Genetics of Fertility Restoration in Cytoplasmic Male Sterile Pepper

Pepper hybrid seeds production using male sterility could lower cost by reducing time and labour, and increase the genetic purity of the F1 seeds. To investigate the genetics of fertility restoration of the Peterson cytoplasmic sterility in pepper, a doubled haploid population of 115 pepper lines obtained from anther culture of the F1 hybrid between Yolo Wonder （sterility maintainer line） and Perennial （fertility restorer line） and the parental lines were test-crossed by 77013A （a strict cytoplasmic-genic male sterile line）. The fertility of the test-crossed lines was assessed in greenhouse and open field with the following three criteria： pollen index （PI, visual estimation of pollen amount per flower）, pollen number （PN, pollen counting under microscope）, and seed number （SN, the number of seeds per fruit in open pollination）. Correlations between the each couple of criteria within, as well as between the cultivation methods ranged from 0.55 to 0.84. Analysis of variance showed that the genotype （DH line） and environment were the significant sources of variation of the fertility. Narrow sense of heritance of fertility restoration ranged from 0.38 to 0.92, depending on the criteria and environment. The distribution of the progeny was continuous between the parental genotypes indicating the quantitative inheritance of fertility restoration. Inferred from segregation according to Snape et al.（1984）, the number of segregating genes was estimated to be that three to four genetic factors were involved in pollen traits （PI and PN） and five to eight genetic factors in seed production （SN）. The heredity analysis of the CMS will be helpful for understanding of the genetic mechanism of the fertility restoration and the exploitation of the CMS in hybrid seed production.

An Ingenious IoT Based Crop Prediction System Using ML and EL

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield.Python is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable crop.We implemented the proposed system in the field.The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.