Plant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.

Combining machine learning algorithms with Earth observations has great potential in the context of crop monitoring and management, which is essential in the face of global challenges related to food security and climate change [...]

Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.

Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.

Today’s agriculture faces numerous challenges due to climate change, a growing population and the need to increase food productivity [...]

Digital agriculture, defined as the analysis and collection of various farm data, is constantly evolving [...]

Waste management is one of most important challenges in environmental protection. Much effort is put into the development of waste treatment methods for further use. A serious problem is the treatment of municipal sewage sludge. One method that is useful for this substrate is composting. However, it is reasonable to compost a sewage sludge mixed with other substrates, such as maize straw. To carry out the composting process properly, it is necessary to control some parameters, including the total solids and volatile solids content in the composted mixture. In this paper, a method for the determination of the total solids and volatile solids content based on image analysis and neural networks was proposed. Image analysis was used for the determination of the colour and texture parameters. The three additional features describing the composted material were percentage of sewage sludge, type of maize straw, and stage of compost maturity. The neural models were developed based on various combinations of the input parameters. For both the total solids and volatile solids content, the most accurate models were obtained using all input parameters, including 30 parameters for image colour and texture and three features describing the composted material. The uncertainties of the developed models, expressed by the MAPE error, were 2.88% and 0.59%, respectively, for the prediction of the total solids and volatile solids content.

Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples.

Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.

Thermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of differential equations into algebraic equations, as well as knowledge about the initial and boundary conditions. Furthermore, a lack of information or incomplete information about the initial conditions makes it difficult or impossible to evaluate the volume of stored energy, or can cause significant errors during evaluation. Such situations occur in systems equipped with a rock battery, in which solar collectors act as source of energy. Considering the above, as well as the lack of a model for batteries in a vertical setting, we identified the need for research into the storage phase of rock bed thermal storage systems, working in a horizontal setting, and generating MLP-type neural models. Among these models, MLP 4-7-1 turned out to be the best both in terms of the values of regression statistics and possibilities of generalization. According to the authors, artificial neural models depicting temperature changeability in storage phase will be helpful in the development of a new methodology that can predict the heat volume in rock bed thermal storage systems.

The paper concentrates on researching the possibilities of using modern information technologies in animal production in order to monitor and identify behavior and well-being of cows. Having in mind the challenges related to managing dairy herds, and economic pressure put on breeders (as well as the broadly defined well-being of animals), an endeavor was made to create a new method, which would be competitive in comparison with the existing solutions. The proposed method of collecting data and data processing with beacon devices as well as data warehouse, allows-according to the authors-a more complete identification of behaviors and physiological condition of a dairy herd. It is also worth pointing out that this method is competitive in terms of price. By virtue of the multitude of data that were collected, a decision was made to resign from processing data on a local computer and use a cloud compute engine instead. The presented information system creates a sequence of components, which were subject to verification both on the level of creating and conducting research. Research results that were received were then compared with knowledge presented in the literature. A vital element of validation of the aforementioned methodology was comparing results that were achieved in the course of research work with the system making use of pedometer. The aim of the authors was to develop a new information technology solution, as well as a method based on beacons, which are rather universal devices, with the use of data warehouses, allowing the identification of behavior and physiological state of milk cattle, the method which would be competitive in comparison with the existing solutions, especially in terms of price. In the proposed solution, both information coming from microcomputers and weather forecast data coming from weather forecast stations, which make the above identification easy, were used as data sources.

Large amounts of municipal sewage sludge are generated every year. Composting is one of the most popular and cost-effective methods of handling this waste. It is particularly important to carry out the composting of sewage sludge in an efficient way. The identification of the stage of early maturity of composted material is crucial. The objective of this study was to check the possibility of using convolutional neural networks to classify the early maturity of compost made from sewage sludge and rapeseed straw. The literature review showed that this specific and modern type of neural network had not been investigated for this purpose before. A mixture of these two substrates, composted for 25 days in 7 experiments, was the research material. Images of the material samples were acquired in a dedicated photo station illuminated with: visible light, ultraviolet light, and mixed visible and ultraviolet light. For each acquisition variant 1312 images were obtained. On the basis of the acquired images, 25 convolutional neural networks with one convolutional layer and a varied number of convolution filters were developed to determine the stage of early maturity of the material. Their classification error ranged from 0.51% to 17.77%. The best classification result was obtained for the model containing 16 convolution filters and based on images of the material acquired in mixed visible and ultraviolet light. The results presented in this paper have shown that convolutional neural networks are an adequate tool to assess compost maturity. Such models may provide a simplified and cost-effective approach to the approximate evaluation of the early maturity of compost.

Potato is a major crop all over the world. New farming trends involve modern agrotechnical techniques which significantly minimize the use of chemicals. The problem of selection of the measure and its environment emerged in the validated method used for assessment of the degree of coverage based on image analysis. The aim of this study was to indicate an adequate representative (staining) liquid, background and lighting. In search of a representative liquid enabling distinction between a sprayed and non-sprayed surface the problem of diversity in the brightness of contrasting surfaces was taken into consideration. Having conducted preliminary analyses, the diversity of the spectral composition of reflected light was used. The study resulted in finding an adequate representative working (staining) liquid in the form of diluted ink. The study also confirmed that white emulsion paint was the best background and that white halogen light was optimal for object illumination.

