نشریه ماشین های کشاورزی
سال پانزدهم شماره 1 (پیاپی 35، بهار 1404)

In this study, the air drying of cumin seeds was boosted by cold plasma pre-treatment (CPt) followed by high-power ultrasound waves (USp). To examine the impact of included effects, different CP exposure times (0, 15, and 30 s), sonication powers (0, 60, 120, and 180 W), and drying air temperatures (30, 35, and 40 ºC) were selected as input variables. A series of well-designed experiments were conducted to evaluate drying time, effective moisture diffusivity, and energy consumption, as well as color change and rupture force of dried seeds for each drying program. Numerical investigations can effectively bypass the challenges associated with experimental analysis. Therefore, the wavelet-based neural network (WNN), the multilayer perceptron neural network (MLPNN), and the radial-basis function neural network (RBFNN), as three well-known artificial neural networks models, were used to map the inputs and output data and the results were compared with the Multiple Quadratic Regression (MQR) analysis. According to the results, the WNN model with an average correlation coefficient of R2 > 0.92 for the train data set, and R2 > 0.83 for the test data set provided the most beneficial tool for evaluating the drying process of cumin seeds.

Optimal use of resources, including energy, is one of the most important principles in modern and sustainable agricultural systems. Exergy analysis and life cycle assessment were used to study the efficient use of inputs, energy consumption reduction, and various environmental effects in the corn production system in Lorestan province, Iran. The required data were collected from farmers in Lorestan province using random sampling. The Cobb-Douglas equation and data envelopment analysis were utilized for modeling and optimizing cumulative energy and exergy consumption (CEnC and CExC) and devising strategies to mitigate the environmental impacts of corn production. The Cobb-Douglas equation results revealed that electricity, diesel fuel, and N-fertilizer were the major contributors to CExC in the corn production system. According to the Data Envelopment Analysis (DEA) results, the average efficiency of all farms in terms of CExC was 94.7% in the CCR model and 97.8% in the BCC model. Furthermore, the results indicated that there was excessive consumption of inputs, particularly potassium and phosphate fertilizers. By adopting more suitable methods based on DEA of efficient farmers, it was possible to save 6.47, 10.42, 7.40, 13.32, 31.29, 3.25, and 6.78% in the exergy consumption of diesel fuel, electricity, machinery, chemical fertilizers, biocides, seeds, and irrigation, respectively.

The IoT monitoring system for stingless bee colonies aims to provide real-time information about temperature, humidity, and hive weight in response to the issue of colony collapse disorder (CCD) caused by human intervention in beekeeping. It also aims to improve the current monitoring methods for the bees more effectively and efficiently. The monitoring system features a water-cooling control system to maintain an optimal temperature for Tetragonula Biroi (Stingless Bees). The system also includes a user dashboard for remote monitoring and alerts the beekeeper when it is time to harvest. It’s primarily built around the ESP8266-MOD microcontroller, with an Arduino Mega 2560 R3 for the water valve control system. Data were collected from a DHT22 sensor for temperature and humidity, and load cells connected to an HX711 amplifier for hive weight. The system was tested by comparing samples from the system and actual measuring instruments using MAPE for two months, and it demonstrated 98.74% and 97.89% accuracy for surrounding temperature and humidity, respectively. An accuracy of 95.92% for the weight scale and 93% for the water valve control system was also obtained. Hives equipped with the IoT system gained 3.414% more weight than those without it, indicating that the project succeeded in achieving its objectives.

Diagnosing plant diseases is an important part of crop management and can significantly affect the quantity and quality of production. Traditional methods of visual assessment by human observers are time-consuming, costly, and error-prone, making accurate diagnosis and differentiation between various diseases difficult. Advances in agriculture have enabled the use of non-destructive machine vision systems for plant disease detection, and color imaging sensors have demonstrated great potential in this field. This study presents a framework for diagnosing early blight and late blight diseases in potatoes using a combination of Relief feature selection algorithms and Random Forest classification, along with color, texture, and shape features in three color spaces: RGB, HSV, and CIELAB (Lab*). The results indicated that the diagnostic accuracy for the early blight and late blight disease groups, as well as the healthy leaf group, were 94.71%, 95%, and 95.2%, respectively. The overall accuracy for disease classification was 95.99%. Additionally, the diagnostic accuracy for the early blight and late blight disease groups, along with the healthy leaf group, was 91.07%, 98.36%, and 98.93%, respectively, with an overall classification accuracy of 96.12%. After separating the diseased area from the healthy part of the leaf, a total of 150 features were extracted, including 45 color, 99 textural, and 6 shape features. The most effective features for disease detection were identified using a combination of all three feature sets. This study demonstrated that integrating these three sets of features can lead to a more accurate classification of potato leaves and provide valuable insights into the diagnosis and classification of potato diseases. This approach can help farmers and other plant disease specialists to accurately diagnose and manage potato diseases, and ultimately lead to an increase in product quality and yield.

