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[Objective]To addresses the problem of traversing multiple fields for agricultural robots in hilly terrain, a traversal path planning method is proposed by combining the Floyd algorithm with an improved genetic algorithm. The method provides a solution that can reduce the cost of agricultural robot operation and optimize the order of field traversal in order to improve the efficiency of farmland operation in hilly areas and realizes to predict how an agricultural robot can transition to the next field after completing its coverage path in the current field.[Methods]In the context of hilly terrain characterized by small and densely distributed field blocks, often separated by field ridges, where there was no clear connectivity between the blocks, a method to establish connectivity between the fields was proposed in the research. This method involved projecting from the corner node of the headland path in the current field to each segment of the headland path in adjacent fields vertically. The shortest projected segment was selected as the candidate connectivity path between the two fields, thus establishing potential connectivity between them. Subsequently, the connectivity was verified, and redundant segments or nodes were removed to further simplify the road network. This method allowed for a more accurate assessment of the actual distances between field blocks, thereby providing a more precise and feasible distance cost between field blocks for multi-block traversal sequence planning. Next, the classical graph algorithm, Floyd algorithm, was employed to address the shortest path problem for all pairs of nodes among the fields. The resulting shortest path matrix among headland path nodes within fields, obtained through the Floyd algorithm, allowed to determine the shortest paths and distances between any two endpoint nodes in different fields. This information was used to ascertain the actual distance cost required for agricultural machinery to transfer between fields. Furthermore, for the genetic algorithm in path planning, there were problems such as difficult parameter setting, slow convergence speed and easy to fall into the local optimal solution. This study improved the traditional genetic algorithm by implementing an adaptive strategy. The improved genetic algorithm in this study dynamically adjusted the crossover and mutation probabilities in each generation based on the fitness of the previous generation, adapting to the problem's characteristics. Simultaneously, it dynamically modified the ratio of parent preservation to offspring generation in the current generation, enhancing population diversity and improving global solution search capabilities. Finally, this study employed genetic algorithms and optimization techniques to address the field traversal order problem, akin to the Traveling Salesman Problem (TSP), with the aim of optimizing the traversal path for agricultural robots. The shortest transfer distances between field blocks obtained through the Floyd algorithm were incorporated as variables into the genetic algorithm for optimization. This process leads to the determination of an optimized sequence for traversing the field blocks and the distribution of entry and exit points for each field block.[Results and Discussions]A traversal path planning simulation experiment was conducted to compare the improved genetic algorithm with the traditional genetic algorithm. After 20 simulation experiments, the average traversal path length and the average convergence iteration count of the two algorithms were compared. The simulation results showed that, compared to the traditional genetic algorithm, the proposed improved genetic algorithm in this study shortened the average shortest path by 13.8%, with fewer iterations for convergence, and demonstrated better capability to escape local optimal solutions. To validate the effectiveness of the multi-field path planning method proposed in this study for agricultural machinery coverage, simulations were conducted using real agricultural field data and field operation parameters. The actual operating area located at coordinates (103.61°E, 30.47°N) was selected as the simulation subject. The operating area consisted of 10 sets of field blocks, with agricultural machinery operating parameters set at a minimum turning radius of 1.5 and a working width of 2. The experimental results showed that in terms of path length and path repetition rate, the present method showed more superior performance, and the field traversal order and the arrangement of imports and exports could effectively reduce the path length and path repetition rate.[Conclusions]The experimental results proved the superiority and feasibility of this study on the traversing path planning of agricultural machines in multiple fields, and the output trajectory coordinates of the algorithm can serve as a reference for both human operators and unmanned agricultural machinery during large-scale operations. In future research, particular attention will be given to addressing practical implementation challenges of intelligent algorithms, especially those related to the real-time aspects of navigation systems and challenges such as Kalman linear filtering. These efforts aim to enhance the applicability of the research findings in real-world scenarios.

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields.