In this paper an attempt was made to build classification models, based on convolutional neural networks, for identification of co-substrate composted with sewage sludge. Due to the pilot character of the studies, they were limited to two co-substrates, i.e. maize straw and rapeseed straw. In total, 12 composting experiments were carried out, each half of them with the content of each of the adopted types of straw. As a result of experiments, 2304 images of composted material samples were obtained, and they bacame the input information for the neural networks. Classification models were developed using the Tensorflow environment, TFLearn library and Python programming language. In their structure, one convolutional layer with different number of convolutional filters and one pooling layer were used to extract image features, and also two fully-connected layers were adopted for classification purposes. The training of the network was carried out with the use of the Adam optimization algorithm. Finally, 4 convolutional neural networks were developed, and their classification error estimated for the test set ranged from 4.1 to 11.0%.

The aim of this research was investigate the possibility of using methods of computer image analysis and neural modeling for assess the amount of dry matter in the tested corn cobs. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in this material. Generated neural models may be the beginning of research into the use of neural image analysis assess the content of dry matter in individual corn fractions. The presented models: RBF 31:31-20-1:1 characterized by RMS test error 0.244136 and RBF 18:22-1-1:1 characterized by RMS test error 0.230206 may be more efficient for more learning data. PiAO software and STATISTICA software were used in this work.

The paper concerns the implementation of research task no. 4 of the PBS3/B8/26/2015 project, the aim of which was to create models of artificial neural networks determining the meat of pork half-carcasses. As a result, thanks to threedimensional scans of the examined half-carcasses, the characteristic cross-section of half-carcasses was determined and the parameters necessary to determine the meatiness were defined. The neon model realizing this task was RBF 9:9-25- 1:1 network, which for the cross-section of 89 was characterized by test quality 0,9887 and RMSE error 0,1556. The work carried out allowed for the development of an algorithm approved by the European Commission on 11 February 2019 np. 2019/252 and the production of devices ESTIMEAT and MEAT3D

The aim of this work was to develop a non-invasive method for the quality assessment of oocytes, performed on the basis of graphic information encoded in the form of monochromatic digital images obtained via microscopy techniques. The classification process was conducted based on the information presented in the form of microphotography pictures of domestic pig oocytes, using advanced methods of neural image analysis. The quality classification process was conducted based on the information presented in the form of microphotography pictures of domestic pig oocytes, using advanced methods of neural image analysis. In order to do that, the discriminative features of oocytes, presented in the digital photographs, were identified and extracted. This was necessary to create empirical training sets required in the process of generating neural classifiers.

Composting is one of the most important methods of sewage sludge management. This paper describes the methods of computer image analysis used for objective comparison of the appearance of composted materials under diverse conditions in terms of size and thermal insulation of the composting chambers. The research material was a mixture of sewage sludge and barley straw. The composting process was performed under strictly controlled laboratory conditions, using 10 composting chambers with five different volumes. In half of them additional thermal insulation was used, while in the others no insulation was applied. A proper composting process run was observed only in the three chambers with the largest volume and with additional thermal insulation. The images of the materials were subjected to a wide analysis, wherein the values of 17 parameters regarding color and texture were estimated. Significant differences were observed in the appearances between materials obtained during the properly running composting processes and those obtained in the chambers of insufficient size and thermal insulation. The values of the considered parameters determined for images of the composted material under normal and abnormal conditions were significantly different from each other. Thus, these parameters may be used as indicators of a correctly conducted composting process. In the cases of 15 parameters, the values of these differences exceeded 10%, and in the cases of 10 parameters 50%, while in the cases of three parameters as much as 100%.

Modern technologies with artificial intelligence are widely used in processing industry and serve, among other things, as boost to increase efficiency and automatization of production processes, which in turn allows to increase productivity of companies while at the same increasing their competitiveness. At present a real challenge for this branch of economy is manufacturing farm and food products, characterized by best parameters in terms of quality while maintaining optical production and distribution costs of biological material which is subject to processing. The given paper touches upon the subject related to evaluation of changes of quality of dried meat made from turkey meat in the process of microwave and vacuum drying as well as its influence on the quality of meat, microstructure and sensory features.

Various mechanisms of mapping relational structures in graph database Neo4j presented in previous publications describing both detailed and more universal solutions, did not take into account the whole complexity of relational databases. The main aim of the aforementioned actions was to utilize the possibilities of graph database of programming languages paying special attention to searching for connections between tables when both their number and relations is very high and documentation is incomplete or unavailable. The effects of queries realization were presented with graph form, which resulted from the potential of the client program of the platform called Neo4j used for the needs of this research. The problem that the authors have failed to take into consideration so far, and that is presented now, is the case of intermediate tables occurrence, which are used in great measure to eliminate many to many relations on the level of relational bases as well as to eliminate relations based on complex keys. Another improvement of the presented application is new user interface, which eliminates the necessity for user of having Cypher language skill in order to query graph database. At the same time the application ensures results visualization in richer form than client program with the engine Neo4j that is available and offered.