22 pages, The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture.
5 pages., This research aimed to figure out the attitude and readiness of agriculture extension officers
in using the Information and Communication Technology. Data collection was done through a
survey with total sample 60 respondents. Data gained were primary data from questionnaire
filling by respondents who were all extension officers in Food Security and Extension
Implementation Agency. Data analyses used in this research were reliability and validity analysis, Fishbein’s Attitude Model, and regression analysis which continued with F and t test. The results of Validity and Reliability Test gave a valid outcome with rcal >0,3 and reliability value of α >0,6 in all variables. Fishbein’s Attitude Model test in all variables showed an answer from neutral to very positive data. Linear Regression Test resulted in an
equation Y = -6,234+ 0,211 X1 + 0,213X2 + 0,550 X3 + 0,119 X4 + 1,252X5 + 0,665X6. The
value of determination coefficient (R2) was 0,816 which meant that variable variance of
Information and Communication Technology acceptance could be explained by data
variance of extension officers’ attitude and readiness (farmer readiness, extension officers readiness, infrastructure, management support, culture support) in values of 81,6%. In F test, Fcal was = 44,683 and was significant in p < 0,05, which meant that the effects of extension officers’ attitude and readiness to Information and Communication Technology acceptance.
6 pages, Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centered around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.
