Agricultural irrigation developments have gained attention to improve crop yields and reduce water use. However, traditional irrigation requires excessive amounts of water and consumes high electrical energy to schedule irrigations. This paper proposes a fuzzy-based intelligent irrigation scheduling system using a low-cost wireless sensor network (WSN). The fuzzy logic system takes crop and soil water variabilities into account to adaptively schedule irrigations. The theoretical crop water stress index (CWSI) is calculated to indicate plant water status using canopy temperature, solar irradiation, and vapor pressure deficit. Furthermore, the soil moisture content obtained by a capacitive soil moisture sensor is used as a determination of water status in soil. These two variables are thus incorporated to improve the precision of the irrigation scheduling system. In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance. Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%. The experimental results reveal that the proposed irrigation scheduling system is effective in terms of precision irrigation scheduling and efficient regarding water use and energy consumption. Finally, the cost analysis is performed to confirm the economic benefit of the proposed irrigation scheduling system. INDEX TERMS Crop water stress index (CWSI), Fuzzy logic system, Irrigation scheduling, Wireless sensor network (WSN), Soil moisture content.

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This research aimed to create a tool for pineapples quality grading according to the standard weight and size of Thai Agricultural Commodity Food Standard. The standard weights of pineapple are divided into 10 levels (A-J) and the standard sizes of pineapple are categorized into two classes (class I and class II). The developed tool consists of hardware components and a grading software program. The control light source box was constructed for camera and load cell installation to capture pineapple image and measure pineapple weight, respectively. The obtained image was sent to software program to change colors of the image into gray scale and to reduce noises in the image. The clearly edges of the image were employed to compute size of a pineapple and the data were transferred to fuzzy system. The inputs of fuzzy system determined the size and weight of pineapple which used to establish twenty fuzzy rules. The experiments performed by random selection size and weight of three pineapple kinds including Nanglae, Sriracha, Phuket. The experimental results reveal that classification of pineapple by the created tool exhibited high accuracy of size and weight detection equaled 87.5%. The average relative error performed 2.30% and 5.24% of size and weight, respectively.

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