Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources.

Heritability of quantitative characters of local black rice (Oryza sativa L.) results of generation F3

Local Black rice is a food crop variety that has a low productivity level and a longer planting time than other colored rice. In addition, black rice contains anthocyanins, which are antioxidant molecules that can counteract free radicals in the human body. Anthocyanin levels can be increased by producing full black potential lines. Effective and efficient selection with heritability analysis. This study aims to determine the heritability of the agronomic character of black rice from the F3 generation line. The study was conducted in the Manipi Sinjai rice field in February – August 2020. The experiment used was a completely randomized design, there were 17 lines with 3 replications. The data obtained were analyzed by means of variance, and if there was a significant effect of treatment, further tests were carried out with the BNT 0.05 test. In addition, the heritability of the observed characters was determined. The results showed that the character of plant height in the flowering phase, days to 50% flowering, and the percentage of grain with full black endosperm had heritability values with high criteria.

Farmers' Response on Field Performance of BSMRAU Developed IPSA Seem and BU Pepe1 Crop Variety

The study was conducted to investigate the adoption of IPSA seem and BU pepe1 crop variety by the farmers in Bhaluka upazila of Mymensingh and Meherpur Sadar upazila of Meherpur districts, respectively in Bangladesh. In-person interviews with 80 respondents and two focus group discussions were carried out to collect data. According to study findings, the highest portion of the respondents were young aged, literate, had medium farm size, low farming experience, and organizational participation and their average annual income were Tk. 192850 and Tk. 200500 for IPSA seem and BU pepe1 growers, respectively. Extent of adoption was above fifty percent in both cases of IPSA seem and BU pepe1 whereas the extent of BU pepe1 adoption was higher than IPSA seem. Performance of IPSA seem and BU pepe1 was satisfactory to the farmers in terms of ease of handling, better marketability and adaptation to the environment, vigor, and better physical appearance. Majority of the IPSA seem and BU pepe1 respondents experienced a medium increase in crop yield while medium to high-income increase by cultivating those varieties. Majority of them had a low to moderate knowledge gap in cultivating IPSA seem and BU pepe1. The major problems faced by the farmers were pod borer infestation, common mosaic virus for IPSA seem while low germination percentage, common mosaic virus for BU pepe1. The study results showed that respondents with small farm sizes were more interested in adopting IPSA seem than others. So, engaging small farmers in cultivating IPSA seem would make this technology more available and popular among the farmers.

A novel method for peanut variety identification and classification by Improved VGG16

Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.

Research Achievements in Relation to Maize (Zea mays L) Crop Production and Productivity in Ethiopia: A Systematic Review

Despite the conducting much number of maize researches in different centers a little change on production and productivity in Ethiopia. Hence this review aimed to indicate how a maize production and productivity have been developed and used to inform that food security concern body of the country. I researched for different papers reporting maize production achievements, databases of peer review journal articles, scholar Google and other web sites. A total of 70 papers were reviewed from which 20 papers are included and from this 51.2% describe about maize production, 34.6% describe about productivity of a crop per area and 14.2% describe about deficiency of agricultural input utilization by local farmers. The use of new crop variety and artificial fertilizers is relatively a wide spread throughout the country. However, practical application on small holder’s field has less technical support and comparatively traditional way of crop managements have been involved. Most of the research findings, particularly those from agronomic practices, indicated that Maize has wide flexibility that is suitable for production.

Adaptation Strategies in Response to the Effect of Climate Change on Tomato Production

Climate change is one of the largest challenge of this century. Globally, climate change causes drops in yield for their most valuable crops, particularly in developing countries. Afghanistan is one of the world's most vulnerable countries to the negative consequences of climate change. Tomato cultivation is a means of livelihood for most farmers in Afghanistan. The aim of this study was to assess the adaptation strategies on tomato production in response to the impact of climate change in Ghazni province. The study findings indicate a rise in both maximum and minimum temperatures, combined with a decline in annual precipitation over the ten years (2008-2017) period in an unreliable seasonal distribution. The study found that the occurrence of pests and diseases had a substantial impact on tomato production due to climate change. Present study highlights the role of climate variables in the production of tomatoes (temperature and precipitation) while controlling other confounding factors. Selection of crop variety according to climate change and planting time are the two adaptation methods to cope up with the drastic change in the climate to retain the productivity to some extent.

Constructing Crop Portraits Based on Graph Databases Is Essential to Agricultural Data Mining

Neo4j is a graph database that can use not only data, but also data relationships. Crop portraits, a kind of property graph, model the crop entity in the real world based on data to realize the networked management of crop knowledge. The existing crop knowledge base has shortcomings such as single crop variety, incomplete description, and lack of agricultural knowledge. Constructing crop portraits can provide a comprehensive description of crops and make up for these shortcomings. This research used agricultural question-and-answer data and popular science data obtained by text crawling as the original data, selected labels to establish a crop portrait that including three categories (crops, pesticides, and diseases and pests), and used the graph database (Neo4j) to store and display these portrait data. Information mining found that the crop portrait revealed the occurrence trend of diseases and pests, exhibited a nonintrinsic connection between different diseases and pests, and provided a variety of pesticides to choose from for control of diseases and pests. The results showed that constructing crop portraits is beneficial to agricultural analysis, and has practical application values and theoretical research prospects in the field of big data analytics.