Information Processing in Agriculture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.

Non-invasive potato defects detection has been demanded for sorting and grading purpose. Researches on the classification of the defects has been available, however, investigation on the severity level calculation is limited. For the detection of the common scab, it has been found that imaging in the infrared region provide an interesting characteristic that could distinguish defected area to normal area. Thus, investigations on this wavelength range is interesting to add more knowledge and for applications. In this research, the multispectral image has been obtained and investigated especially at three wavelengths (950, 1 150, 1 600 nm). Image pre-processing and pseudo-color conversion techniques were explored to enhance the contrast between defects, normal background skin area and soil deposits. Results show that external defects, such as common scab and some mechanical damage types, appear brighter in the near infrared region, especially at 1 600 nm against the normal skin background. It has been found that pseudo-color images conversion provides more information regarding type if surface characteristics compared to grayscale single imaging. Image segmentation using pseudo-color images after multiplication operation pre-processing could be used for common scab and mechanical damage detection excluding soil deposits with a Dice Sorensen coefficient of 0.64. In addition, image segmentation using single image at 1 600 nm shown relatively better results with Dice Sorensen coefficient of 0.72 with note that thick soil deposits will also be segmented. Defect severity level evaluation had an R² correlation of 0.84 against standard measurements of severity.

Non-isothermal convective drying schemes were examined for Lavandula × allardii leaves and inflorescences. Drying process parameters were optimized using response surface methodology (RSM) to ensure the peak operational performance. The effects of temperature increase rate (2–4 °C/h) and the airflow velocity (1–3 m/s) on the essential oil yield, drying duration and consumption, were investigated. A face-centered central composite design was deployed and the experimental data was adapted to the most suitable polynomial models, as determined by the regression analysis. Analysis of variance was applied to assess the effects of the process variables, their interactions and the statistical significance of the examined models. Both factors of temperature increase rate and airflow velocity had a significant impact on the drying duration. Airflow velocity had a greater effect on leaves’ essential oil yield and inflorescences’ process energy consumption, whereas the rates of temperature increase had a greater influence on the inflorescences’ essential oil yield and leaves’ energy consumption. The minimum drying duration and energy consumption were obtained for the maximum temperature increasing rate at 3 and 1 m/s airflow velocities respectively; and the highest essential oil yield was obtained for the least rate of temperature increase and airflow velocity for both leaves and inflorescences. Numerical optimization was performed for minimizing drying duration and energy consumption by maximizing the essential oil yield. The rate of temperature increases of 4 °C/h and the airflow velocity of 1 m/s, were proposed as the optimum non-isothermal drying conditions for both leaves and inflorescences of Lavandula × allardii. Predicted values of essential oil content have been 1.387/3.05 mL/g, 4.21/4.18 h drying time and 0.809/0.732 kWh energy consumption at the optimum operation point for leaves and inflorescences, respectively. The resulted optimized non-stationary temperature scheme considerably improved the drying kinetics and the process consumption by achieving a similar essential oil recovery with the standard low-temperature convective drying. The present study aimed to eliminate the preexisting gap of the optimum selection of the process parameters for the particular type of the examined non-isothermal drying schemes. Previous findings could be utilized for designing dryers and drying schedules aiming to retain the qualitative attributes, by reducing the cost and duration of the drying operations.

This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology, inspired by the needs of problem solving for biological, agricultural, aquacultural and environmental systems. The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based, unified models that can also be applied for the effective coupling amongst the various (sophisticated field-specific, sensor data processing-based, upper level optimization-driven, etc.) models. The scientific problem addressed in this innovation is to develop Programmable Process Structures (PPS) by combining functional basis of systems theory, structural approach of net theory and computational principles of agent based modeling. PPS offers a novel framework for the automatic generation of easily extensible and connectible, unified models for the underlying complex systems. PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure. The models consist of unified state and transition elements. The local program containing prototype elements, derived also from the meta-prototypes, are responsible for the case-specific calculations. The integrity and consistency of PPS architecture are based on the meta-prototypes, prepared to distinguish between the conservation-laws-based measures and the signals. The simulation is based on data flows amongst the state and transition elements, as well as on the unification based data transfer between these elements and their calculating prototypes. This architecture and its AI language-based (Prolog) implementation support the integration of various field- and task-specific models, conveniently. The better understanding is helped by a simple example. The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications, focusing on the recently studied agricultural and aquacultural cases.

The precision livestock farming (PLF) has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production. Among the PLF techniques, the personalised management of each individual animal based on sensors systems, represents a viable option. It is worth noting that the implementation of an effective PLF approach can be still expensive, especially for small and medium-sized farms; for this reason, to guarantee the sustainability of a customized livestock management system and encourage its use, plug and play and cost-effective systems are needed. Within this context, we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera. By leveraging the current state-of-the-art methods for real-time object detection, (i.e., YOLOv3) cattle's face areas, we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker. The cow IDs are read by an Optical Character Recognition (OCR) algorithm for which, an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs. Thanks to the detection of the tag position, the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed. Activity times for the areas are outputted as cattle activity recognition results. Evaluation results demonstrate the effectiveness of our proposed method, showing a [email protected] of 89%.

Some of the problems attributed to traditional laboratory analyses that limit the correct assessment of the nutrient contents in the soil are time requirements and high cost of the soil nutrient determinations. To solve these problems, a study was carried out to evaluate the use of visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR) spectroscopy in the prediction of soil available ions submitted to the application of rock powders. The study was carried out on an Arenosol in Paranavaí City/Brazil. Treatments (rock powders) were arranged within a split-plot system designed in randomized blocks with four repetitions. Sugarcane was cultivated for 14 months after the application of rock powders. Later, 96 soil samples were collected for measuring the pH and available ions P, K⁺, Ca²⁺, Mg²⁺, S-SO₄^2-, Si, Cu²⁺, Fe²⁺, Mn²⁺, and Zn²⁺ as well as spectral reading through a Vis-NIR-SWIR spectroradiometer to predict the soil chemical attributes through the partial least square regression (PLS) technique. The results showed that the elements K⁺, Ca²⁺, Mg²⁺, Cu²⁺, and Fe²⁺ could be predicted with a reasonable rightness degree (R²_p > 0.50, RPD_p > 1.40) from spectral models. However, for the attributes pH, P, S-SO₄^2-, Si, Mn²⁺, and Zn²⁺, there were no satisfactory models (R²_p < 0.50, RPD_p < 1.40). Thus, the application of rock powder changed the spectral curves and, because of that, allows the building of PLS models to predict the elements K⁺, Ca²⁺, Mg²⁺, Cu²⁺, and Fe²⁺. Therefore, Vis-NIR-SWIR spectroscopy is a promising alternative to the routine analyses of soil fertility since it has advantages such as fast analytical speed, low cost, easy to operate, non-destructive, and environmentally friendly, because it does not use harmful chemicals.