AgriEngineering, Volume 3, Issue 3 (September 2021) – 16 articles

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h. Full article

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare ($TSDM$ (tha⁻¹)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients ($R^2$) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 $TSDM$ (tha⁻¹). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base. Full article

Vegetable transplanting is an important and advantageous practice in vegetables production systems. In recent years, the development of vegetable transplanting tools has increased, as well as the interest for automatic and robotic transplanters. However, at present, the feeding of transplanting machines is often still performed by hand. This paper presents the design, development and testing of a needle gripper and a two-finger gripper for vegetable transplanting. Both grippers were self-designed and tested for picking, lifting and transplanting plug seedlings. Tests have been conducted on fennel (Foeniculum vulgare L.), leek (Allium ampeloprasum L.) chicory (Cichorium intybus L.) and lettuce (Lactuca sativa L.) seedlings to determine the impact that gripper typology might have on the further growth of plants after transplanting. The average success rate of the two-finger gripper in the transplanting experiment was 95% and of the needle gripper 81.75%, respectively. Although neither gripper typology affected the growth of the seedlings after transplanting, several design implications were identified in order to improve the performance of both grippers. Furthermore, the two-finger gripper is more reliable for lettuce and chicory, while the needle gripper requires root plugs with higher firmness and cohesion to prevent shattering. Full article

Chlorophyll fluorescence is interesting for phenotyping applications as it is rich in biological information and can be measured remotely and non-destructively. There are several techniques for measuring and analysing this signal. However, the standard methods use rather extreme conditions, e.g., saturating light and dark adaption, which are difficult to accommodate in the field or in a greenhouse and, hence, limit their use for high-throughput phenotyping. In this article, we use a different approach, extracting plant health information from the dynamics of the chlorophyll fluorescence induced by a weak light excitation and no dark adaption, to classify plants as healthy or unhealthy. To evaluate the method, we scanned over a number of species (lettuce, lemon balm, tomato, basil, and strawberries) exposed to either abiotic stress (drought and salt) or biotic stress factors (root infection using Pythium ultimum and leaf infection using Powdery mildew Podosphaera aphanis). Our conclusions are that, for abiotic stress, the proposed method was very successful, while, for powdery mildew, a method with spatial resolution would be desirable due to the nature of the infection, i.e., point-wise spread. Pythium infection on the roots is not visually detectable in the same way as powdery mildew; however, it affects the whole plant, making the method an interesting option for Pythium detection. However, further research is necessary to determine the limit of infection needed to detect the stress with the proposed method. Full article

Minimal tillage methods mainly include subsoiling technology and topsoil tillage technology. Based on the analysis of domestic technical modes and application status of minimal tillage, this paper reviewed the working principle, technical characteristics and research status of subsoiling and topsoil tillage in two key parts. Current technical difficulties were analyzed and generalized, combined with the research progress and application requirements of minimal tillage in China, and future research emphasis and development direction were provided. Full article

In olive oil extraction processes, different operating methods used for the preparation of olive pastes significantly affect their rheological characteristics, as well as the extraction yields and qualitative characteristics of the oils. To enhance and improve the characteristics of high-quality EVOOs (Extra Virgin Olive Oils), milling technologies have implemented olive pitting in the preparation of olive pastes to be processed for olive oil extraction. Commonly used pitting machines employ the percussion and centrifugal projection of drupes, which often involve the heating of pastes, breaking of kernels, and emulsion of oils. Aiming to improve olive oil pitting processes, the CREA Research Centre for Engineering and Agri-food Processing in Treviglio, Italy, has conceived an alternative method, which is based on the low-speed constriction and mutual abrasion of drupes inside a rotative working chamber. This paper describes the process that led to the hypothesis of an innovative pitting method and to the validation of the hypothesis through the development of a lab-scale pitter prototype. The development steps and the assessment of the results of the prototype trials are reported. Full article

Minimum or no-till seeding technology is the core of conservation tillage, which can effectively reduce soil degradation by water and wind erosion. It is an essential part of agricultural modernization. The anti-blocking technology is the key to realize minimum or no-till seeding technology. According to the principle, it can be divided into three types: straw-flowing type, gravity-cutting stubble type, and power-driven type. Emphasis is placed on the anti-blocking principle, technical characteristics, and development trends of minimum or no till seeders based on three different anti-blocking principles. In view of analyzing and summarizing the advantages and disadvantages of three technologies and typical machines, the future development trends of minimum or no-till seeders were prospected as follows: (1) strengthening research on basic theories and integration mechanisms; (2) building a big data-sharing platform for seeding operations; (3) establishing and improving specific systems of minimum and no-till seeders with China character. Full article