In Iran, more than 50,000 hectares of sunflowers (oil and nuts) are cultivated annually. Conventional grain combine harvesters are not compatible with the unique characteristics of sunflowers, leading to significant grain losses during harvesting. Therefore, it is currently being harvested manually. Manual harvesting increases labor hardships, energy and time consumption, and production costs. In this research, to harvest sunflower seeds, modifications were made on conventional head of a combine harvester (John deer 1055) to allow simultaneous harvesting, threshing, and cleaning of the sunflower seeds. After designing and fabricating the accessory, the improved head in field conditions was evaluated and compared with conventional harvesting methods. The field evaluation of the improved head was based on a randomized complete block design with three replications. The treatments involved three different harvesting methods 1) using a modified combine head, 2) employing a combine equipped with pan attachment, and 3) manual harvesting. In each of the machine treatments, beating and cleaning units were set up for sunflower harvest. The results showed that there was a significant difference between the treatments concerning machine losses, field capacity, and harvesting costs, all at the 5% significance level. In the modified combine, combine with pans attachment, and manual method, combine losses were 0.72, 4.85, and 6%, and field capacity was 1.2, 1.13, and 0.12 ha h-1, respectively. The profit-to-cost ratio was 13.97, 13.3, and 3.01, respectively. The grain breakage percentage was 3, 3.3, and 0.56, respectively. According to the results, due to lower losses, appropriate field capacity, and lower harvesting costs, the use of John deer 1055 combine with the modified head is recommended for harvesting of the sunflower.

The main objective of this research is to create a comprehensive and adaptable framework for assessing performance in agricultural supply chains and develop two improving approaches. The most relevant performance measures are selected to assess the current status of services in agricultural supply chains (ASCs). The contribution of this research is related to the selection of key performance indicators (KPIs) and approaches for enhancing ASC performance. The proposed framework comprises performance measurement and a service selection process. Two approaches have been developed based on the selected KPIs of services in ASC to identify which services require improvement. The proposed approaches are robust and versatile tools for agricultural managers to strategize and enhance their supply chains. A case study is also presented from Iran. For this region, selection approaches prioritize agricultural services such as postproduction consulting, financial support, mechanization, business consulting, and input supply. The framework shows that these services should be improved in order to better meet the needs of the region under study.

Magnetic resonance imaging (MRI) is a non-destructive technique for determining the quality of fruits which, with different protocols, shows the density and structure of hydrogen atoms in the fruit in which it is placed. This study compared MRI images of healthy and bruised apple flesh tissues, both with and without pests, using various protocols to identify the best one. For this purpose, magnetic resonance imaging (MRI) using two protocols: T1 (Spin-lattice relaxation time) and T2 (Spin-spin relaxation time), was carried out on 200 apple fruits that were loaded during storage. The loading of fruits was performed at four levels: 150, 300, 450, and 600 N in a quasi-static manner, and then stored for periods of 25, 50, and 75 days at 4 °C. At the end of each storage period, imaging was carried out. Then, the contrast of T1 and T2 images of healthy and bruised tissue of apple fruit with and without pests using ImageJ software was determined. It was concluded that the healthy tissue of apple fruit without pests was clearer in T1 images than in T2 images. It has also been seen that the bruised area of fruits without pests in T2 images is more recognizable than in T1 images.

In some countries, people commonly consume hazelnuts in their shells to extend shelf life or due to technological limitations. Therefore, open-shell hazelnuts are more marketable. At the semi-industrial scale, open-shell and closed-shell hazelnuts are currently separated from each other through visual inspection. This study aims to develop a new algorithm to separate open-shell hazelnuts from cracked or closed-shell hazelnuts. In the first approach, dimension reduction techniques such as Sequential Forward Feature Selection (SFFS) and Principal Component Analysis (PCA) were used to select or extract a combination of color, texture, and grayscale features for the model’s input. In the second approach, individual features were used directly as inputs. In this study, three famous machine learning models, including Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-Layer Perceptron (MLP) were employed. The results indicated that the SFFS method had a greater effect on improving the performance of the models than the PCA method. However, there was no significant difference between the performance of the models developed with combined features (98.00%) and that of the models using individual features (98.67%). The overall results of this study indicated that the MLP model, with one hidden layer, a dropout of 0.3, and 10 neurons using Histogram of Oriented Gradients (HOG) features as input, is a good choice for classifying hazelnuts into two classes of open-shell and closed-shell.