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    Chitin and chitosan from shellfish waste and their applications in agriculture and biotechnology industries
    (2025-01-01)
    Rai, Sampurna
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    Pokhrel, Prashant
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    Udash, Pranaya
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    Chemjong, Menjo
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    Bhattarai, Namita
    A shellfish processing plant generates only 30–40% of edible meat, while 70–60% of portions are considered inedible or by-products. This large amount of byproduct or shellfish processing waste contains 20–40% chitin, that can be extracted using chemical or greener alternative extraction technologies. Chitin and its derivative (chitosan) are natural polysaccharides with nontoxicity, biocompatible, and biodegradable properties. Due to their versatile physicochemical, mechanical, and various bioactivities, these compounds find applications in various industries, including: biomedical, dental, cosmetics, food, textiles, agriculture, and biotechnology. In the agricultural sector, these compounds have been reported to promote: plant growth, plant defense system, slow release of nutrients in fertilizer, plant nutrition, and remediate soil conditions, etc. Whereas, biotechnology applications indicated: enhanced enzyme stability and efficacy, water purification and remediation, application in fuel cells and supercapacitors for energy conversion, acting as a catalyst in chemical synthesis, etc. This review provides a comprehensive discussion on the utilization of these biopolymers in agriculture (fertilizer, seed coating, soil treatment, and bioremediation) and biotechnology (enzyme immobilization, energy conversion, wastewater treatment, and chemical synthesis). Additionally, various extraction techniques including conventional and non-thermal techniques have been reported. Lastly, concluding remarks and future direction have been provided.
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    Assessment of Potential Area for Solar Energy Investment in Northeastern Thailand by Entropy-TOPSIS Method
    (2024-01-01)
    Phonphoon, Phichata
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    Kiatcharoenpol, Tossapol
    ;
    Klongboonjit, Sakon
    Agriculture is one of the important engines of Thailand's food industrial growth however the cost of fossil energy is quite high for Thai farmers. Since Thailand has great solar potential, especially northeastern region, renewable energy sources of sunlight in this region should be considered to be another energy source for Thai agriculture. To assess and classify the potential of the investment in agricultural solar power systems of 20 provinces in Thailand's northeastern region, this study applied the combining method of Entropy Weight Method and TOPSIS with secondary data of solar irradiance, farmer household density, and income of farmer households. With this combining method, the results showed that farmer household density and income of farmer household were more influence on assessing and classifying the potential of this investment than solar irradiance. Finally, all 20 provinces were classified into four groups of Group A (A<inf>12</inf>, A<inf>2</inf>, A<inf>4</inf>, A<inf>1</inf>, and A<inf>10</inf>), Group B (A<inf>8</inf>, A<inf>9</inf>, A<inf>19</inf>, A<inf>3</inf>, and A<inf>15</inf>)), Group C (A<inf>17</inf>, A<inf>13</inf>, A<inf>18</inf> A<inf>16</inf>, and A<inf>20</inf>) and Group D (A<inf>6</inf>, A<inf>11</inf>, A<inf>7</inf>, A<inf>14</inf>, and A<inf>5</inf>) from the most potential province group for investment in agriculture solar power system to the least potential province group for investment in agriculture solar power system.
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    PigSense: Structural Vibration-based Activity and Health Monitoring System for Pigs
    (2023-10-18)
    Dong, Yiwen
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    Bonde, Amelie
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    Codling, Jesse R.
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    Bannis, Adeola
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    Cao, Jinpu
    Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sow and piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditions while remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poor maternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress over the lactation cycle.
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    Android-based gardening robot with fuzzy variable set model
    (2017-11-03)
    Tangtisanon, Pikulkaew
    The aim of this paper is to build android-based gardening robot that can be controlled by Android-based smartphone via long distance. Moreover, the robot can be used to check humidity of the soil and sending the information to a cloud server. The information from the server will be used in the fuzzy algorithm to determine plan of the planting for the user. In the present study, two Android based smartphone are required. One smartphone was attached to the robot and acted as a robot's eyes and brain. Another was held by the user to monitor a video content sending from the robot's eyes. The user can control a movement of the robot by sliding fingers on a touch screen or tilting the smartphone. If he sees weeds along the way, he can send the command to the robot to immediately cut it. Furthermore, the user also can ask the robot to measure a soil humidity and view the result online in real-time.