农学最新文献

UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both the scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices. They enable us do a better crop planning, input estimates, early identification and correction of sowing failures, more efficient irrigation systems, among other tasks. Since all these activities deal with low or medium altitude images, automated identification of crop lines plays a crucial role improving these tasks. We address the problem of detecting and segmenting crop lines. We use a Convolutional Neural Network to segment the images, labeling their regions in crop lines or unplanted soil. We also evaluated three traditional semantic networks: U-Net, LinkNet, and PSPNet. We compared each network in four segmentation datasets provided by an expert. We also assessed whether the network’s output requires a post-processing step to improve the segmentation. Results demonstrate the efficiency and feasibility of these networks in the proposed task.

Plant growth monitoring techniques are of great interest to agricultural engineering. The interaction between root and soil water is one important plant response to environmental variations. This paper aims to develop a new method to estimate plant biological response using root-soil water interaction. It provides a case study on moisture transfer at the boundary area of a soil water retention zone (SWRZ). We produced a SWRZ around growing roots of a cultivated tomato plant in homogenous dried soil using water-saving drip irrigation. The irrigation was designed to supply moisture only in the root zone to meet the minimum need of plant growth. High-resolution soil moisture sensors were used to detect moisture transfer at the boundary area of the SWRZ. We applied frequency analysis to the acquired vibration spectrum using filtering and Fast Fourier Transform (FFT) in order to investigate the frequency content at each sensor location. Distinct frequencies of moisture vibration were identified at the boundary area of the SWRZ which indicated water transfer to the roots caused by plant water absorption. A mechanical vibration model was proposed to describe this phenomenon. The pinpoint irrigation to the root zone in the water-saving cultivation method enabled a well-structured spherical root system to form via hydrotropism. This enabled a simple method to analyze moisture transfer based on a mechanical vibration model. The results suggest a new method to estimate plant biological response by studying root-soil water interaction.

The importance of Model Predictive Control (MPC) has significant applications in the agricultural industry, more specifically for greenhouse’s control tasks. However, the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system. Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data. In this paper, we introduce an application of Constrained Model Predictive Control (CMPC) for a greenhouse temperature and relative humidity. For this purpose, two Multi Input Single Output (MISO) systems, using Numerical Subspace State Space System Identification (N4SID) algorithm, are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions. In this sense, linear state space models were adopted in order to evaluate the robustness of the control strategy. Once the system is identified, the MPC technique is applied for the temperature and the humidity regulation. Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed, both parameters respect the ranges 15 °C ≤ T_int ≤ 30 °C and 50 % ≤ H_int ≤ 70 % respectively. On the other hand, the control signals u_f and u_h applied to the fan and the heater, respect the hard constraints notion, the control signals for the fan and the heater did not exceed 0 ≤ u_f ≤ 4.3 Volts and 0 ≤ u_h ≤ 5 Volts, respectively, which proves the effectiveness of the MPC and the tracking tasks. Moreover, we show that with the proposed technique, using a new optimization toolbox, the computational complexity has been significantly reduced. The greenhouse in question is devoted to Schefflera Arboricola cultivation.

Tiger puffer is a commercially important fish cultured in high-density environments, and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding. However, the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments. The farmed tiger puffer detection model, called knowledge aggregation YOLO (KAYOLO), fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem. To alleviate feature loss caused by target blurring, we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer's features and improve detection precision. To address missed detection caused by mutual occlusion in high-density farming environments, a prediction box aggregation method, aggregating prediction boxes of the same object, was proposed to reduce the influence among different objects to improve detection recall. To validate the effectiveness of the proposed methods, ablation experiments, model performance experiments, and model robustness experiments were designed. The experimental results showed that KAYOLO's detection precision and recall results reached 94.92% and 92.21%, respectively. The two indices were improved by 1.29% and 1.35%, respectively, compared to those of YOLOv5. Compared with the recent state-of-the-art underwater object detection models, such as SWIPENet, RoIMix, FERNet, and SK-YOLOv5, KAYOLO achieved 2.09%, 1.63%, 1.13% and 0.85% higher precision and 1.2%, 0.18%, 1.74% and 0.39% higher recall, respectively. Experiments were conducted on different datasets to verify the model's robustness, and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5. The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects. Additionally, the model had a strong generalization ability on different datasets, indicating that the model can be adapted to different environments, and it has strong robustness.