The experiment field was carried out during spring season of 2006 on experimental farm of Field Crop Science Department College of Agriculture, University of Baghdad, to study the effect of foliar applications of Ethephon, Boron and Zinc on the number of vascular bundles in stem and some characteristics of xylem- in sunflower hybrid (ZahratilIraq).
By using the Randomized Complete Block Design with three replications. Eight treatment were used: control treatment (with water spray only), Ethephon (0.480 kg.a.i/ha), Boron (200 g/L), Zinc (50 mg/L) and the interaction between them. The results revealed significant anatomical changes in disc peduncles of plants caused by the foliar applications of Ethephon, Boron, and Zinc treatments. Bor

  This paper deals  the prediction of the process of  random spatial data of two properties, the first is called  Primary variables  and the second is called secondary  variables ,   the method  that were used in the  prediction process for this type  of data is technique Co-kriging  , the method is usually used when the number of primary variables  meant to predict for one of its elements is measured in a particular location a few (because of the cost or difficulty of obtaining them) compare with secondary variable which is the number of elements  are available and  highly correlated with primary variables, as was the&nbs

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.

EFFECT OF SPRAYING IRON AND ZINC CONCENTRATIONS IN GRAIN AND LEAF CONTENT FOR TWO VARIETIES OF WHEAT CROP

     Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the a

A field experiment was conducted during the spring season 2020 in Karbala proving/ Al-Sharia Distrit, located at latitude N 32° 42' 13.8" and longitude E 43° 54' 36.6" and at an altitude of 27 m above sea level. The experiment included a study of two factors: the first, Irrigation Interval, three treatments were used: irrigation treatment every 2 days, Irrigation treatment every 4 days, and Irrigation treatment every 6 days. The second factor is the addition of soil conditioners, in which four treatments were used: the control treatment without any addition, the treatment of adding bio-organic fertilizers, the treatment of adding water-conserving technology (polymer), and the treatment of adding water-conserving technology + fertilizers O

     The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mea