There are two key parameters in short-haul truck operations to deliver biomass to a biorefinery: (1) mass of the load and (2) cycle time (load, travel, unload, and return). A plan to optimize both these parameters is outlined in this study. Operation of a logistics system to deliver 20-bale racks to a biorefinery for continuous 24/7 operation, 48 weeks/year is described. Round bales are stored in satellite storage locations (SSLs) by feedstock producers. A truckload consists of two tandem trailers (40, 0.4 Mg bales), a specification that maximizes load mass. Load-out at the SSL (loading bales into racks) is performed by a contractor and paid by the biorefinery. Subsequent hauling (truck tractor to pull the trailers) is also contracted for by the biorefinery. Central control is specified; the “feedstock manager” at the biorefinery decides the order SSLs are loaded out and can route a truck to any SSL where a load is ready. Tandem trailers with empty racks are dropped at the SSL, and the trailers with full racks are towed to the biorefinery. Uncoupling the loading and hauling in this manner reduces the time the truck waits for loading and the SSL load-out waits for a truck; thus, productivity of both operations is increased. At the biorefinery receiving facility, full racks are removed from the trailers and replaced with empty racks. The objective for this transfer is a 10 min unload time, which completes a logistics design that minimizes cycle time. A delivered rack is placed in a rack unloader to supply bales for immediate processing, or it is placed in central storage to supply bales for nighttime and weekend operations. Three biorefinery capacities were studied: 0.5, 1.0, and 1.5 bale/min. The analysis shows that rack cost to supply a biorefinery processing a bale/min for 24/7 operation is ~3.00 USD/Mg of annual biorefinery capacity, and the rack trailer cost is ~3.25 USD/Mg. Total delivery cost, beginning with bales in SSL storage and ending with a rack being placed in an unloader to deliver individual bales for processing, is 31.51, 28.42, and 26.92 USD/Mg for a biorefinery processing rates of 0.5, 1.0, and 1.5 bale/min, respectively. Full article

Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time. Full article

Basil (Ocimum basilicum) was cultivated in three hydroponic subsystems (i) a modified commercial aeroponics, (ii) a dynamic root floating (DRF) system, and (iii) a floating raft system in a decoupled aquaponic system in Northern Germany, Mecklenburg–Western Pomerania. For plant nutrition, aquaculture process water from intensive rearing of African catfish (Clarias gariepinus) was used without fertilizer. After 39 days, 16 plant growth parameters were compared, with aeroponics performing significantly better in 11 parameters compared with the DRF, and better compared with the raft in 13 parameters. The economically important leaf wet and dry weight was over 40% higher in aeroponics (28.53 ± 8.74 g; 4.26 ± 1.23 g), but similar in the DRF (20.19 ± 6.57 g; 2.83 ± 0.90 g) and raft (20.35 ± 7.14 g; 2.84 ± 1.04 g). The roots in the DRF grew shorter and thicker; however, this resulted in a higher root dry weight in aeroponics (1.08 ± 0.38 g) compared with the DRF (0.82 ± 0.36 g) and raft (0.67 ± 0.27 g). With optimal fertilizer and system improvement, aquaponic aeroponics (s.s.) could become a productive and sustainable large-scale food production system in the future. Due to its simple construction, the raft is ideal for domestic or semi-commercial use and can be used in areas where water is neither scarce nor expensive. The DRF system is particularly suitable for basil cultivation under hot tropical conditions. Full article

Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) have developed and examined various traditional machine learning (ML) and deep learning (DL) algorithms for plant disease classification. However, through pass survey analysis, we found that there are no studies comparing the classification performance of ML and DL for the tomato disease classification problem. The performance and outcomes of different traditional ML and DL (a subset of ML) methods may vary depending on the datasets used and the tasks to be solved. This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem. For machine learning algorithm implementation, we used different methods to extract disease features manually. In our study, we extracted a total of 52 texture features using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) methods and 105 color features using color moment and color histogram methods. Among all the feature extraction methods, the COLOR+GLCM method obtained the best result. By comparing the different methods, we found that the metrics (accuracy, precision, recall, F1 score) of the tested deep learning networks (AlexNet, VGG16, ResNet34, EfficientNet-b0, and MobileNetV2) were all better than those of the measured machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF)). Furthermore, we found that, for our dataset and classification task, among the tested ML/DL algorithms, the ResNet34 network obtained the best results, with accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and F1 score of 99.7%. Full article

The fresh fruit agricultural and distribution sector is faced with risks and uncertainties from climate change, water scarcity, land-use increase for industrial and urban development, consumer behavior, and price volatility. The planning framework for production and distribution is highly complex as a result. Mathematical models have been developed over the decades to deal with this complexity. With improvements in both processor speed and memory, these models are becoming increasingly sophisticated. This review focuses on the recent progress in mathematically based decision making to account for uncertainties in the fresh fruit supply chain. The models in the literature are mostly based on linear and mixed integer programming and involve variants such as stochastic programming and robust optimization. The functional areas of application include planting, harvest optimization, logistics and distribution. The perishability of the fresh fruit supply chain is an important issue as is the cycle time of cultivation and harvest. Full article

The removal of plastic contamination from cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales is plastic used to wrap cotton modules produced by John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during module unwrapping, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin, a machine-vision detection and removal system was developed that utilizes low-cost color cameras to see plastic coming down the gin-stand feeder apron, which upon detection, blows plastic out of the cotton stream to prevent contamination. This paper presents the software design of this inspection and removal system. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. The focus of this report is to describe the software design and discuss relevant issues that influenced the design of the software. Full article

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species. Full article

A field area coverage-planning algorithm has been developed for the optimization and simulation of capacitated field operations such as the organic fertilizer application process. The proposed model provides an optimal coverage plan, which includes the optimal sequence of the visited tracks with a designated application rate. The objective of this paper is to present a novel approach for route planning involving two simultaneous optimization criteria, non-working distance minimization and the optimization of application rates, for the capacitated field operations such as organic fertilizer application to improve the overall operational efficiency. The study and the developed algorithm have shown that it is possible to generate the optimized coverage plan based on the required defined capacity of the distributer. In this case, the capacity of the distributer is not considered a limiting factor for the farmers. To validate this new method, a shallow injection application process was considered, and the results of applying the optimization algorithm were compared with the conventional methods. The results show that the proposed method increase operational efficiency by 19.7%. Furthermore, the applicability of the proposed model in robotic application were demonstrated by way of two defined scenarios. Full article

Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers’ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors ‘Lying down’ followed by ‘Lying laterally’ occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controllers. Full article