07″E)设计4 个试验处理[T1: 地膜覆盖(在传统地膜覆盖的基础上膜上覆盖1 cm 土 层)+不灌水; T2: 无地膜+冻水; T3: 无地膜+冻水+拔节水; T4: 无地膜+冻水+拔节水+开花水], 利用称重式蒸 渗仪研究该种地膜覆盖下的冬小麦耗水特征和产量形成机制。结果表明, 4 种处理的累计蒸散量随着播种天数而呈现三次多项式动态方程, 且4 种处理的绝对系数R2>0.99, 拟合性较高。T1、T4 的土壤-作物系数(Kc)最大理论值与实际最大值均出现在抽穗期, 而T2、T3 出现在拔节期, 且4 种处理的Kc 随播种天数呈二次方程,绝对系数R2>0.70(T2 为0.69)。从阶段耗水量看, 播种—拔节期, T1 显著低于T2(T3/T4); 拔节—成熟期, T1 与T2 差异不显著, 但均显著低于T3 和T4 处理(P0.05); 生长后期, 增加了对50~100 cm 土层的水分消耗。从蒸散速率及Kc 看, T1 的蒸散高峰值高于T2, 但低于T3 和T4; T1 的冬后蒸散高峰最大值出现时间(播后215 d)晚于T2、T3 和T4(播后194 d); T1 的Kc 最大值出现时间与T4 相同(播后214 d), 但晚于T2、T3(分别为播后200 d、199 d)。与T2、T3 相比, T1 增加了旗叶叶片水势, 延缓了叶片衰老, 而且土壤表层(0~5 cm)的温度增加了0.5 ℃, 但增加不显著, 这利于降低棵间的土壤蒸发。从产量与产量构成及水分利用效率看, T1 穗粒数和千粒重高于T2 和T3,低于T4, 但差异不显著; T1 产量与T2 和T3 差异不明显, 但显著低于T4, 水分利用效率显著提高了22.6%(P<0.05)。上述结果表明, 在底墒水充足的条件下, 地膜覆盖可代替冻水、拔节水的作用, 通过减少前期土壤蒸发, 为冬小麦生长后期节省大量水分, 在保证产量的前提下降低冬小麦全生育期耗水量, 提高作物水分利用效率。
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