Precise irrigation plays an essential role in agricultural production and its management. Based on current conditions and historical records, profitability in the farming sector depends on making the right and timely operational decision. For the last two decades, especially in India, climate change, groundwater depletion, and erratic variation in rainfall have affected crop production significantly. Due to advancements in technologies and reduction in size, sensors are becoming involved in almost every field of life. Agriculture is one such domain where sensors and their networks are successfully used to get numerous benefits from them. In this paper, a review of the scope of smart irrigation using IoT has been discussed. The scarcity of agricultural workers in irrigation can be compensated by the Internet of Things (IoT) platform. The various parameters, such as soil moisture, soil temperature, humidity, and pH, have been collected using the Internet of Things (IoT) platform, equipped with sensors and wireless communication systems (WSN).
Weed is a crop which is grown along with the main crop and competes for sunlight, food, water and space. Weeding is very tedious operation as compared to all other agriculture operations. Most parts of the country weeding operation is being done by manual methods and also by mechanical methods. Drudgery involved in weeding operation increases stress on worker causing increase in heart rate and oxygen consumption. The main objective of the study was to evaluate the agronomical evaluation of power weeder. The heart rate and oxygen consumption rate of operator was varied from 131.0 to 145.5 beats/min and 0.80 to 0.98 l/min, respectively. The energy expenditure rate of operator ranged from 4.01 to 4.90 kcal/min.
Leaf area (LA) measurement provides valuable key information in understanding the growth and physiology of a plant. Simple, accurate and non-destructive methods are inevitable for leaf area estimation. These methods are important for physiological and agronomic studies. However, the major limitations of existing leaf area measurement techniques are destructive in nature and time consuming. Therefore, the objective of the present work is to develop ANN and linear regression models along with image processing techniques to estimate spinach leaf area making use of leaf width (LW) and length (LL) and comparison of developed models performance based on the statistical parameters. The spinach leaves were grown under different nitrogen fertilizer doses (0, 50, 100, 150, 200, 250, 300, 350 and 400 kg N/ha). The morphological parameters length (LL), width (LW) and area (LA) of leaves were measured using an image-processing software. The performance LA= -0.66+0.64 (LL × LW) (R2 = 0.98, RMSE = 3.25 cm2) equation was better than the other linear models. The performance of the ANN model (R2 = 0.99, RMSE = 3.10 cm2) was better than all other linear models. Therefore, developed models along with image processing techniques can be used as a non-destructive technique for estimation of spinach leaf area.
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