Development of a Method for Soil Tilth Quality Evaluation from Crumbling Roller Baskets Using Deep Machine Learning Models.

A combination tillage with disks, rippers, and roller baskets allows the loosening of compacted soils and the crumbling of soil clods. Statistical methods for evaluating the soil tilth quality of combination tillage are limited. Light Detection and Ranging (LiDAR) data and machine learning models (Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)) are proposed to investigate roller basket pressure settings on soil tilth quality. Soil profiles were measured using LiDAR (stop and go and on-the-go) and RGB visual images from a Completely Randomized Design (CRD) tillage experiment on clay loam soil with treatments of roller basket down, roller basket up, and no-till in three replicates. Utilizing RF, SVM, and NN methods on the LiDAR data set identified median, mean, maximum, and standard deviation as the top features of importance variables that were statistically affected by the roller settings. Applying multivariate discriminatory analysis on the four statistical measures, three soil tilth classes were predicted with mean prediction rates of 77% (Roller-basket down), 64% (Roller-basket up), and 90% (No till). The LiDAR data analytics-inspired soil tilth classes correlated well with the RGB image discriminatory analysis. Soil tilth machine learning models were shown to be successful in classifying soil tilth with regard to onboard operator pressure control settings on the roller basket of the combination tillage implement.

Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content.

This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSEc was 0.3429, and Rc2 was 0.9905. In the prediction set, RMSEp was 1.5670, and Rp2 was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSEc was 0.8721, and Rc2 was 0.9429. In the prediction set, RMSEp was 1.1810, and Rp2 was 0.8683.

Perceptual Soft End-Effectors for Future Unmanned Agriculture.

As consumers demand ever-higher quality standards for agricultural products, the inspection of such goods has become an integral component of the agricultural production process. Unfortunately, traditional testing methods necessitate the deployment of numerous bulky machines and cannot accurately determine the quality of produce prior to harvest. In recent years, with the advancement of soft robot technology, stretchable electronic technology, and material science, integrating flexible plant wearable sensors on soft end-effectors has been considered an attractive solution to these problems. This paper critically reviews soft end-effectors, selecting the appropriate drive mode according to the challenges and application scenarios in agriculture: electrically driven, fluid power, and smart material actuators. In addition, a presentation of various sensors installed on soft end-effectors specifically designed for agricultural applications is provided. These sensors include strain, temperature, humidity, and chemical sensors. Lastly, an in-depth analysis is conducted on the significance of implementing soft end-effectors in agriculture as well as the potential opportunities and challenges that will arise in the future.

A proteomic study of spike development inhibition in bread wheat.

Spike development in wheat is a complicated development process and determines the wheat propagation and survival. We report herein a proteomic study on the bread wheat mutant strain 5660M underlying spike development inhibition. A total of 121 differentially expressed proteins, which were involved in cold stress response, protein folding and assembly, cell-cycle regulation, scavenging of ROS, and the autonomous pathway were identified using MS/MS and database searching. We found that cold responsive proteins were highly expressed in the mutant in contrast to those expressed in the wild-type line. Particularly, the autonomous pathway protein FVE, which modulates flowering, was dramatically downregulated and closely related to the spike development inhibition phenotype of 5660M. A quantitative RT-PCR study demonstrated that the transcription of the FVE and other six genes in the autonomous pathway and downstream flowering regulators were all markedly downregulated. The results indicate that spike development of 5660M cannot complete the floral transition. FVE might play an important role in the spikes development of the wheat. Our results provide the theory basis for studying floral development and transition in the reproductive growth period, and further analysis of wheat yield formation.

[Research on the trash content measurement and classification of ginned cotton by using NIR spectroscopy technique].

Near infrared (NIR) spectroscopy was investigated to predict trash content and classify types of ginned cotton by using a fiberoptic in diffuse reflectance mode. Different spectra preprocessing methods were compared, and partial least-squares (PLS) regression was established to predict the trash content of ginned cotton. Discriminant analysis (DA) was used to classify various types of lint and content level of trash. The correlation coefficient r was 0.906 for optimal PLS model using three factors based on first-order derivative spectra, and RMSEC and RMSEP was 0.440 and 0.823 respectively. To classify ginned cotton with and without plant trash, the accuracy rate reached 95.4% using 15 principal components (PCs) via DA, whereas the prediction accuracy rate was only 80.9% for the classification of sample types due to containing foreign fiber, and the classification result for the content level of trash in lint was not good for the samples without any preprocessing. The result indicated that the NIR spectra of sample can be used to predict trash content in ginned cotton, which is often disturbed by type, content and distribution of foreign matters, and the accuracy of some prediction model is unsatisfactory. In order to improve the prediction accuracy, some methods would be applied in future research, such as pretreatment according to acquisition request of solid sample, or using transmission mode.