Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics.

The aim of this study was to develop three supervised self-organizing map (SOM) models for the automatic recognition of a systemic resistance state in plants after application of a resistance inducer. The pathosystem Fusarium oxysporum f. sp. radicis-lycopersici (FORL) + tomato was used. The inorganic, defense inducer, Acibenzolar-S-methyl (benzo-[1,2,3]-thiadiazole-7-carbothioic acid-S-methyl ester, ASM), reported to induce expression of defense genes in tomato, was applied to activate the defense mechanisms in the plant. A handheld fluorometer, FluorPen FP 100-MAX-LM by SCI, was used to assess the fluorescence kinetics response of the induced resistance in tomato plants. To achieve recognition of resistance induction, three models of supervised SOMs, namely SKN, XY-F, and CPANN, were used to classify fluorescence kinetics data, in order to determine the induced resistance condition in tomato plants. To achieve this, a parameterization of fluorescence kinetics curves was developed corresponding to fluorometer variables of the Kautsky Curves. SKN was the best supervised SOM, achieving 97.22% to 100% accuracy. Gene expression data were used to confirm the accuracy of the supervised SOMs.

Nutritional composition changes in bell pepper as affected by the ripening stage of fruits at harvest or postharvest storage and assessed non-destructively.

BACKGROUND: Nutritional quality in bell pepper is related to the ripening stage of the fruit at harvest and postharvest storage. Its determination requires time-consuming, tissue-destructive, analytical laboratory techniques. The objective of this study was to investigate the effect of ripening stage and of postharvest storage period on fruit nutritional quality, and whether it is feasible to develop reliable models for assessing the nutritional components in peppers using non-destructive methods. The dry matter, soluble solids, ascorbic acid, phenolics, chlorophylls, carotenoids and the total antioxidant capacity were determined in bell pepper fruits at six ripening stages, from green to full red, during storage at 10 °C for 8 days. Color, chlorophyll fluorescence, visible/near infrared (Vis/NIR) spectroscopy, red-green-blue (R-G-B) and red-green-near infrared (R-G-NIR) digital imaging were tested for assessing the nutritional quality of peppers. RESULTS: The nutritional composition was mainly affected by the ripening stage of bell pepper fruits at harvest and only to a small degree by the storage period. Indeed, the more advanced ripening stage of fruit at harvest resulted in superior nutritional quality. Most of the non-destructive techniques reliably predicted the internal quality of the fruit. The genetic algorithm (GA), the variable importance in projection (VIP) scores, and the variable inflation factor (VIF) tests identified nine distinct regions and four specific wavelengths on the whole visible/NIR electromagnetic spectrum that exhibited the most significant effect in the assessment of the nutritional components. CONCLUSION: It is possible to predict individual nutritional components in bell pepper fruit reliably and non-destructively, and irrespective of the ripening stage of fruits at harvest. © 2021 Society of Chemical Industry.

Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm.

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostenice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

Machine Learning in Agriculture: A Review.

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination.

Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310⁻1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage.

Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery.

Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

The potential of optical canopy measurement for targeted control of field crop diseases.

There is increasing pressure to reduce the use of pesticides in modern crop production to decrease the environmental impact of current practice and to lower production costs. It is therefore imperative that sprays are only applied when and where needed. Since diseases in fields are frequently patchy, sprays may be applied unnecessarily to disease-free areas. Disease control could be more efficient if disease patches within fields could be identified and spray applied only to the infected areas. Recent developments in optical sensor technology have the potential to enable direct detection of foliar disease under field conditions. This review assesses recent developments in the use of optical methods for detecting foliar disease, evaluates the likely benefits of spatially selective disease control in field crops, and discusses practicalities and limitations of using optical disease detection systems for crop protection in precision pest management.