Spectral technology is a scientific method used to study and analyze substances. In recent years, the role of spectral technology in the non-destructive testing (NDT) of fruits has become increasingly important, and it is expected that its application in the NDT of fruits will be promoted in the coming years. However, there are still challenges in terms of dataset collection methods. This article aims to enhance the effectiveness of spectral technology in NDT of citrus and other fruits and to apply this technology in orchard environments. Firstly, the principles of spectral imaging systems and chemometric methods in spectral analysis are summarized. In addition, while collecting fruit samples, selecting an experimental environment is crucial for the study of maturity classification and pest detection. Subsequently, this article elaborates on the methods for selecting regions of interest (ROIs) for fruits in this field, considering both quantitative and qualitative perspectives. Finally, the impact of sample size and feature size selection on the experimental process is discussed, and the advantages and limitations of the current research are analyzed. Therefore, future research should focus on addressing the challenges of spectroscopy techniques in the non-destructive inspection of citrus and other fruits to improve the accuracy and stability of the inspection process. At the same time, achieving the collection of spectral data of citrus samples in orchard environments, efficiently selecting regions of interest, scientifically selecting sample and feature quantities, and optimizing the entire dataset collection process are critical future research directions. Such efforts will help to improve the application efficiency of spectral technology in the fruit industry and provide broad opportunities for further research. [ABSTRACT FROM AUTHOR]

The segmentation and positioning of tea buds are the basis for intelligent picking robots to pick tea buds accurately. Tea images were collected in a complex environment, and median filtering was carried out to obtain tea bud images with smooth edges. Four semantic segmentation algorithms, U-Net, high-resolution network (HRNet_W18), fast semantic segmentation network (Fast-SCNN), and Deeplabv3+, were selected for processing images. The centroid of the tea buds and the image center of the minimum external rectangle were calculated. The farthest point from the centroid was extracted from the tea stalk orientation, which was the final picking point for tea buds. The experimental results showed that the mean intersection over union (mIoU) of HRNet_W18 was 0.81, and for a kernel with a median filter size of 3 × 3, the proportion of abnormal tea buds was only 11.6%. The average prediction accuracy of picking points with different tea stalk orientations was 57%. This study proposed a fresh tea bud segmentation and picking point location method based on a high-resolution network model. In addition, the cloud platform can be used for data sharing and real-time calculation of tea bud coordinates, reducing the computational burden of picking robots. [ABSTRACT FROM AUTHOR]

Laser speckle is a new technology for non-destructive detection in agriculture. When laser light with good coherence irradiates on the optically rough surface or the surface with some kind of activity, the scattering light will interfere with each other and form a mottled pattern called laser speckle. The laser speckle contains various information of the reflector, such as the roughness, the particle's motion and the temperature information. Huangguan pears were used as the object in this research to investigate the possibility of identifying the defects from the stem/calyx and the sound area of pears based on laser speckle technology. A laser speckle imaging system was established which contained a semiconductor laser (635 nm, 50 mw) applied as the light source and a digital signal generator applied as the trigger source of the CCD (charge coupled device) camera. Two hundred pears including one hundred sound pears and one hundred pears with the defect (rot) were tested. The speckle images of defect (rot) parts and good parts (calyx/stem, sound area) of the pears were collected under the same condition. First of all, the speckle images were converted into the grayscale images using Matlab 2011b. The method of Fujii and the weighted generalized differences method (WGD) were used to analyze the grayscale speckle images to get the images of Fujii and WGD. Then gray level co-occurrence matrix (GLCM) was used to extract the mean and the standard deviation values of the angular second moment (ASM), entropy (ENT), moment of inertia (INE) and correlation (COR) from the images of Fujii and WGD, respectively. Therefore, in total, 16 features were extracted. The performance of each feature was evaluated by the receiver operator characteristic (ROC) curve. According to the ROC curves, the features whose values of the area under the curves (AUC) were higher than 0.5 were chosen for further analysis. The best threshold value of each selected feature was calculated by Youden's index. The classification analysis based on single feature was tested using the best threshold value. Besides, the classification analysis based on multiple features was carried out by the binary logistic regression. The combination of every two features was tested to get better classification accuracy. The results showed that there were seven features whose AUC values were bigger than 0.5. In classification of single feature, the ASM extracted from WGD image had the best performance whose overall accuracies of calibration and validation sets were 96.4% and 96.7% respectively. In classification of multiple features, the combination of the ASM and standard deviation of correlation extracted from WGD image had the best performance whose overall accuracies of calibration and validation sets were both 97.5%. In order to find out the causes of the error, the original RGB (red, green and blue) images of the misjudged samples were studied. It turned out that the defect area which was misjudged to be normal was not obvious. Besides, the defect area was larger than the light spot whose diameter was 20 mm which could not cover the defect area completely. Therefore, the texture features of laser speckle images of misjudged samples were closer to the sound area than the defect area, which led to the miscalculation. This research shows that the method of laser speckle imaging is feasible in the detection of pear defect (rot) and stem/calyx. [ABSTRACT FROM AUTHOR]