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Abstract The decrease in the possibilities of increasing the arable agricultural areas in the world and the continuous increase in the population have led those who are engaged in plant production to seek ways to make maximum use of the existing agricultural areas. One of these ways is mixed sowing systems. It is very difficult to sow species with different grain sizes in mixtures. Special sowing machines are needed for this aim. Because of this reason, the article aims to be a guide for artificial intelligence capable of mixed sowing in forage crops. In the research, it is found that there are some differences between YOLOv4-tiny and Y0L0v4 models as Precision, Recall, F1-score, TP, FP, FN scores. For the YOLOv4-tiny model, these scores were found as 0.99, 1.00, 0.99, 90, 1, 0, respectively and the scores for the Y0L0v4 model were 1.00, 1.00, 1.00, 90, 0, 0. According to the YOLOv4-tiny and YOLOv4 tests in the lab, suggesting that the YOLOv4-tiny is faster, and the YOLOv4 is more reliable in terms of all these factors combined. This research establishes a standard for real-time recognition of forage crops based on current technology at NVIDIA Jetson TX2 due to its high performance and low power consumption and a high-performance computer with CUDA support.

Abstract This research is aimed to explore the efficacy and comparison of low-cost light sensor measurements for agricultural applications. For this reason, TSL2561, BH1750, and TEMT6000, low-cost light sensors have been used with various lighting regimes. Consequently, their efficiency has been tested for agricultural applications. These types of low-cost light sensors can be used in a range of applications in agriculture. For this reason, it is necessary to evaluate the calculation of light sources. The light calculation values of low-cost light sensors were statistically compared. Calibration results were also analyzed, so the reliability of low-cost light sensors was detected. Statistical analyses of light values have been presented in the results and discussion part of the paper as; the regression coefficient based on UNI-T UT382 vs. TSL2561 for the calibration was found as R-Sq 99,6%, R-Sq(adj) 99,5%, and S 10,18; for the calibration of UNI-T UT382 vs. BH1750 was found as R-Sq 100%, R-Sq(adj) 100% and S 2,73; at last for the calibration of UNI-T UT382 vs. TEMT6000 was found as R-Sq 99,2%, R-Sq(adj) 99,1% and S 14,71. After calibration, the regression coefficient based on UNI-T UT382 vs. TSL2561 for the light measurement was found as R-Sq 100%, R-Sq(adj) 100%, and S 2,21; the light measurement for UNI-T UT382 vs. BH1750 was found as R-Sq 100%, R-Sq(adj) 100% and S 1,09; also for the UNI-T UT382 vs. TEMT6000 was found as R-Sq 100%, R-Sq(adj) 100% and S 1,73. According to calibration and measurement results, the BH1750 sensor is better than the others.

It is well known that the need for quality roughage to maintain the level of intake by livestock is not met in Turkey. It is a fact accepted by everyone that the resulting deficit is tried to be compensated by using cereal straw. The annual roughage production of Turkey is 29.6 million tons, which meets 37.6% required amount for maintenance of 17.1 million animal units in Turkey. The quality roughage needed in Kırşehir province is 836.3 thousand tons for 181.8 thousand animal units, whereas the current roughage production is only 97.4 thousand tons. The roughage deficit of Turkey is 49 million tons while this deficit is 738.9 thousand tons in Kırşehir. The deficiency in roughage production in Turkey is much more critical in Kırşehir province. The compensative ratio of quality roughage production in Kırşehir province is only 11.6%. Therefore, increasing the quality of roughage production has become an urgent obligation for both Turkey and Kırşehir province. The funded roughage production by the government has led to a significant increase in the cultivation areas and the amount of production. However, even these increases are far from meeting the current need. To reach the targeted quality roughage production; (1) the culture of forage crops should be adopted by the producers, (2) the forage crops should economically be competitive with the other crops, (3) the farmers should be convinced of the benefits of roughage other than the need for animal feeding, and (4) pastures should be improved and used according to management rules.